Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "#
\n", 17 | " \n", 18 | "**Object-oriented programming**\n", 19 | " \n", 20 | "- Write a class that represents a \"real-world\" thing or situation\n", 21 | " - a set of instructions for making an instance (see below)\n", 22 | "\n", 23 | "\n", 24 | "- Call that class to create inidividual objects\n", 25 | "\n", 26 | "- According to PEP8:\n", 27 | " - **classes** are **capitalized** and uses **CamalCase**\n", 28 | " - **methods** are lowercase and uses **pot_hole**\n", 29 | "\n", 30 | "#### Sources\n", 31 | "1. Python Software Foundation, \"Classes\" Lasted Accessed on 21.02.2024 (https://docs.python.org/3/tutorial/classes.html#classes)\n", 32 | "2. \"What does __init__ and self do in python?\" Reddit's \n", 33 | "r/learnpython, Lasted Accessed on 21.02.2024 (https://www.reddit.com/r/learnpython/comments/19aghsb/comment/kimgw46/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)\n", 34 | "3. GeeksforGeeks \"Access Modifiers in Python: Public, Private and Protected\" Lasted Accessed on 21.02.2024 (https://www.geeksforgeeks.org/access-modifiers-in-python-public-private-and-protected)\n", 35 | "\n", 36 | "
" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "## Do we need `Classes`?\n", 44 | "\n", 45 | "Strickly speaking, we don't.\n", 46 | "\n", 47 | "However, they do have advantages -- just like user-defined functions -- which includes improving a code's:\n", 48 | "\n", 49 | "- readability\n", 50 | "\n", 51 | "\n", 52 | "- organization (i.e., **contextual idea/concepts grouping**) - at a higher level than user-defined functions\n", 53 | " - Put related idea/concepts together under one header\n", 54 | "\n", 55 | "\n", 56 | "- usability\n", 57 | "\n", 58 | "\n", 59 | "- encapsulation: restricting variable access and methods to help prevent accidental modification\n", 60 | " - enables a separation between what the \"outside world\" knows about an object versus what the object knows about itself [2]\n", 61 | " - protected and private variable (indicated using a single leading underscore (i.e., `_`) in the naming):\n", 62 | " - \" ...there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. `_spam`) should be treated as a non-public part of the API (whether it is a function, a method or a data member).\" [1, 3]\n", 63 | " - public: `some_name`\n", 64 | " - protected: `_some_name`\n", 65 | " - private: `__some_name`\n", 66 | " \n", 67 | " - also is used to \"hide\" data\n", 68 | "\n", 69 | "\n", 70 | "- inheritance" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": null, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [ 79 | "class Dog():\n", 80 | " \"\"\" A dog model.\n", 81 | " \"\"\"\n", 82 | " \n", 83 | " def __init__(self, name, age):\n", 84 | " 'Initialize name and age attributes'\n", 85 | " self.name = name ## public variable\n", 86 | " self.age = age\n", 87 | " self.breed = 'mutt' ## a default value\n", 88 | "\n", 89 | " def sit(self):\n", 90 | " 'Simulates sitting due to a command given.'\n", 91 | " print(f'{self.name} is now sitting.')\n", 92 | "\n", 93 | "\n", 94 | " def roll_over(self):\n", 95 | " 'Simulates rolling over due to a command given.'\n", 96 | " print(f'{self.name} is now rolling over.')" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "#### The first \"method\"\n", 104 | "A method is a function that is part of a class.\n", 105 | "\n", 106 | "`def __init__(self, name, age)`:\n", 107 | "\n", 108 | "- A special Python method that will **automatically run** when we create a new instance (i.e., call the class)\n", 109 | " - the two leading and trailing underscores (`__`) indicate special methods (a.k.a. \"magic methods\" or \"dunder methods\")\n", 110 | " - more about underscores (`_`): https://www.datacamp.com/community/tutorials/role-underscore-python\n", 111 | "\n", 112 | "- `__init__` **initialise** the created class **instance** (i.e., setting the attributes that it expects it to have)\n", 113 | "\n", 114 | "- `self` refers to the **current instance**\n", 115 | " - the name **self** is agreed upon as standard practice (however, you could use something different, but this is not recommended)\n", 116 | "\n", 117 | "\n", 118 | "- Three parameters are required\n", 119 | " - **self** (**required** and must come **first**)\n", 120 | " - **name** and **dog** (i.e., attributes)\n", 121 | " \n", 122 | "\n", 123 | "- Two more methods\n", 124 | " - sit and roll_over\n", 125 | " - in a real coding situation, these methods would contain code that make a robot dog sit or roll over" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "#### Making a new instance from a class\n", 133 | "- Convention is to have **instances** as **lower case**" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": null, 139 | "metadata": {}, 140 | "outputs": [], 141 | "source": [ 142 | "pet_1 = Dog('Emma', 6)" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | "What happened here:\n", 150 | "1. A new instance of a `Dog` object is created\n", 151 | "2. `__init__` is called and makes the assignments to the `name` and `age` variables\n", 152 | "3. The new `Dog` object is assigned to the variable `pet_1`\n", 153 | "\n", 154 | "\n", 155 | "\n", 156 | "##### Accessing the newly made instance\n", 157 | "To access an instance's (i.e., `pet_1`) attribute (e.g., `age`) of the class (i.e., `Dog`):" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "print(f'My dog, {pet_1.name}, is a {pet_1.breed} and she is {pet_1.age} years old.')" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [ 175 | "pet_1.sit()" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": null, 181 | "metadata": {}, 182 | "outputs": [], 183 | "source": [ 184 | "pet_1.roll_over()" 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": {}, 190 | "source": [ 191 | "
\n", 192 | "\n", 193 | "#### Modify an attribute value directly" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "metadata": {}, 200 | "outputs": [], 201 | "source": [ 202 | "pet_1.breed = 'terrier'\n", 203 | "\n", 204 | "print(f'My dog, {pet_1.name}, is a {pet_1.breed} and she is {pet_1.age} years old.')" 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": {}, 210 | "source": [ 211 | "#### Modify an attribute value through a new method" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": null, 217 | "metadata": {}, 218 | "outputs": [], 219 | "source": [ 220 | "pet_2 = Dog('Emma', 6)\n", 221 | "\n", 222 | "print(f'My dog, {pet_2.name}, is a {pet_2.breed} and she is {pet_2.age} years old.')" 223 | ] 224 | }, 225 | { 226 | "cell_type": "markdown", 227 | "metadata": {}, 228 | "source": [ 229 | "
\n", 230 | "\n", 231 | "Alternatively, we can create another method that does the updates an attribute:" 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": null, 237 | "metadata": {}, 238 | "outputs": [], 239 | "source": [ 240 | "class Dog():\n", 241 | " \"\"\"A Dog model\"\"\"\n", 242 | " \n", 243 | " def __init__(self, name, age):\n", 244 | " \"\"\"Initialize name and age attributes\"\"\"\n", 245 | " self.name = name\n", 246 | " self.age = age\n", 247 | " self.breed = 'mutt'\n", 248 | "\n", 249 | " def sit(self):\n", 250 | " \"\"\"Simulate a dog sitting due to a command given.\"\"\"\n", 251 | " print(\"{0} is now sitting.\".format(self.name))\n", 252 | "\n", 253 | "\n", 254 | " def roll_over(self):\n", 255 | " \"\"\"Simulate a dog rolling over due to a command given.\"\"\"\n", 256 | " print(\"{0} is now rolling over.\".format(self.name))\n", 257 | "\n", 258 | "\n", 259 | " def update_breed(self, new_breed):\n", 260 | " \"\"\"Set breed to new value.\"\"\"\n", 261 | " self.breed = new_breed" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": null, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [ 270 | "pet_3 = Dog('Emma', 6)" 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": null, 276 | "metadata": {}, 277 | "outputs": [], 278 | "source": [ 279 | "pet_3.update_breed('chihuahua')\n", 280 | "\n", 281 | "print(f'My dog, {pet_3.name}, is a {pet_3.breed} and she is {pet_3.age} years old.')" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": null, 287 | "metadata": {}, 288 | "outputs": [], 289 | "source": [ 290 | "pet_3.update_breed('terrier')\n", 291 | "\n", 292 | "print(f'My dog, {pet_3.name}, is a {pet_3.breed} and she is {pet_3.age} years old.')" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "
\n", 300 | "\n", 301 | "### A word of caution\n", 302 | "- Bypass internal checks written into methods" 303 | ] 304 | }, 305 | { 306 | "cell_type": "code", 307 | "execution_count": null, 308 | "metadata": {}, 309 | "outputs": [], 310 | "source": [ 311 | "class Dog():\n", 312 | " \"\"\"A Dog model\"\"\"\n", 313 | " \n", 314 | " def __init__(self, name, age):\n", 315 | " \"\"\"Initialize name and age attributes\"\"\"\n", 316 | " self.name = name\n", 317 | " self.age = age\n", 318 | " self.breed = 'mutt'\n", 319 | "\n", 320 | " def sit(self):\n", 321 | " \"\"\"Simulate a dog sitting due to a command given.\"\"\"\n", 322 | " print(\"{0} is now sitting.\".format(self.name))\n", 323 | "\n", 324 | "\n", 325 | " def roll_over(self):\n", 326 | " \"\"\"Simulate a dog rolling over due to a command given.\"\"\"\n", 327 | " print(\"{0} is now rolling over.\".format(self.name))\n", 328 | "\n", 329 | "\n", 330 | " def update_breed(self, new_breed):\n", 331 | " \"\"\"Set breed to new value.\"\"\"\n", 332 | " if new_breed == 'pug':\n", 333 | " print(\"Pugs are not allowed.\")\n", 334 | " else:\n", 335 | " self.breed = new_breed" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": null, 341 | "metadata": {}, 342 | "outputs": [], 343 | "source": [ 344 | "pet_4 = Dog('Emma', 6)\n", 345 | "\n", 346 | "pet_4.update_breed('pug')" 347 | ] 348 | }, 349 | { 350 | "cell_type": "markdown", 351 | "metadata": {}, 352 | "source": [ 353 | "That is all okay.\n", 354 | "\n", 355 | "But the `update_breed` can be bypassed by setting the attribute directly." 356 | ] 357 | }, 358 | { 359 | "cell_type": "code", 360 | "execution_count": null, 361 | "metadata": {}, 362 | "outputs": [], 363 | "source": [ 364 | "pet_4.breed = 'pug'\n", 365 | "\n", 366 | "print(f'My dog, {pet_4.name}, is a {pet_4.breed} and she is {pet_4.age} years old.')" 367 | ] 368 | }, 369 | { 370 | "cell_type": "markdown", 371 | "metadata": {}, 372 | "source": [ 373 | "
\n", 374 | "\n", 375 | "## Encapsulation\n", 376 | "\n", 377 | "Create a variable that is private the represents an animal's species:" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": null, 383 | "metadata": {}, 384 | "outputs": [], 385 | "source": [ 386 | "class Dog():\n", 387 | " \"\"\" A dog model.\n", 388 | " \"\"\"\n", 389 | " \n", 390 | " def __init__(self, name, age):\n", 391 | " 'Initialize name and age attributes'\n", 392 | " self.name = name\n", 393 | " self.age = age\n", 394 | " self.breed = 'mutt'\n", 395 | " \n", 396 | " self._species = 'mammal'\n", 397 | "\n", 398 | " def sit(self):\n", 399 | " 'Simulates sitting due to a command given.'\n", 400 | " print(f'{self.name} is now sitting.')\n", 401 | "\n", 402 | "\n", 403 | " def roll_over(self):\n", 404 | " 'Simulates rolling over due to a command given.'\n", 405 | " print(f'{self.name} is now rolling over.')" 406 | ] 407 | }, 408 | { 409 | "cell_type": "code", 410 | "execution_count": null, 411 | "metadata": {}, 412 | "outputs": [], 413 | "source": [ 414 | "pet_5 = Dog('Emma', 6)\n", 415 | "\n", 416 | "print(f'My dog (a {pet_5._species}), {pet_5.name}, is a {pet_5.breed} and she is {pet_5.age} years old.')" 417 | ] 418 | }, 419 | { 420 | "cell_type": "markdown", 421 | "metadata": {}, 422 | "source": [ 423 | "Since `_species` is a private variable, one still runs the risk of being a able to modify it:" 424 | ] 425 | }, 426 | { 427 | "cell_type": "code", 428 | "execution_count": null, 429 | "metadata": {}, 430 | "outputs": [], 431 | "source": [ 432 | "pet_5._species = 'bird'\n", 433 | "\n", 434 | "print(f'My dog (a {pet_5._species}), {pet_5.name}, is a {pet_5.breed} and she is {pet_5.age} years old.')" 435 | ] 436 | }, 437 | { 438 | "cell_type": "markdown", 439 | "metadata": {}, 440 | "source": [ 441 | "### An alternative example" 442 | ] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": null, 447 | "metadata": {}, 448 | "outputs": [], 449 | "source": [ 450 | "class BankAccount:\n", 451 | " ''' Example to show how public, protected and private variables\n", 452 | " and modules work (i.e., name, _name, and __name).\n", 453 | " \n", 454 | " Original source:\n", 455 | " https://www.tutorialspoint.com/access-modifiers-in-python-public-private-and-protected\n", 456 | " '''\n", 457 | " def __init__(self, account_number, nationality):\n", 458 | " self.__account_number = account_number\n", 459 | " self.nationality = nationality\n", 460 | "\n", 461 | "\n", 462 | " def display_nationality(self):\n", 463 | " ''' A public module (no underscore in name)\n", 464 | " '''\n", 465 | " print(\"Nationality:\", self.nationality)\n", 466 | "\n", 467 | "\n", 468 | " def _display_nationality(self):\n", 469 | " ''' A protected module (1 underscore in name)\n", 470 | " '''\n", 471 | " print(\"Nationality:\", self.nationality)\n", 472 | "\n", 473 | "\n", 474 | " def __display_nationality(self):\n", 475 | " ''' A private module (2 underscore in name)\n", 476 | " '''\n", 477 | " print(\"Nationality:\", self.nationality)" 478 | ] 479 | }, 480 | { 481 | "cell_type": "code", 482 | "execution_count": null, 483 | "metadata": {}, 484 | "outputs": [], 485 | "source": [ 486 | "person_1 = BankAccount(1234567890, 'DE')\n", 487 | "\n", 488 | "person_1.display_nationality()" 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": null, 494 | "metadata": {}, 495 | "outputs": [], 496 | "source": [ 497 | "person_1._display_nationality()" 498 | ] 499 | }, 500 | { 501 | "cell_type": "code", 502 | "execution_count": null, 503 | "metadata": {}, 504 | "outputs": [], 505 | "source": [ 506 | "person_1.__display_nationality()" 507 | ] 508 | }, 509 | { 510 | "cell_type": "markdown", 511 | "metadata": {}, 512 | "source": [ 513 | "
\n", 514 | "\n", 515 | "## Example 2: UFO" 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": null, 521 | "metadata": {}, 522 | "outputs": [], 523 | "source": [ 524 | "class Ufo():\n", 525 | " \"\"\"A UFO model\"\"\"\n", 526 | " \n", 527 | " def __init__(self, origin, attitude):\n", 528 | " \"\"\"Initialize the UFO's physical origin and if they are friendly\"\"\"\n", 529 | " self.origin = origin\n", 530 | " self.friendly = attitude\n", 531 | "\n", 532 | "\n", 533 | " def species(self):\n", 534 | " \"\"\"The alien's species that controls the UFO\"\"\"\n", 535 | "\n", 536 | "\n", 537 | " def craft_shape(self):\n", 538 | " \"\"\"The UFO's shape\"\"\"\n", 539 | " #print(self.origin.)\n" 540 | ] 541 | }, 542 | { 543 | "cell_type": "code", 544 | "execution_count": null, 545 | "metadata": {}, 546 | "outputs": [], 547 | "source": [ 548 | "first_ufo = Ufo('mars', 'friendly')\n", 549 | "first_ufo.origin" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": null, 555 | "metadata": {}, 556 | "outputs": [], 557 | "source": [ 558 | "second_ufo = Ufo('venus', 'unfriendly')\n", 559 | "second_ufo.origin" 560 | ] 561 | }, 562 | { 563 | "cell_type": "code", 564 | "execution_count": null, 565 | "metadata": {}, 566 | "outputs": [], 567 | "source": [ 568 | "print('The first UFO is from {0} and they are {1}.'.format(first_ufo.origin, first_ufo.friendly))\n", 569 | "print('The second UFO is from {0} and they are {1}.'.format(second_ufo.origin, second_ufo.friendly))" 570 | ] 571 | }, 572 | { 573 | "cell_type": "code", 574 | "execution_count": null, 575 | "metadata": {}, 576 | "outputs": [], 577 | "source": [] 578 | } 579 | ], 580 | "metadata": { 581 | "kernelspec": { 582 | "display_name": "Python 3 (ipykernel)", 583 | "language": "python", 584 | "name": "python3" 585 | }, 586 | "language_info": { 587 | "codemirror_mode": { 588 | "name": "ipython", 589 | "version": 3 590 | }, 591 | "file_extension": ".py", 592 | "mimetype": "text/x-python", 593 | "name": "python", 594 | "nbconvert_exporter": "python", 595 | "pygments_lexer": "ipython3", 596 | "version": "3.10.13" 597 | } 598 | }, 599 | "nbformat": 4, 600 | "nbformat_minor": 2 601 | } 602 | -------------------------------------------------------------------------------- /colaboratory_introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "colab_type": "text", 7 | "id": "5fCEDCU_qrC0" 8 | }, 9 | "source": [ 10 | "
![\"Colaboratory](\"/img/colab_favicon.ico\")
Welcome to Colaboratory!
\n", 13 | "\n", 14 | "\n", 15 | "Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.\n", 16 | "\n", 17 | "With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser." 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": null, 23 | "metadata": { 24 | "colab": { 25 | "height": 420 26 | }, 27 | "colab_type": "code", 28 | "id": "xitplqMNk_Hc", 29 | "outputId": "ed4f60d2-878d-4056-c438-352dac39a112" 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "#@title Introducing Colaboratory { display-mode: \"form\" }\n", 34 | "#@markdown This 3-minute video gives an overview of the key features of Colaboratory:\n", 35 | "from IPython.display import YouTubeVideo\n", 36 | "YouTubeVideo('inN8seMm7UI', width=600, height=400)" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": { 42 | "colab_type": "text", 43 | "id": "GJBs_flRovLc" 44 | }, 45 | "source": [ 46 | "## Getting Started\n", 47 | "\n", 48 | "The document you are reading is a [Jupyter notebook](https://jupyter.org/), hosted in Colaboratory. It is not a static page, but an interactive environment that lets you write and execute code in Python and other languages.\n", 49 | "\n", 50 | "For example, here is a **code cell** with a short Python script that computes a value, stores it in a variable, and prints the result:" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": null, 56 | "metadata": { 57 | "colab": { 58 | "height": 35 59 | }, 60 | "colab_type": "code", 61 | "id": "gJr_9dXGpJ05", 62 | "outputId": "5626194c-e802-4293-942d-2908885c3c1f" 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "seconds_in_a_day = 24 * 60 * 60\n", 67 | "seconds_in_a_day" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": { 73 | "colab_type": "text", 74 | "id": "2fhs6GZ4qFMx" 75 | }, 76 | "source": [ 77 | "To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut \"Command/Ctrl+Enter\".\n", 78 | "\n", 79 | "All cells modify the same global state, so variables that you define by executing a cell can be used in other cells:" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": { 86 | "colab": { 87 | "height": 35 88 | }, 89 | "colab_type": "code", 90 | "id": "-gE-Ez1qtyIA", 91 | "outputId": "8d2e4259-4682-4e19-b683-7b9087f28820" 92 | }, 93 | "outputs": [], 94 | "source": [ 95 | "seconds_in_a_week = 7 * seconds_in_a_day\n", 96 | "seconds_in_a_week" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": { 102 | "colab_type": "text", 103 | "id": "lSrWNr3MuFUS" 104 | }, 105 | "source": [ 106 | "For more information about working with Colaboratory notebooks, see [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb).\n" 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": { 112 | "colab_type": "text", 113 | "id": "JyG45Qk3qQLS" 114 | }, 115 | "source": [ 116 | "---\n", 117 | "# Cells\n", 118 | "A notebook is a list of cells. Cells contain either explanatory text or executable code and its output. Click a cell to select it." 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": { 124 | "colab_type": "text", 125 | "id": "KR921S_OQSHG" 126 | }, 127 | "source": [ 128 | "## Code cells\n", 129 | "Below is a **code cell**. Once the toolbar button indicates CONNECTED, click in the cell to select it and execute the contents in the following ways:\n", 130 | "\n", 131 | "* Click the **Play icon** in the left gutter of the cell;\n", 132 | "* Type **Cmd/Ctrl+Enter** to run the cell in place;\n", 133 | "* Type **Shift+Enter** to run the cell and move focus to the next cell (adding one if none exists); or\n", 134 | "* Type **Alt+Enter** to run the cell and insert a new code cell immediately below it.\n", 135 | "\n", 136 | "There are additional options for running some or all cells in the **Runtime** menu.\n" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": null, 142 | "metadata": { 143 | "cellView": "both", 144 | "colab": { 145 | "base_uri": "https://localhost:8080/", 146 | "height": 34 147 | }, 148 | "colab_type": "code", 149 | "executionInfo": { 150 | "elapsed": 809, 151 | "status": "ok", 152 | "timestamp": 1585470408839, 153 | "user": { 154 | "displayName": "", 155 | "photoUrl": "", 156 | "userId": "" 157 | }, 158 | "user_tz": -120 159 | }, 160 | "id": "WUtu4316QSHL", 161 | "outputId": "3446364b-328c-4383-a2e7-0b4a2e4fb6d5" 162 | }, 163 | "outputs": [], 164 | "source": [ 165 | "a = 13\n", 166 | "a" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": { 172 | "colab_type": "text", 173 | "id": "Id6tDF1HQSHD" 174 | }, 175 | "source": [ 176 | "## Text cells\n", 177 | "This is a **text cell**. You can **double-click** to edit this cell. Text cells\n", 178 | "use markdown syntax. To learn more, see our [markdown\n", 179 | "guide](/notebooks/markdown_guide.ipynb).\n", 180 | "\n", 181 | "You can also add math to text cells using [LaTeX](http://www.latex-project.org/)\n", 182 | "to be rendered by [MathJax](https://www.mathjax.org). Just place the statement\n", 183 | "within a pair of **\\$** signs. For example `$\\sqrt{3x-1}+(1+x)^2$` becomes\n", 184 | "$\\sqrt{3x-1}+(1+x)^2.$\n" 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": { 190 | "colab_type": "text", 191 | "id": "7bqjkGKwQSHW" 192 | }, 193 | "source": [ 194 | "## Adding and moving cells\n", 195 | "You can add new cells by using the **+ CODE** and **+ TEXT** buttons that show when you hover between cells. These buttons are also in the toolbar above the notebook where they can be used to add a cell below the currently selected cell.\n", 196 | "\n", 197 | "You can move a cell by selecting it and clicking **Cell Up** or **Cell Down** in the top toolbar. \n", 198 | "\n", 199 | "Consecutive cells can be selected by \"lasso selection\" by dragging from outside one cell and through the group. Non-adjacent cells can be selected concurrently by clicking one and then holding down Ctrl while clicking another. Similarly, using Shift instead of Ctrl will select all intermediate cells." 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": { 205 | "colab_type": "text", 206 | "id": "aro-UJgUQSH1" 207 | }, 208 | "source": [ 209 | "# Integration with Drive\n", 210 | "\n", 211 | "Colaboratory is integrated with Google Drive. It allows you to share, comment, and collaborate on the same document with multiple people:\n", 212 | "\n", 213 | "* The **SHARE** button (top-right of the toolbar) allows you to share the notebook and control permissions set on it.\n", 214 | "\n", 215 | "* **File->Make a Copy** creates a copy of the notebook in Drive.\n", 216 | "\n", 217 | "* **File->Save** saves the File to Drive. **File->Save and checkpoint** pins the version so it doesn't get deleted from the revision history. \n", 218 | "\n", 219 | "* **File->Revision history** shows the notebook's revision history. " 220 | ] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "metadata": { 225 | "colab_type": "text", 226 | "id": "4hfV37gxpP_c" 227 | }, 228 | "source": [ 229 | "## Commenting on a cell\n", 230 | "You can comment on a Colaboratory notebook like you would on a Google Document. Comments are attached to cells, and are displayed next to the cell they refer to. If you have **comment-only** permissions, you will see a comment button on the top right of the cell when you hover over it.\n", 231 | "\n", 232 | "If you have edit or comment permissions you can comment on a cell in one of three ways: \n", 233 | "\n", 234 | "1. Select a cell and click the comment button in the toolbar above the top-right corner of the cell.\n", 235 | "1. Right click a text cell and select **Add a comment** from the context menu.\n", 236 | "3. Use the shortcut **Ctrl+Shift+M** to add a comment to the currently selected cell. \n", 237 | "\n", 238 | "You can resolve and reply to comments, and you can target comments to specific collaborators by typing *+[email address]* (e.g., `+user@domain.com`). Addressed collaborators will be emailed. \n", 239 | "\n", 240 | "The Comment button in the top-right corner of the page shows all comments attached to the notebook." 241 | ] 242 | }, 243 | { 244 | "cell_type": "markdown", 245 | "metadata": { 246 | "colab_type": "text", 247 | "id": "-Rh3-Vt9Nev9" 248 | }, 249 | "source": [ 250 | "## More Resources\n", 251 | "- [Guide to Markdown](/notebooks/markdown_guide.ipynb)\n", 252 | "- Colaboratory is built on top of [Jupyter Notebook](https://jupyter.org/)." 253 | ] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "metadata": { 258 | "colab_type": "text", 259 | "id": "8HFGaSr3eYKE" 260 | }, 261 | "source": [ 262 | "---\n", 263 | "**Original Sources:**\n", 264 | "1. https://colab.research.google.com/notebooks/welcome.ipynb\n", 265 | "2. https://colab.research.google.com/notebooks/basic_features_overview.ipynb" 266 | ] 267 | } 268 | ], 269 | "metadata": { 270 | "colab": { 271 | "collapsed_sections": [], 272 | "name": "colaboratory_introduction", 273 | "provenance": [ 274 | { 275 | "file_id": "/v2/external/notebooks/welcome.ipynb", 276 | "timestamp": 1585471612038 277 | } 278 | ], 279 | "toc_visible": true 280 | }, 281 | "kernelspec": { 282 | "display_name": "Python 3", 283 | "language": "python", 284 | "name": "python3" 285 | }, 286 | "language_info": { 287 | "codemirror_mode": { 288 | "name": "ipython", 289 | "version": 3 290 | }, 291 | "file_extension": ".py", 292 | "mimetype": "text/x-python", 293 | "name": "python", 294 | "nbconvert_exporter": "python", 295 | "pygments_lexer": "ipython3", 296 | "version": "3.8.1" 297 | } 298 | }, 299 | "nbformat": 4, 300 | "nbformat_minor": 1 301 | } 302 | -------------------------------------------------------------------------------- /containers.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "**Lecture is based on:**\n", 8 | "\n", 9 | "-[learning-python3.ipynb] https://gist.github.com/kenjyco/69eeb503125035f21a9d \n", 10 | "-[Introduction to Python] http://introtopython.org/lists_tuples.html#Tuples\n", 11 | "***" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "## Containers (i.e. collections)\n", 19 | "\n", 20 | "Containers are objects that group other objects (i.e. they collect things).\n", 21 | "\n", 22 | "- **str** - string: \n", 23 | " - immutable\n", 24 | " - indexed by integers\n", 25 | " - items are stored in the order they were added\n", 26 | " - can use +, *, +=, and *= operators\n", 27 | "\n", 28 | "\n", 29 | "- **list** - list:\n", 30 | " - mutable\n", 31 | " - indexed by integers\n", 32 | " - items are stored in the order they were added\n", 33 | " - can use +, *, +=, and *= operators\n", 34 | " > ['Star Wars', 1, \"Empire\", 2, 'Jedi', '3', True, 1, 1]\n", 35 | "\n", 36 | "- **tuple** - tuple:\n", 37 | " - immutable\n", 38 | " - indexed by integers\n", 39 | " - items are stored in the order they were added\n", 40 | " - can use +, *, +=, and *= operators\n", 41 | " - faster than mutable lists\n", 42 | " > ('Star Wars', 1, \"Empire\", 2, 'Jedi', '3', True, 1, 1)\n", 43 | "\n", 44 | "- **dict** - dictionary:\n", 45 | " - mutable\n", 46 | " - key-value pairs are indexed by immutable keys\n", 47 | " - items are NOT stored in the order they were added\n", 48 | " - use dictionaries as an alternative to switch functions\n", 49 | " > {'year': 1992, 'western': 'Unforgiven', 'scifi': ['Aliens3', 'Seedpeople']}\n", 50 | "\n", 51 | "- **set** - set:\n", 52 | " - mutable\n", 53 | " - not indexed at all\n", 54 | " - items are NOT stored in the order they were added\n", 55 | " - can only contain immutable objects\n", 56 | " - does NOT contain duplicate objects\n", 57 | " > {'Star Wars', 1, \"Empire\", 2, 'Jedi', '3', True}\n", 58 | "\n", 59 | "**Note**:\n", 60 | "- When defining lists, tuples, or sets, use commas (,) to separate the individual items\n", 61 | "- When defining dicts, use a colon (:) to separate keys from values and commas (,) to separate the key-value pairs.\n", 62 | "***" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "### Strings\n", 70 | "\n", 71 | "- immutable\n", 72 | "- indexed by integers\n", 73 | "- items are stored in the order they were added\n", 74 | "- can use +, *, +=, and *= operators" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": null, 80 | "metadata": {}, 81 | "outputs": [], 82 | "source": [ 83 | "my_string = 'Mom'\n", 84 | "\n", 85 | "print(my_string[0])\n", 86 | "print(my_string[1])\n", 87 | "print(my_string[-1])\n", 88 | "len(my_string)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "## an example for immutable and its error\n", 98 | "my_string[1] = 'u'" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "***\n", 106 | "### Lists\n", 107 | "- mutable\n", 108 | "- indexed by integers\n", 109 | "- items are stored in the order they were added\n", 110 | "- can use +, *, +=, and *= operators" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": null, 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "## Create a list\n", 120 | "movies = ['Star Wars', 'X-men', 'Forest Gump']\n", 121 | "\n", 122 | "movies = ['Star Wars',\n", 123 | " 'X-men',\n", 124 | " 'Forest Gump']\n", 125 | "\n", 126 | "print(movies)\n", 127 | "print()\n", 128 | "print('First item: {0}'.format(movies[1]))\n", 129 | "print('Last item: {0}'.format(movies[-1]))\n", 130 | "print('Number of movies: {0}'.format(len(movies)))" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": null, 136 | "metadata": {}, 137 | "outputs": [], 138 | "source": [ 139 | "## mutable\n", 140 | "movies.append('Blade Runner') # add to the end\n", 141 | "print(movies)\n", 142 | "\n", 143 | "movies.pop() # remove last item and print\n", 144 | "print(movies)\n", 145 | "\n", 146 | "movies.remove('X-men') # remove specific item\n", 147 | "print(movies)\n", 148 | "\n", 149 | "movies.insert(1, 'Gattaca') # add to position 1\n", 150 | "print(movies)\n", 151 | "\n", 152 | "movies.extend(['Solo', '2001']) # add multiple items to end\n", 153 | "print(movies)\n", 154 | "\n", 155 | "movies[3] = 'Blade Runner' # replace position 3 (i.e. the 4th movie)\n", 156 | "print(movies)" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": null, 162 | "metadata": {}, 163 | "outputs": [], 164 | "source": [ 165 | "## sorting\n", 166 | "x = sorted(['dogs', 'cats', 'dragons', 'Chicago',\n", 167 | " 'Bonn', 'ants', 'mice'])\n", 168 | "print(x)\n", 169 | "\n", 170 | "y = sorted([0, 10, 1, 3.6, 7, 5, 2, -3])\n", 171 | "print(y)" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": null, 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [ 180 | "## max, min, sum and abs\n", 181 | "print( min([0, 10, 1, 3.6, 7, 5, 2, -3]) )\n", 182 | "print( max([0, 10, 1, 3.6, 7, 5, 2, -3]) )\n", 183 | "print( sum([0, 10, 1, 3.6, 7, 5, 2, -3]) )" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "***\n", 191 | "### Dictionaries\n", 192 | "Dictionaries allow you to connect pieces of knowledge together using a **key: value** pairing" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": null, 198 | "metadata": {}, 199 | "outputs": [], 200 | "source": [ 201 | "movies = {} # creates an empty dictionary that can later be added to\n", 202 | "\n", 203 | "# create a filled list\n", 204 | "movies = {'scifi': 'Star Wars',\n", 205 | " 'romance': 'Titanic',\n", 206 | " 'western': 'Unforgiven'}\n", 207 | "\n", 208 | "print(movies)\n", 209 | "print()\n", 210 | "print(movies['western'])\n", 211 | "print()\n", 212 | "print(movies['western'], movies['romance'])" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [ 221 | "## Add a new key:value pair\n", 222 | "movies['horror'] = '28 Days Later'\n", 223 | "print(movies)\n", 224 | "print()\n", 225 | "\n", 226 | "movies.update( {'comedies' : 'The Big Lebowski'} )\n", 227 | "print(movies)\n", 228 | "print()" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": null, 234 | "metadata": {}, 235 | "outputs": [], 236 | "source": [ 237 | "## Modify a value\n", 238 | "movies['horror'] = 'The Evil Dead'\n", 239 | "print(movies)\n", 240 | "print()\n", 241 | "\n", 242 | "movies.update(romance = 'love actually')\n", 243 | "print(movies)\n", 244 | "print()" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": null, 250 | "metadata": {}, 251 | "outputs": [], 252 | "source": [ 253 | "## Delete a key:value pair\n", 254 | "del movies['romance'] ## or\n", 255 | "\n", 256 | "#movies.pop('romance')\n", 257 | "\n", 258 | "print(movies)" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": null, 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "## Modify a key\n", 268 | "\n", 269 | "## Copy and the delete\n", 270 | "movies['sci_fi'] = movies['scifi']\n", 271 | "del movies['scifi']\n", 272 | "print(movies)" 273 | ] 274 | }, 275 | { 276 | "cell_type": "code", 277 | "execution_count": null, 278 | "metadata": {}, 279 | "outputs": [], 280 | "source": [ 281 | "## Add two dictionaries together\n", 282 | "movies_his = {'scifi': 'Star Wars', 'western': 'Unforgiven'}\n", 283 | "movies_her = {'horror': 'The Evil Dead', 'comedies': 'The Big Lebowski'}\n", 284 | "\n", 285 | "movies_his.update(movies_her)\n", 286 | "print(movies_his)" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": null, 292 | "metadata": {}, 293 | "outputs": [], 294 | "source": [ 295 | "## Values as lists\n", 296 | "movies = {'scifi': ['Star Wars'], 'romance': ['Titanic'],\n", 297 | " 'western': ['Unforgiven']}\n", 298 | "\n", 299 | "# Add a value to a key\n", 300 | "movies['scifi'].append('Blade Runner')\n", 301 | "print(movies)\n", 302 | "\n", 303 | "# Remove last value of a key\n", 304 | "movies['scifi'].pop(-1)\n", 305 | "print(movies)" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": null, 311 | "metadata": {}, 312 | "outputs": [], 313 | "source": [ 314 | "## type functions\n", 315 | "type(movies)" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "***\n", 323 | "### Dictionaries replace switch functions\n", 324 | "\n", 325 | "Python does not have a switch function like the following bash example:" 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": null, 331 | "metadata": {}, 332 | "outputs": [], 333 | "source": [ 334 | "%%bash\n", 335 | "\n", 336 | "printf 'Which do you want?\\n'\n", 337 | "ARGUMENT=1\n", 338 | "\n", 339 | "case $ARGUMENT in\n", 340 | " 0)\n", 341 | " echo \"zero\"\n", 342 | " ;;\n", 343 | " 1)\n", 344 | " echo \"one\"\n", 345 | " ;;\n", 346 | " 2)\n", 347 | " echo \"two\"\n", 348 | " ;; \n", 349 | " *)\n", 350 | " echo \"nothing\"\n", 351 | " ;;\n", 352 | "esac" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": null, 358 | "metadata": {}, 359 | "outputs": [], 360 | "source": [ 361 | "def numbers_to_strings(argument):\n", 362 | " #Define the dictionary:\n", 363 | " switcher = {\n", 364 | " 0: \"zero\",\n", 365 | " 1: \"one\",\n", 366 | " 2: \"two\",\n", 367 | " }\n", 368 | " return switcher.get(argument, \"nothing\")\n", 369 | "\n", 370 | "print('Which do you want?')\n", 371 | "\n", 372 | "numbers_to_strings(1)" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": {}, 378 | "source": [ 379 | "***\n", 380 | "Link to [Program Files Notebook](program_file.ipynb)" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": null, 386 | "metadata": {}, 387 | "outputs": [], 388 | "source": [] 389 | } 390 | ], 391 | "metadata": { 392 | "kernelspec": { 393 | "display_name": "Python 3", 394 | "language": "python", 395 | "name": "python3" 396 | }, 397 | "language_info": { 398 | "codemirror_mode": { 399 | "name": "ipython", 400 | "version": 3 401 | }, 402 | "file_extension": ".py", 403 | "mimetype": "text/x-python", 404 | "name": "python", 405 | "nbconvert_exporter": "python", 406 | "pygments_lexer": "ipython3", 407 | "version": "3.5.3" 408 | } 409 | }, 410 | "nbformat": 4, 411 | "nbformat_minor": 2 412 | } 413 | -------------------------------------------------------------------------------- /csv_library.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "***\n", 8 | "# Reading Data Files\n", 9 | "- Commma Separated Value (CSV) files - a generic text file for data\n", 10 | " - (https://en.wikipedia.org/wiki/Comma-separated_values)\n", 11 | "\n", 12 | "\n", 13 | "- Use the csv standard library - https://docs.python.org/3/library/csv.html\n", 14 | "\n", 15 | "---\n", 16 | "\n", 17 | "The data within a CSV files can be separated by things other than a 'comma':\n", 18 | "\n", 19 | "comma: 1.0 , 2.0 , 3.0\n", 20 | "\n", 21 | "semicolon: 1.0 ; 2.0 ; 3.0\n", 22 | "\n", 23 | "includes quotes (for groupings): 1 , 2 , \"a, b, c\"\n", 24 | "\n", 25 | "---\n", 26 | "\n", 27 | "We will use the following file for reading in some three-dimensional data.\n", 28 | "\n", 29 | "**data_3d.csv** contains the following information:\n", 30 | "- Hearder first line\n", 31 | "- 28 data rows\n", 32 | "- 3 data columns (Time, Exp and Theory)" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "import csv" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": null, 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "## CSV data file acan be found at\n", 51 | "## https://github.com/karlkirschner/2020_Scientific_Programming/blob/master/data_3d.csv\n", 52 | "\n", 53 | "## For Colabs\n", 54 | "\n", 55 | "## In order to upload data\n", 56 | "#from google.colab import files\n", 57 | "#uploaded = files.upload()\n", 58 | "\n", 59 | "## In order to show the plots\n", 60 | "#%matplotlib inline" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "Example of a true comma separated value file" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": null, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "## A Jupyter and Linux bash trick for looking into a file\n", 77 | "!head data_3d.csv --lines=10" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "Example of a semicolon separated value file." 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": null, 90 | "metadata": {}, 91 | "outputs": [], 92 | "source": [ 93 | "!head data_3d_semi.csv --lines=10" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "For the cells that follow - we will use the data_3d.csv file." 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": null, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "time = []\n", 110 | "exp = []\n", 111 | "sim = []\n", 112 | "\n", 113 | "with open('data_3d.csv') as file:\n", 114 | " read_it = csv.reader(file, delimiter=',')\n", 115 | "\n", 116 | " next(read_it) ## skips header (i.e. the first row)\n", 117 | " \n", 118 | " for row in read_it:\n", 119 | " time.append(float((row[0])))\n", 120 | " exp.append(float(row[1]))\n", 121 | " sim.append(float(row[2]))" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "Now we have created three list that contains floats, giving us our data that we can then do something with!\n", 129 | "\n", 130 | "---\n", 131 | "#### Slicing\n", 132 | "A quick note about getting a slice of data from a list.\n", 133 | "\n", 134 | "Let's say that **a** is a list, then we can get different ranges via:\n", 135 | "\n", 136 | "a (or a[:]) ## a copy of the whole array\n", 137 | "\n", 138 | "a[start:stop] ## items start to stop-1\n", 139 | "\n", 140 | "a[start:] ## items start to end of list\n", 141 | "\n", 142 | "a[:stop] ## items start of list to stop-1\n", 143 | "\n", 144 | "a[item] ## a single item" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": null, 150 | "metadata": {}, 151 | "outputs": [], 152 | "source": [ 153 | "print(time)" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": null, 159 | "metadata": {}, 160 | "outputs": [], 161 | "source": [ 162 | "print(time[:])" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "metadata": {}, 169 | "outputs": [], 170 | "source": [ 171 | "print(time[5:15])" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": null, 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [ 180 | "print(time[5:])" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": null, 186 | "metadata": {}, 187 | "outputs": [], 188 | "source": [ 189 | "print(time[:15])" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "print(time[1])" 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "---\n", 206 | "Okay, back to our original focus.\n", 207 | "\n", 208 | "Let's verify that our items are indeed numbers (we will come back to this in a bit)." 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": null, 214 | "metadata": {}, 215 | "outputs": [], 216 | "source": [ 217 | "type(time[0])" 218 | ] 219 | }, 220 | { 221 | "cell_type": "markdown", 222 | "metadata": {}, 223 | "source": [ 224 | "We can treat the items within the time list and floats." 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": {}, 231 | "outputs": [], 232 | "source": [ 233 | "time[1] - time[0]" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": {}, 239 | "source": [ 240 | "---\n", 241 | "### Okay, so let's demo how we could use this data.\n", 242 | "\n", 243 | "(Again, we take a peak into our future and do some plotting.)" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": null, 249 | "metadata": {}, 250 | "outputs": [], 251 | "source": [ 252 | "import matplotlib.pyplot as plt" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": null, 258 | "metadata": {}, 259 | "outputs": [], 260 | "source": [ 261 | "## For Colabs\n", 262 | "## In order to show the plots\n", 263 | "\n", 264 | "#%matplotlib inline" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": null, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [ 273 | "plt.style.use('seaborn-whitegrid')\n", 274 | "plt.figure(figsize=(15,5))\n", 275 | "\n", 276 | "plt.plot(time, exp, linewidth=5, linestyle='solid', label='Experimental')\n", 277 | "plt.plot(time, sim, linewidth=5, linestyle='dashed', label='Simulated')\n", 278 | "\n", 279 | "plt.xlabel('Time (seconds)')\n", 280 | "plt.ylabel('Y-Axis (Unit)')\n", 281 | "plt.title('Experimental and Simulated Results')\n", 282 | "plt.grid(True)\n", 283 | "\n", 284 | "plt.show()" 285 | ] 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "metadata": {}, 290 | "source": [ 291 | "Notice that we have gaps in the tick marks for the x-axis.\n", 292 | "\n", 293 | "How can we easily change this for plotting?\n", 294 | "- Read the x-axis data as: **strings** (versus a float as above) and plot again.\n", 295 | "\n", 296 | " ```time.append(str(row[0]))```" 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": null, 302 | "metadata": {}, 303 | "outputs": [], 304 | "source": [ 305 | "time=[]\n", 306 | "exp=[]\n", 307 | "sim=[]\n", 308 | "\n", 309 | "with open('data_3d.csv') as file:\n", 310 | " read_it = csv.reader(file, delimiter=',')\n", 311 | "\n", 312 | " next(read_it)\n", 313 | " \n", 314 | " for row in read_it:\n", 315 | " time.append(str(row[0]))\n", 316 | " exp.append(float(row[1]))\n", 317 | " sim.append(float(row[2]))" 318 | ] 319 | }, 320 | { 321 | "cell_type": "code", 322 | "execution_count": null, 323 | "metadata": {}, 324 | "outputs": [], 325 | "source": [ 326 | "type(time[0])" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": null, 332 | "metadata": {}, 333 | "outputs": [], 334 | "source": [ 335 | "plt.style.use('seaborn-whitegrid')\n", 336 | "plt.figure(figsize=(15,5))\n", 337 | "\n", 338 | "plt.plot(time, exp, linewidth=5, linestyle='solid', label='Experimental')\n", 339 | "plt.plot(time, sim, linewidth=5, linestyle='dashed', label='Simulated')\n", 340 | "\n", 341 | "plt.xlabel('Time (seconds)')\n", 342 | "plt.ylabel('Y-Axis (Unit)')\n", 343 | "plt.title('Experimental and Simulated Results')\n", 344 | "plt.grid(True)\n", 345 | "plt.show()" 346 | ] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "metadata": {}, 351 | "source": [ 352 | "**Notice**: the x-axis ticks have changed - every data point is now labeled.\n", 353 | "\n", 354 | "**Also notice**: we can no longer treat them a numbers" 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": null, 360 | "metadata": {}, 361 | "outputs": [], 362 | "source": [ 363 | "time[1] - time[0]" 364 | ] 365 | }, 366 | { 367 | "cell_type": "code", 368 | "execution_count": null, 369 | "metadata": {}, 370 | "outputs": [], 371 | "source": [ 372 | "float(time[1]) - float(time[0])" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": {}, 378 | "source": [] 379 | } 380 | ], 381 | "metadata": { 382 | "kernelspec": { 383 | "display_name": "Python 3", 384 | "language": "python", 385 | "name": "python3" 386 | }, 387 | "language_info": { 388 | "codemirror_mode": { 389 | "name": "ipython", 390 | "version": 3 391 | }, 392 | "file_extension": ".py", 393 | "mimetype": "text/x-python", 394 | "name": "python", 395 | "nbconvert_exporter": "python", 396 | "pygments_lexer": "ipython3", 397 | "version": "3.8.1" 398 | } 399 | }, 400 | "nbformat": 4, 401 | "nbformat_minor": 2 402 | } 403 | -------------------------------------------------------------------------------- /data_3d.csv: -------------------------------------------------------------------------------- 1 | Time,Exp,Theory 2 | 0.0,0.1185,0.2255 3 | 0.5,0.6524,0.3052 4 | 1.0,0.1291,0.0744 5 | 1.5,0.9445,0.7611 6 | 2.0,0.0272,0.1183 7 | 2.5,0.7598,0.0450 8 | 3.0,0.8159,0.2669 9 | 3.5,0.8003,0.1770 10 | 4.0,0.5716,0.2433 11 | 4.5,0.6651,0.2302 12 | 5.0,0.9983,0.0772 13 | 5.5,0.1004,0.0805 14 | 6.0,0.8433,0.6214 15 | 6.5,0.0067,0.1156 16 | 7.0,0.8238,0.1607 17 | 7.5,0.3952,0.0420 18 | 8.0,0.6452,0.1123 19 | 8.5,0.8480,0.3830 20 | 9.0,0.1986,0.5566 21 | 9.5,0.9114,0.6670 22 | 10.0,0.7693,0.5655 23 | 10.5,0.5009,0.4875 24 | 11.0,0.2110,0.0104 25 | 11.5,0.9227,0.4968 26 | 12.0,0.0461,0.2639 27 | 12.5,0.2177,0.2197 28 | 13.0,0.9554,0.9440 29 | 13.5,0.6130,0.2423 30 | -------------------------------------------------------------------------------- /data_3d_semi.csv: -------------------------------------------------------------------------------- 1 | Time;Exp;Theory 2 | 0.0;0.1185;0.2255 3 | 0.5;0.6524;0.3052 4 | 1.0;0.1291;0.0744 5 | 1.5;0.9445;0.7611 6 | 2.0;0.0272;0.1183 7 | 2.5;0.7598;0.0450 8 | 3.0;0.8159;0.2669 9 | 3.5;0.8003;0.1770 10 | 4.0;0.5716;0.2433 11 | 4.5;0.6651;0.2302 12 | 5.0;0.9983;0.0772 13 | 5.5;0.1004;0.0805 14 | 6.0;0.8433;0.6214 15 | 6.5;0.0067;0.1156 16 | 7.0;0.8238;0.1607 17 | 7.5;0.3952;0.0420 18 | 8.0;0.6452;0.1123 19 | 8.5;0.8480;0.3830 20 | 9.0;0.1986;0.5566 21 | 9.5;0.9114;0.6670 22 | 10.0;0.7693;0.5655 23 | 10.5;0.5009;0.4875 24 | 11.0;0.2110;0.0104 25 | 11.5;0.9227;0.4968 26 | 12.0;0.0461;0.2639 27 | 12.5;0.2177;0.2197 28 | 13.0;0.9554;0.9440 29 | 13.5;0.6130;0.2423 30 | -------------------------------------------------------------------------------- /data_eu.csv: -------------------------------------------------------------------------------- 1 | Time;Exp;Theory;Value 2 | 0,0;0,1185;0,226;10.135,11 3 | 0,5;0,6524;0,305;11.106,23 4 | 1,0;0,1291;0,074;12.347,45 5 | 1,5;0,9445;0,761;18.421,76 6 | 2,0;0,0272;0,118;13.551,78 7 | 2,5;0,7598;0,045;14.827,12 8 | 3,0;0,8159;0,267;14.305,56 9 | 3,5;0,8003;0,177;10.908,72 10 | 4,0;0,5716;0,243;11.106,23 11 | 4,5;0,6651;0,230;12.347,45 12 | 5,0;0,9983;0,077;18.421,76 13 | 5,5;0,1004;0,081;13.551,78 14 | 6,0;0,8433;0,621;14.827,12 15 | 6,5;0,0067;0,116;14.305,56 16 | 7,0;0,8238;0,161;10.908,72 17 | 7,5;0,3952;0,042;11.106,23 18 | 8,0;0,6452;0,112;12.347,45 19 | 8,5;0,8480;0,383;18.421,76 20 | 9,0;0,1986;0,557;13.551,78 21 | 9,5;0,9114;0,667;14.827,12 22 | 10,0;0,7693;0,566;14.305,56 23 | 10,5;0,5009;0,488;10.908,72 24 | 11,0;0,2110;0,010;11.106,23 25 | 11,5;0,9227;0,497;12.347,45 26 | 12,0;0,0461;0,264;18.421,76 27 | 12,5;0,2177;0,220;13.551,78 28 | 13,0;0,9554;0,944;14.827,12 29 | 13,5;0,6130;0,242;14.305,56 30 | -------------------------------------------------------------------------------- /etiquette_and_practices.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "#\n",
8 | "\n",
9 | "# Scientific Programming in Python\n",
10 | "## Karl N. Kirschner
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "# Expressions, Types and Variables\n",
13 | "\n",
14 | "
" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "## Expressions\n", 22 | "An expression is a command that returns a value. For example:" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "2 + 2" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "abs(-2 - 2)" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "
\n", 48 | "\n", 49 | "## Types\n", 50 | "\n", 51 | "Everything in Python is an object and every object in Python has a type. Some of the basic type include:\n", 52 | "\n", 53 | "- `int` (integer: a whole number with no decimal place)\n", 54 | " - `10`\n", 55 | " - `-3`\n", 56 | "- `float` (float: a number that has a decimal place)\n", 57 | " - `7.41`\n", 58 | " - `-0.006`\n", 59 | "- `bool` (boolean: a binary value that is either true or false)\n", 60 | " - `True`\n", 61 | " - `False`\n", 62 | "- `str` (string: a sequence of characters enclosed in single quotes, double quotes and a mixture)\n", 63 | " - `'this is a string using single quotes'`\n", 64 | " - `\"this is a string using double quotes\"`\n", 65 | " - `\"Socrates once said, 'The only true wisdom is in knowing you know nothing.'\"`\n", 66 | "- `NoneType` (a special type representing the absence of a value)\n", 67 | " - `None`" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": null, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "type(4)" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": null, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "type(2.1)" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": null, 91 | "metadata": {}, 92 | "outputs": [], 93 | "source": [ 94 | "type(3 > 4)" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": null, 100 | "metadata": {}, 101 | "outputs": [], 102 | "source": [ 103 | "print(3 > 4)" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": null, 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "type([1, 2, 3, 4])" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": null, 118 | "metadata": {}, 119 | "outputs": [], 120 | "source": [ 121 | "type(range(3, 9, 1))" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "Tip: using the `type` will help you figure out many problems in Python, especially when using functions. " 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "
\n", 136 | "\n", 137 | "## Variables\n", 138 | "A variable is assigned an object. You can modify that assignment (i.e., the variable's value) at any time.\n", 139 | "\n", 140 | "Example - Encode the following statement: \"my_variable is assigned the object [1.1, 1.2, 1.3]\":\n", 141 | "\n", 142 | "`my_variable = [1.1, 1.2, 1.3]`\n", 143 | "\n", 144 | "#### Naming rules\n", 145 | "- Variables can only contain letters (e.g., A, B, C, a, b, c), numbers (e.g., 1, 2, 3) and underscores (i.e., _ ).\n", 146 | "\n", 147 | "\n", 148 | "- Variable names can start with\n", 149 | " - a letter (e.g., `a_name`)\n", 150 | " - an underscore (e.g., `_name`)\n", 151 | " - these should be reserved for \"private\"/\"internal\" variables that are not meant to be used outside of the current code. For illustration, `from module import *` will not import any `_name` variables). However, you can do it directly: `from module import _name`.\n", 152 | "\n", 153 | "\n", 154 | "- Variable names cannot start with\n", 155 | " - a number (e.g., `1name`).\n", 156 | " \n", 157 | " \n", 158 | "- Spaces are not allowed in variable names (e.g., `student name`), so we use underscores instead of spaces (e.g., `student_name`).\n", 159 | "\n", 160 | "\n", 161 | "- pothole (a.k.a. snake) case is prefered in Python (e.g., `weather_data`).\n", 162 | "\n", 163 | "- Camal case (e.g., `weatherData`) is used less often.\n", 164 | "\n", 165 | "\n", 166 | "- Variable names should be descriptive, without being too long. For example, a code concening motorcycle wheels - `mc_wheels` is better than just `wheels` and the extended `number_of_wheels_on_a_motorycle`." 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [ 175 | "newton_quote = \"Isaac Newton once said, 'If I have seen further, it is by standing on the shoulders of GIANTS.'\"\n", 176 | "\n", 177 | "print(newton_quote)" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "- You cannot use Python keywords (e.g., `lambda`, `type`) as variable names." 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "import keyword\n", 194 | "keyword.kwlist" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "metadata": {}, 200 | "source": [ 201 | "
\n", 202 | "\n", 203 | "## Python's Comprehension\n", 204 | "\n", 205 | "Comprehensions are a Pythonic way to create containers with content on a single line (via for loops).\n", 206 | "\n", 207 | "Possible types:\n", 208 | "- List: `['a', 'b', 'c', 'c', 'c']`\n", 209 | "\n", 210 | "- Set: `{'a', 'b', 'c'}`\n", 211 | " - list with unique items\n", 212 | " \n", 213 | "- Dictionary: `{'a': 1, 'b': 2, 'c': 3}`\n", 214 | " - {key: value}" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "#### Lists\n", 222 | "\n", 223 | "The regular approach:" 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "execution_count": null, 229 | "metadata": {}, 230 | "outputs": [], 231 | "source": [ 232 | "my_list = []\n", 233 | "\n", 234 | "for number in range(1, 5, 1):\n", 235 | " my_list.append(number)\n", 236 | "\n", 237 | "my_list" 238 | ] 239 | }, 240 | { 241 | "cell_type": "markdown", 242 | "metadata": {}, 243 | "source": [ 244 | "The comprehension approach:" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": null, 250 | "metadata": {}, 251 | "outputs": [], 252 | "source": [ 253 | "[number for number in range(1, 5, 1)]" 254 | ] 255 | }, 256 | { 257 | "cell_type": "markdown", 258 | "metadata": {}, 259 | "source": [ 260 | "Can also do this with strings:" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": null, 266 | "metadata": {}, 267 | "outputs": [], 268 | "source": [ 269 | "[letter for letter in 'Learning this is super.']" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": {}, 275 | "source": [ 276 | "One can even generate nested lists:" 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": null, 282 | "metadata": {}, 283 | "outputs": [], 284 | "source": [ 285 | "[[number_1 + number_2 for number_2 in range(20, 25, 1)] for number_1 in range(1, 5, 1)]" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "metadata": {}, 291 | "source": [ 292 | "#### Sets" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": null, 298 | "metadata": {}, 299 | "outputs": [], 300 | "source": [ 301 | "{letter for letter in 'Learning this is super.'} ## non-duplicated letters (e.g., 's')" 302 | ] 303 | }, 304 | { 305 | "cell_type": "markdown", 306 | "metadata": {}, 307 | "source": [ 308 | "#### Dictionaries" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": null, 314 | "metadata": {}, 315 | "outputs": [], 316 | "source": [ 317 | "{number: number**2 for number in range(1, 5, 1)}" 318 | ] 319 | } 320 | ], 321 | "metadata": { 322 | "kernelspec": { 323 | "display_name": "Python 3 (ipykernel)", 324 | "language": "python", 325 | "name": "python3" 326 | }, 327 | "language_info": { 328 | "codemirror_mode": { 329 | "name": "ipython", 330 | "version": 3 331 | }, 332 | "file_extension": ".py", 333 | "mimetype": "text/x-python", 334 | "name": "python", 335 | "nbconvert_exporter": "python", 336 | "pygments_lexer": "ipython3", 337 | "version": "3.13.2" 338 | } 339 | }, 340 | "nbformat": 4, 341 | "nbformat_minor": 4 342 | } 343 | -------------------------------------------------------------------------------- /function_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#!/usr/bin/env python3\n", 10 | "\n", 11 | "\n", 12 | "def ask_for_input():\n", 13 | " adj_1 = input('Adjective: ')\n", 14 | " adj_2 = input('Adjective: ')\n", 15 | " noun_1 = input('Noun: ')\n", 16 | " noun_2 = input('Noun: ')\n", 17 | " adj_3 = input('Adjective: ')\n", 18 | " pl_noun_1 = input('Plural Noun: ')\n", 19 | " game = input('Game: ')\n", 20 | " pl_noun_2 = input('Plural Noun: ')\n", 21 | " verb_1 = input('Verb ending in -ing: ')\n", 22 | " verb_2 = input('Verb ending in -ing: ')\n", 23 | " print()\n", 24 | "\n", 25 | " return (adj_1, adj_2, noun_1, noun_2, adj_3, pl_noun_1, game, pl_noun_2, verb_1, verb_2)\n", 26 | "\n", 27 | "\n", 28 | "# Assign variables\n", 29 | "adj_1 = ''\n", 30 | "adj_2 = ''\n", 31 | "noun_1 = ''\n", 32 | "noun_2 = ''\n", 33 | "adj_3 = ''\n", 34 | "pl_Noun_1 = ''\n", 35 | "game = ''\n", 36 | "pl_Noun_2 = ''\n", 37 | "verb_1 = ''\n", 38 | "verb_2 = ''\n", 39 | "\n", 40 | "# Main part of program\n", 41 | "print()\n", 42 | "print('Let\\'s play Mad Libs')\n", 43 | "print('Please fill in the following information.')\n", 44 | "print()\n", 45 | "\n", 46 | "adj_1, adj_2, noun_1, noun_2, adj_3, pl_noun_1, game, pl_noun_2, verb_1, verb_2 = ask_for_input()\n", 47 | "\n", 48 | "print('A vacation is when you take a trip to some', adj_1, 'place with your', adj_2, 'family.')\n", 49 | "print('Usually you go to some place that is near a/an', noun_1, 'or up on a/an', noun_2, '.')\n", 50 | "print('A good vacation place is one where you can ride', pl_noun_1, 'or play', game)\n", 51 | "print('or go hunting for', pl_noun_2, '. I like to spend my time', verb_1, 'or', verb_2, '.')\n", 52 | "print()" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [] 61 | } 62 | ], 63 | "metadata": { 64 | "kernelspec": { 65 | "display_name": "Python 3", 66 | "language": "python", 67 | "name": "python3" 68 | }, 69 | "language_info": { 70 | "codemirror_mode": { 71 | "name": "ipython", 72 | "version": 3 73 | }, 74 | "file_extension": ".py", 75 | "mimetype": "text/x-python", 76 | "name": "python", 77 | "nbconvert_exporter": "python", 78 | "pygments_lexer": "ipython3", 79 | "version": "3.8.1" 80 | } 81 | }, 82 | "nbformat": 4, 83 | "nbformat_minor": 2 84 | } 85 | -------------------------------------------------------------------------------- /intro_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "#
" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "## Expressions\n", 22 | "An expression is a command that returns a value. For example:" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "2 + 2" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "abs(-2 - 2)" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "
\n", 48 | "\n", 49 | "## Types\n", 50 | "\n", 51 | "Everything in Python is an object and every object in Python has a type. Some of the basic type include:\n", 52 | "\n", 53 | "- `int` (integer: a whole number with no decimal place)\n", 54 | " - `10`\n", 55 | " - `-3`\n", 56 | "- `float` (float: a number that has a decimal place)\n", 57 | " - `7.41`\n", 58 | " - `-0.006`\n", 59 | "- `bool` (boolean: a binary value that is either true or false)\n", 60 | " - `True`\n", 61 | " - `False`\n", 62 | "- `str` (string: a sequence of characters enclosed in single quotes, double quotes and a mixture)\n", 63 | " - `'this is a string using single quotes'`\n", 64 | " - `\"this is a string using double quotes\"`\n", 65 | " - `\"Socrates once said, 'The only true wisdom is in knowing you know nothing.'\"`\n", 66 | "- `NoneType` (a special type representing the absence of a value)\n", 67 | " - `None`" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": null, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "type(4)" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": null, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "type(2.1)" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": null, 91 | "metadata": {}, 92 | "outputs": [], 93 | "source": [ 94 | "type(3 > 4)" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": null, 100 | "metadata": {}, 101 | "outputs": [], 102 | "source": [ 103 | "print(3 > 4)" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": null, 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "type([1, 2, 3, 4])" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": null, 118 | "metadata": {}, 119 | "outputs": [], 120 | "source": [ 121 | "type(range(3, 9, 1))" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "Tip: using the `type` will help you figure out many problems in Python, especially when using functions. " 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "
\n", 136 | "\n", 137 | "## Variables\n", 138 | "A variable is assigned an object. You can modify that assignment (i.e., the variable's value) at any time.\n", 139 | "\n", 140 | "Example - Encode the following statement: \"my_variable is assigned the object [1.1, 1.2, 1.3]\":\n", 141 | "\n", 142 | "`my_variable = [1.1, 1.2, 1.3]`\n", 143 | "\n", 144 | "#### Naming rules\n", 145 | "- Variables can only contain letters (e.g., A, B, C, a, b, c), numbers (e.g., 1, 2, 3) and underscores (i.e., _ ).\n", 146 | "\n", 147 | "\n", 148 | "- Variable names can start with\n", 149 | " - a letter (e.g., `a_name`)\n", 150 | " - an underscore (e.g., `_name`)\n", 151 | " - these should be reserved for \"private\"/\"internal\" variables that are not meant to be used outside of the current code. For illustration, `from module import *` will not import any `_name` variables). However, you can do it directly: `from module import _name`.\n", 152 | "\n", 153 | "\n", 154 | "- Variable names cannot start with\n", 155 | " - a number (e.g., `1name`).\n", 156 | " \n", 157 | " \n", 158 | "- Spaces are not allowed in variable names (e.g., `student name`), so we use underscores instead of spaces (e.g., `student_name`).\n", 159 | "\n", 160 | "\n", 161 | "- pothole (a.k.a. snake) case is prefered in Python (e.g., `weather_data`).\n", 162 | "\n", 163 | "- Camal case (e.g., `weatherData`) is used less often.\n", 164 | "\n", 165 | "\n", 166 | "- Variable names should be descriptive, without being too long. For example, a code concening motorcycle wheels - `mc_wheels` is better than just `wheels` and the extended `number_of_wheels_on_a_motorycle`." 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [ 175 | "newton_quote = \"Isaac Newton once said, 'If I have seen further, it is by standing on the shoulders of GIANTS.'\"\n", 176 | "\n", 177 | "print(newton_quote)" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "- You cannot use Python keywords (e.g., `lambda`, `type`) as variable names." 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "import keyword\n", 194 | "keyword.kwlist" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "metadata": {}, 200 | "source": [ 201 | "
\n", 202 | "\n", 203 | "## Python's Comprehension\n", 204 | "\n", 205 | "Comprehensions are a Pythonic way to create containers with content on a single line (via for loops).\n", 206 | "\n", 207 | "Possible types:\n", 208 | "- List: `['a', 'b', 'c', 'c', 'c']`\n", 209 | "\n", 210 | "- Set: `{'a', 'b', 'c'}`\n", 211 | " - list with unique items\n", 212 | " \n", 213 | "- Dictionary: `{'a': 1, 'b': 2, 'c': 3}`\n", 214 | " - {key: value}" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "#### Lists\n", 222 | "\n", 223 | "The regular approach:" 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "execution_count": null, 229 | "metadata": {}, 230 | "outputs": [], 231 | "source": [ 232 | "my_list = []\n", 233 | "\n", 234 | "for number in range(1, 5, 1):\n", 235 | " my_list.append(number)\n", 236 | "\n", 237 | "my_list" 238 | ] 239 | }, 240 | { 241 | "cell_type": "markdown", 242 | "metadata": {}, 243 | "source": [ 244 | "The comprehension approach:" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": null, 250 | "metadata": {}, 251 | "outputs": [], 252 | "source": [ 253 | "[number for number in range(1, 5, 1)]" 254 | ] 255 | }, 256 | { 257 | "cell_type": "markdown", 258 | "metadata": {}, 259 | "source": [ 260 | "Can also do this with strings:" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": null, 266 | "metadata": {}, 267 | "outputs": [], 268 | "source": [ 269 | "[letter for letter in 'Learning this is super.']" 270 | ] 271 | }, 272 | { 273 | "cell_type": "markdown", 274 | "metadata": {}, 275 | "source": [ 276 | "One can even generate nested lists:" 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": null, 282 | "metadata": {}, 283 | "outputs": [], 284 | "source": [ 285 | "[[number_1 + number_2 for number_2 in range(20, 25, 1)] for number_1 in range(1, 5, 1)]" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "metadata": {}, 291 | "source": [ 292 | "#### Sets" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": null, 298 | "metadata": {}, 299 | "outputs": [], 300 | "source": [ 301 | "{letter for letter in 'Learning this is super.'} ## non-duplicated letters (e.g., 's')" 302 | ] 303 | }, 304 | { 305 | "cell_type": "markdown", 306 | "metadata": {}, 307 | "source": [ 308 | "#### Dictionaries" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": null, 314 | "metadata": {}, 315 | "outputs": [], 316 | "source": [ 317 | "{number: number**2 for number in range(1, 5, 1)}" 318 | ] 319 | } 320 | ], 321 | "metadata": { 322 | "kernelspec": { 323 | "display_name": "Python 3 (ipykernel)", 324 | "language": "python", 325 | "name": "python3" 326 | }, 327 | "language_info": { 328 | "codemirror_mode": { 329 | "name": "ipython", 330 | "version": 3 331 | }, 332 | "file_extension": ".py", 333 | "mimetype": "text/x-python", 334 | "name": "python", 335 | "nbconvert_exporter": "python", 336 | "pygments_lexer": "ipython3", 337 | "version": "3.13.2" 338 | } 339 | }, 340 | "nbformat": 4, 341 | "nbformat_minor": 4 342 | } 343 | -------------------------------------------------------------------------------- /function_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#!/usr/bin/env python3\n", 10 | "\n", 11 | "\n", 12 | "def ask_for_input():\n", 13 | " adj_1 = input('Adjective: ')\n", 14 | " adj_2 = input('Adjective: ')\n", 15 | " noun_1 = input('Noun: ')\n", 16 | " noun_2 = input('Noun: ')\n", 17 | " adj_3 = input('Adjective: ')\n", 18 | " pl_noun_1 = input('Plural Noun: ')\n", 19 | " game = input('Game: ')\n", 20 | " pl_noun_2 = input('Plural Noun: ')\n", 21 | " verb_1 = input('Verb ending in -ing: ')\n", 22 | " verb_2 = input('Verb ending in -ing: ')\n", 23 | " print()\n", 24 | "\n", 25 | " return (adj_1, adj_2, noun_1, noun_2, adj_3, pl_noun_1, game, pl_noun_2, verb_1, verb_2)\n", 26 | "\n", 27 | "\n", 28 | "# Assign variables\n", 29 | "adj_1 = ''\n", 30 | "adj_2 = ''\n", 31 | "noun_1 = ''\n", 32 | "noun_2 = ''\n", 33 | "adj_3 = ''\n", 34 | "pl_Noun_1 = ''\n", 35 | "game = ''\n", 36 | "pl_Noun_2 = ''\n", 37 | "verb_1 = ''\n", 38 | "verb_2 = ''\n", 39 | "\n", 40 | "# Main part of program\n", 41 | "print()\n", 42 | "print('Let\\'s play Mad Libs')\n", 43 | "print('Please fill in the following information.')\n", 44 | "print()\n", 45 | "\n", 46 | "adj_1, adj_2, noun_1, noun_2, adj_3, pl_noun_1, game, pl_noun_2, verb_1, verb_2 = ask_for_input()\n", 47 | "\n", 48 | "print('A vacation is when you take a trip to some', adj_1, 'place with your', adj_2, 'family.')\n", 49 | "print('Usually you go to some place that is near a/an', noun_1, 'or up on a/an', noun_2, '.')\n", 50 | "print('A good vacation place is one where you can ride', pl_noun_1, 'or play', game)\n", 51 | "print('or go hunting for', pl_noun_2, '. I like to spend my time', verb_1, 'or', verb_2, '.')\n", 52 | "print()" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [] 61 | } 62 | ], 63 | "metadata": { 64 | "kernelspec": { 65 | "display_name": "Python 3", 66 | "language": "python", 67 | "name": "python3" 68 | }, 69 | "language_info": { 70 | "codemirror_mode": { 71 | "name": "ipython", 72 | "version": 3 73 | }, 74 | "file_extension": ".py", 75 | "mimetype": "text/x-python", 76 | "name": "python", 77 | "nbconvert_exporter": "python", 78 | "pygments_lexer": "ipython3", 79 | "version": "3.8.1" 80 | } 81 | }, 82 | "nbformat": 4, 83 | "nbformat_minor": 2 84 | } 85 | -------------------------------------------------------------------------------- /intro_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n",
8 | "\n",
9 | "# Scientific Programming in Python\n",
10 | "## Karl N. Kirschner
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "## Introductory Python3 Examples\n",
13 | "\n",
14 | "## (and Demo of Jupyter Notebook / Colaboratory)\n",
15 | "\n",
16 | "
" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "## What might I consider a good Jupyter-notebook and coding be for a homework solution?\n", 24 | "\n", 25 | "### Example 1 - A Solution that Contains All Key Ingredients\n", 26 | "- markdown cells (name, context and sources)\n", 27 | "- code cells (clear, concise and well formatted)\n", 28 | "\n", 29 | "
" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "#### Title: A Simple Example with a Markdown and Code Cell\n", 37 | "\n", 38 | "Author: SciPro_012\n", 39 | "\n", 40 | "Date: March 31, 2025\n", 41 | "\n", 42 | "Goal: Provide an example of Kirschner's expectations for a good Jupyter-notebook and coding. Both markdown and code cells are exemplified.\n", 43 | "\n", 44 | "Task 1: Name some famous scientific women [1-4] using a for loop.\n", 45 | "\n", 46 | "References\n", 47 | "1. Miller, C.C. \"A gifted mathematician who is now recognized as the first computer programmer\". The New York Times, 8 March 2018. https://www.nytimes.com/interactive/2018/obituaries/overlooked-ada-lovelace.html. Online, accessed September 9, 2022.\n", 48 | "2. Rockwell, S. \"The life and legacy of Marie Curie.\" The Yale Journal of Biology and Medicine 76.4-6 (2003): 167.\n", 49 | "3. Uberoi, C. \"Rosalind Franklin: The woman scientist of DNA.\" Resonance 9.3 (2004): 3-5.\n", 50 | "4. Maria Goeppert Mayer - Facts. NobelPrize.org. Nobel Prize Outreach AB 2022. https://www.nobelprize.org/prizes/physics/1963/mayer/facts. Online, accessed September 9, 2022." 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 1, 56 | "metadata": {}, 57 | "outputs": [ 58 | { 59 | "name": "stdout", 60 | "output_type": "stream", 61 | "text": [ 62 | "Scientist number 1 is Ada Love.\n", 63 | "Scientist number 2 is Marie Curie.\n", 64 | "Scientist number 3 is Rosalind Franklin.\n", 65 | "Scientist number 4 is Maria Goeppert Mayer.\n" 66 | ] 67 | } 68 | ], 69 | "source": [ 70 | "index = 1\n", 71 | "\n", 72 | "famous_scientists = ['Ada Love', 'Marie Curie', 'Rosalind Franklin', 'Maria Goeppert Mayer']\n", 73 | "\n", 74 | "for person in famous_scientists:\n", 75 | " print(f'Scientist number {index} is {person}.')\n", 76 | " index += 1" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "
\n", 84 | "\n", 85 | "Talking Points:\n", 86 | "\n", 87 | "1. Commenting code using Jupyter's markdown capabilities\n", 88 | " - porvide context\n", 89 | " - convey thoughts and ideas\n", 90 | " - provide sources of information\n", 91 | "\n", 92 | "\n", 93 | "2. Creating objects (i.e., `index` and `famous_scientists`)\n", 94 | " - human-readable (versus nondescriptive `x` or `s`)\n", 95 | " - proper spacing between list items\n", 96 | "\n", 97 | "\n", 98 | "3. Creation of a \"for loop\" (versus 4 print statements)\n", 99 | " - reduces chances of error\n", 100 | "\n", 101 | "\n", 102 | "4. Python incrementing a number by plus 1 (note: could also `-=`)\n", 103 | "\n", 104 | "\n", 105 | "5. Print statement with f-string formatting\n", 106 | " - concise and clear\n", 107 | "\n", 108 | "\n", 109 | "6. Overall, concisely and cleanly written code" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "
\n", 117 | "\n", 118 | "The purpose of the rest of this notebook is to provide you additional insight into how one might improve their own work.\n", 119 | "\n", 120 | "- A poor example will be given, followed by how it can be improved.\n", 121 | "\n", 122 | "\n", 123 | "### Example 2 - A Poor Solution\n", 124 | "There are always multiple solutions for achieving the same goal.\n", 125 | "\n", 126 | "Here is a poor solution to the homework task of computing the cube of the numbers 0-9: \n", 127 | "\n", 128 | "
" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "#Simple Program, SciPro_012 March 20, 2025\n", 138 | "#Ein Beispiel of what might be a poor solution for demonstrating how it can be improved (better writting, reducing error) through iterative revisions.\n", 139 | "\n", 140 | "\n", 141 | "print('The cube of', 0, 'ist', 0*0*0) # take the cube of 0\n", 142 | "print('The cube of {0} ist {1}.'.format(1, 1*1*1)) # take the cub of 1\n", 143 | "print('The cube of {0} ist {1}.'.format(2, 2**2)) # ect.\n", 144 | "print('The cube of', 3, 'ist', 3*3*3, '.')\n", 145 | "print('The cube of', 3, 'ist {0}.'.format(4*4*4))\n", 146 | "print('The cube of {0} ist {1}.'.format(4, 5*5*5))\n", 147 | "print('The cube of {0} is {1}.'.format(3, 6*6*6))\n", 148 | "\n", 149 | "print(\"The cube of {0} ist {1}.\".format(6, 7*7*7))\n", 150 | "print(f\"The cube of {8} ist {8*8*8}.\")\n", 151 | "print(f'The cube of 9 ist {9*9*9}.')" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "
\n", 159 | "\n", 160 | "Problems with the above code:\n", 161 | "\n", 162 | "1. Lots of information group at once in the first two lines\n", 163 | "\n", 164 | "\n", 165 | "2. The problem context is poorly given as a comment (a markdown cell would have been better), and with mixed German/English language\n", 166 | " - German: `Ein Beispiel` and `ist`\n", 167 | " - Misspelled: `ect.`, `cub`\n", 168 | "\n", 169 | "3. Comment runs off the screen - must scroll to read it all\n", 170 | "\n", 171 | "\n", 172 | "5. Inline comments (e.g., `# take the cube of 0`) are not needed to understand the code\n", 173 | "\n", 174 | "\n", 175 | "6. Inconsistent formatting (i.e. printing and blank line usage)\n", 176 | "\n", 177 | " - `print('The cube of', 0, 'ist', 0*0*0)`, vs\n", 178 | " - `print('The cube of {0} ist {1}.'.format(1, 1*1*1))`, vs\n", 179 | " - `print(f\"The cube of {8} ist {8*8*8}.\")`\n", 180 | "\n", 181 | "\n", 182 | "7. Not concisely written (10 lines to print the cube of each number)\n", 183 | " - notice that the errors are hard to see\n", 184 | " - usage of 3 instead of 4: `print('The cube of', 3, 'ist {0}.'.format(4*4*4))`\n", 185 | " - raising to a power of 2, not 3: `print('The cube of {0} ist {1}.'.format(2, 2**2))`\n", 186 | " \n", 187 | "Could have been done better, and thus reduce the chances of introducing a programmer error." 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "### Repeating Example 2 - A Better Solution\n", 195 | "\n", 196 | "Having writing the solution's first draft, now revise:\n", 197 | "\n", 198 | "- communicate better using markdown cells\n", 199 | "- coding more concisely\n", 200 | "\n", 201 | "
" 202 | ] 203 | }, 204 | { 205 | "cell_type": "markdown", 206 | "metadata": {}, 207 | "source": [ 208 | "Simple Program Improved\n", 209 | "\n", 210 | "Author: SciPro_012\n", 211 | "\n", 212 | "Date: March 20, 2025\n", 213 | "\n", 214 | "---\n", 215 | "\n", 216 | "Goal: Provide an example of how code can be improved through a revision.\n", 217 | "\n", 218 | "Task 1: Take the cube of intergers from 0 to 9." 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": null, 224 | "metadata": {}, 225 | "outputs": [], 226 | "source": [ 227 | "number_tuple = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n", 228 | "\n", 229 | "for number in number_tuple:\n", 230 | " number_cube = number**3\n", 231 | " print('The cube of {0} is {1}.'.format(number, number_cube))" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "Result: a much better solution.\n", 239 | "\n", 240 | "However, there are 2 issues:\n", 241 | "\n", 242 | "1. manually providing a list of sequential numbers (could introduce a typo)\n", 243 | "\n", 244 | "\n", 245 | "2. the use of older print statement (less concise and less readable)\n", 246 | "\n", 247 | "
" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "### Repeating Example 2 - Better Solutions" 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "metadata": {}, 260 | "source": [ 261 | "Now, let's further improve the code cell part above - use a built-in function (`range`)\n", 262 | "\n", 263 | "
" 264 | ] 265 | }, 266 | { 267 | "cell_type": "markdown", 268 | "metadata": {}, 269 | "source": [ 270 | "Simple Program Improved Again\n", 271 | "\n", 272 | "Author: SciPro_01234\n", 273 | "\n", 274 | "Date: October 2, 2023\n", 275 | "\n", 276 | "---\n", 277 | "\n", 278 | "Goal: Provide an example of how code can be improved through iterative revisions.\n", 279 | "\n", 280 | "Task 1: Take the cube of intergers from 0 to 9." 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": null, 286 | "metadata": {}, 287 | "outputs": [], 288 | "source": [ 289 | "number_list = range(0, 10, 1)\n", 290 | "\n", 291 | "for number in number_list:\n", 292 | " print(f'The cube of {number} is {number**3}.')" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "
\n", 300 | "\n", 301 | "The following is better than above example because the codes is\n", 302 | "\n", 303 | "1. still readable (i.e., not too concise nor too \"clever\"), and\n", 304 | "\n", 305 | "2. the number of variables/objects is kept to a minimum, specifically\n", 306 | " - two: `number` and `number_list`, versus\n", 307 | " - three: `number`, `number_list` and `number_cube`)\n", 308 | "\n", 309 | "3. use of modern `f-string` print statement" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "metadata": {}, 315 | "source": [ 316 | "An alternative and still good solution, which is even a bit simpler (i.e., elimating the need to define a `number_list` object)." 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": null, 322 | "metadata": {}, 323 | "outputs": [], 324 | "source": [ 325 | "number = 0\n", 326 | "\n", 327 | "while number < 10:\n", 328 | " print(f'The cube of {number} is {number**3}.')\n", 329 | " number += 1" 330 | ] 331 | }, 332 | { 333 | "cell_type": "markdown", 334 | "metadata": {}, 335 | "source": [ 336 | "
\n", 337 | "\n", 338 | "## Take-home Points\n", 339 | "\n", 340 | "1. Better coding is acheived through iterations of its writing (i.e., code revision).\n", 341 | "\n", 342 | "2. Context and communicting thoughts/ideas using Jupyter's markdown capabilities\n", 343 | "\n", 344 | "3. Good academics scholarship (e.g., writing, citing sources)\n", 345 | "\n", 346 | "4. Human-readable object/variable naming (versus nondescriptive `x` or `s`)\n", 347 | "\n", 348 | "5. Concise coding (e.g., for loop, f-string formatting)\n", 349 | "\n", 350 | "6. Coding that reduces chances of introducing programmer's errors\n", 351 | "\n", 352 | "7. Citing knowledge (see first example above)" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": null, 358 | "metadata": {}, 359 | "outputs": [], 360 | "source": [] 361 | } 362 | ], 363 | "metadata": { 364 | "kernelspec": { 365 | "display_name": "Python 3 (ipykernel)", 366 | "language": "python", 367 | "name": "python3" 368 | }, 369 | "language_info": { 370 | "codemirror_mode": { 371 | "name": "ipython", 372 | "version": 3 373 | }, 374 | "file_extension": ".py", 375 | "mimetype": "text/x-python", 376 | "name": "python", 377 | "nbconvert_exporter": "python", 378 | "pygments_lexer": "ipython3", 379 | "version": "3.13.2" 380 | } 381 | }, 382 | "nbformat": 4, 383 | "nbformat_minor": 4 384 | } 385 | -------------------------------------------------------------------------------- /introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "##
" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "## What might I consider a good Jupyter-notebook and coding be for a homework solution?\n", 24 | "\n", 25 | "### Example 1 - A Solution that Contains All Key Ingredients\n", 26 | "- markdown cells (name, context and sources)\n", 27 | "- code cells (clear, concise and well formatted)\n", 28 | "\n", 29 | "
" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "#### Title: A Simple Example with a Markdown and Code Cell\n", 37 | "\n", 38 | "Author: SciPro_012\n", 39 | "\n", 40 | "Date: March 31, 2025\n", 41 | "\n", 42 | "Goal: Provide an example of Kirschner's expectations for a good Jupyter-notebook and coding. Both markdown and code cells are exemplified.\n", 43 | "\n", 44 | "Task 1: Name some famous scientific women [1-4] using a for loop.\n", 45 | "\n", 46 | "References\n", 47 | "1. Miller, C.C. \"A gifted mathematician who is now recognized as the first computer programmer\". The New York Times, 8 March 2018. https://www.nytimes.com/interactive/2018/obituaries/overlooked-ada-lovelace.html. Online, accessed September 9, 2022.\n", 48 | "2. Rockwell, S. \"The life and legacy of Marie Curie.\" The Yale Journal of Biology and Medicine 76.4-6 (2003): 167.\n", 49 | "3. Uberoi, C. \"Rosalind Franklin: The woman scientist of DNA.\" Resonance 9.3 (2004): 3-5.\n", 50 | "4. Maria Goeppert Mayer - Facts. NobelPrize.org. Nobel Prize Outreach AB 2022. https://www.nobelprize.org/prizes/physics/1963/mayer/facts. Online, accessed September 9, 2022." 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 1, 56 | "metadata": {}, 57 | "outputs": [ 58 | { 59 | "name": "stdout", 60 | "output_type": "stream", 61 | "text": [ 62 | "Scientist number 1 is Ada Love.\n", 63 | "Scientist number 2 is Marie Curie.\n", 64 | "Scientist number 3 is Rosalind Franklin.\n", 65 | "Scientist number 4 is Maria Goeppert Mayer.\n" 66 | ] 67 | } 68 | ], 69 | "source": [ 70 | "index = 1\n", 71 | "\n", 72 | "famous_scientists = ['Ada Love', 'Marie Curie', 'Rosalind Franklin', 'Maria Goeppert Mayer']\n", 73 | "\n", 74 | "for person in famous_scientists:\n", 75 | " print(f'Scientist number {index} is {person}.')\n", 76 | " index += 1" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "
\n", 84 | "\n", 85 | "Talking Points:\n", 86 | "\n", 87 | "1. Commenting code using Jupyter's markdown capabilities\n", 88 | " - porvide context\n", 89 | " - convey thoughts and ideas\n", 90 | " - provide sources of information\n", 91 | "\n", 92 | "\n", 93 | "2. Creating objects (i.e., `index` and `famous_scientists`)\n", 94 | " - human-readable (versus nondescriptive `x` or `s`)\n", 95 | " - proper spacing between list items\n", 96 | "\n", 97 | "\n", 98 | "3. Creation of a \"for loop\" (versus 4 print statements)\n", 99 | " - reduces chances of error\n", 100 | "\n", 101 | "\n", 102 | "4. Python incrementing a number by plus 1 (note: could also `-=`)\n", 103 | "\n", 104 | "\n", 105 | "5. Print statement with f-string formatting\n", 106 | " - concise and clear\n", 107 | "\n", 108 | "\n", 109 | "6. Overall, concisely and cleanly written code" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "
\n", 117 | "\n", 118 | "The purpose of the rest of this notebook is to provide you additional insight into how one might improve their own work.\n", 119 | "\n", 120 | "- A poor example will be given, followed by how it can be improved.\n", 121 | "\n", 122 | "\n", 123 | "### Example 2 - A Poor Solution\n", 124 | "There are always multiple solutions for achieving the same goal.\n", 125 | "\n", 126 | "Here is a poor solution to the homework task of computing the cube of the numbers 0-9: \n", 127 | "\n", 128 | "
" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "#Simple Program, SciPro_012 March 20, 2025\n", 138 | "#Ein Beispiel of what might be a poor solution for demonstrating how it can be improved (better writting, reducing error) through iterative revisions.\n", 139 | "\n", 140 | "\n", 141 | "print('The cube of', 0, 'ist', 0*0*0) # take the cube of 0\n", 142 | "print('The cube of {0} ist {1}.'.format(1, 1*1*1)) # take the cub of 1\n", 143 | "print('The cube of {0} ist {1}.'.format(2, 2**2)) # ect.\n", 144 | "print('The cube of', 3, 'ist', 3*3*3, '.')\n", 145 | "print('The cube of', 3, 'ist {0}.'.format(4*4*4))\n", 146 | "print('The cube of {0} ist {1}.'.format(4, 5*5*5))\n", 147 | "print('The cube of {0} is {1}.'.format(3, 6*6*6))\n", 148 | "\n", 149 | "print(\"The cube of {0} ist {1}.\".format(6, 7*7*7))\n", 150 | "print(f\"The cube of {8} ist {8*8*8}.\")\n", 151 | "print(f'The cube of 9 ist {9*9*9}.')" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "
\n", 159 | "\n", 160 | "Problems with the above code:\n", 161 | "\n", 162 | "1. Lots of information group at once in the first two lines\n", 163 | "\n", 164 | "\n", 165 | "2. The problem context is poorly given as a comment (a markdown cell would have been better), and with mixed German/English language\n", 166 | " - German: `Ein Beispiel` and `ist`\n", 167 | " - Misspelled: `ect.`, `cub`\n", 168 | "\n", 169 | "3. Comment runs off the screen - must scroll to read it all\n", 170 | "\n", 171 | "\n", 172 | "5. Inline comments (e.g., `# take the cube of 0`) are not needed to understand the code\n", 173 | "\n", 174 | "\n", 175 | "6. Inconsistent formatting (i.e. printing and blank line usage)\n", 176 | "\n", 177 | " - `print('The cube of', 0, 'ist', 0*0*0)`, vs\n", 178 | " - `print('The cube of {0} ist {1}.'.format(1, 1*1*1))`, vs\n", 179 | " - `print(f\"The cube of {8} ist {8*8*8}.\")`\n", 180 | "\n", 181 | "\n", 182 | "7. Not concisely written (10 lines to print the cube of each number)\n", 183 | " - notice that the errors are hard to see\n", 184 | " - usage of 3 instead of 4: `print('The cube of', 3, 'ist {0}.'.format(4*4*4))`\n", 185 | " - raising to a power of 2, not 3: `print('The cube of {0} ist {1}.'.format(2, 2**2))`\n", 186 | " \n", 187 | "Could have been done better, and thus reduce the chances of introducing a programmer error." 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "### Repeating Example 2 - A Better Solution\n", 195 | "\n", 196 | "Having writing the solution's first draft, now revise:\n", 197 | "\n", 198 | "- communicate better using markdown cells\n", 199 | "- coding more concisely\n", 200 | "\n", 201 | "
" 202 | ] 203 | }, 204 | { 205 | "cell_type": "markdown", 206 | "metadata": {}, 207 | "source": [ 208 | "Simple Program Improved\n", 209 | "\n", 210 | "Author: SciPro_012\n", 211 | "\n", 212 | "Date: March 20, 2025\n", 213 | "\n", 214 | "---\n", 215 | "\n", 216 | "Goal: Provide an example of how code can be improved through a revision.\n", 217 | "\n", 218 | "Task 1: Take the cube of intergers from 0 to 9." 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": null, 224 | "metadata": {}, 225 | "outputs": [], 226 | "source": [ 227 | "number_tuple = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n", 228 | "\n", 229 | "for number in number_tuple:\n", 230 | " number_cube = number**3\n", 231 | " print('The cube of {0} is {1}.'.format(number, number_cube))" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "Result: a much better solution.\n", 239 | "\n", 240 | "However, there are 2 issues:\n", 241 | "\n", 242 | "1. manually providing a list of sequential numbers (could introduce a typo)\n", 243 | "\n", 244 | "\n", 245 | "2. the use of older print statement (less concise and less readable)\n", 246 | "\n", 247 | "
" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "### Repeating Example 2 - Better Solutions" 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "metadata": {}, 260 | "source": [ 261 | "Now, let's further improve the code cell part above - use a built-in function (`range`)\n", 262 | "\n", 263 | "
" 264 | ] 265 | }, 266 | { 267 | "cell_type": "markdown", 268 | "metadata": {}, 269 | "source": [ 270 | "Simple Program Improved Again\n", 271 | "\n", 272 | "Author: SciPro_01234\n", 273 | "\n", 274 | "Date: October 2, 2023\n", 275 | "\n", 276 | "---\n", 277 | "\n", 278 | "Goal: Provide an example of how code can be improved through iterative revisions.\n", 279 | "\n", 280 | "Task 1: Take the cube of intergers from 0 to 9." 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": null, 286 | "metadata": {}, 287 | "outputs": [], 288 | "source": [ 289 | "number_list = range(0, 10, 1)\n", 290 | "\n", 291 | "for number in number_list:\n", 292 | " print(f'The cube of {number} is {number**3}.')" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "
\n", 300 | "\n", 301 | "The following is better than above example because the codes is\n", 302 | "\n", 303 | "1. still readable (i.e., not too concise nor too \"clever\"), and\n", 304 | "\n", 305 | "2. the number of variables/objects is kept to a minimum, specifically\n", 306 | " - two: `number` and `number_list`, versus\n", 307 | " - three: `number`, `number_list` and `number_cube`)\n", 308 | "\n", 309 | "3. use of modern `f-string` print statement" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "metadata": {}, 315 | "source": [ 316 | "An alternative and still good solution, which is even a bit simpler (i.e., elimating the need to define a `number_list` object)." 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": null, 322 | "metadata": {}, 323 | "outputs": [], 324 | "source": [ 325 | "number = 0\n", 326 | "\n", 327 | "while number < 10:\n", 328 | " print(f'The cube of {number} is {number**3}.')\n", 329 | " number += 1" 330 | ] 331 | }, 332 | { 333 | "cell_type": "markdown", 334 | "metadata": {}, 335 | "source": [ 336 | "
\n", 337 | "\n", 338 | "## Take-home Points\n", 339 | "\n", 340 | "1. Better coding is acheived through iterations of its writing (i.e., code revision).\n", 341 | "\n", 342 | "2. Context and communicting thoughts/ideas using Jupyter's markdown capabilities\n", 343 | "\n", 344 | "3. Good academics scholarship (e.g., writing, citing sources)\n", 345 | "\n", 346 | "4. Human-readable object/variable naming (versus nondescriptive `x` or `s`)\n", 347 | "\n", 348 | "5. Concise coding (e.g., for loop, f-string formatting)\n", 349 | "\n", 350 | "6. Coding that reduces chances of introducing programmer's errors\n", 351 | "\n", 352 | "7. Citing knowledge (see first example above)" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": null, 358 | "metadata": {}, 359 | "outputs": [], 360 | "source": [] 361 | } 362 | ], 363 | "metadata": { 364 | "kernelspec": { 365 | "display_name": "Python 3 (ipykernel)", 366 | "language": "python", 367 | "name": "python3" 368 | }, 369 | "language_info": { 370 | "codemirror_mode": { 371 | "name": "ipython", 372 | "version": 3 373 | }, 374 | "file_extension": ".py", 375 | "mimetype": "text/x-python", 376 | "name": "python", 377 | "nbconvert_exporter": "python", 378 | "pygments_lexer": "ipython3", 379 | "version": "3.13.2" 380 | } 381 | }, 382 | "nbformat": 4, 383 | "nbformat_minor": 4 384 | } 385 | -------------------------------------------------------------------------------- /introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n",
8 | "\n",
9 | "# Scientific Programming in Python\n",
10 | "## Karl N. Kirschner, Ph.D.
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "# Course Introduction\n",
13 | "\n",
14 | "
" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "
\n", 70 | "\n", 71 | "# Good Practices in Programming and Other Disciplines\n", 72 | "## (and keys to success in this course)\n", 73 | "\n", 74 | "### $\\textrm{C}^3$: code is written concisely, with a clear thought process, and placed into context\n", 75 | "\n", 76 | "1. **Concise**, cleanly written code and output\n", 77 | " - Easy to read and understand\n", 78 | " - Reduced chances of introducing programmer error\n", 79 | " - Easier to debug\n", 80 | "
\n", 81 | "\n", 82 | "2. **Clear** thought process with a logical grouping of code\n", 83 | " - User-defined functions that contain a single concept\n", 84 | " - Logical separation and isolation of individual ideas (e.g. using separate code cells & user-defined functions)\n", 85 | " - Promotes usability and reusability in future code (i.e., user-defined functions)\n", 86 | " - Easier to debug\n", 87 | "
\n", 88 | "\n", 89 | "3. **Context** for the code's a) purpose and b) usage are provided\n", 90 | " - Block comments, in-line comments and docstrings (e.g., user-defined functions) - purpose, usage, special notes\n", 91 | " - Jupyter-notebook markdown language (citations, data interpretation)\n", 92 | "\n", 93 | "\n", 94 | "### K.I.S.S.: Keep It (i.e., coding) Simple & Smart\n", 95 | "- $\\textrm{C}^3$ - concise, clear and context\n", 96 | "- Use of built-in functions over libraries with large overhead\n", 97 | "- User-defined functions for reproducibility, reuse, error reduction and isolating ideas\n", 98 | "\n", 99 | "$\\textrm{C}^3$ and K.I.S.S. are the same keys to success that are found in **all** scientific working.\n", 100 | "\n", 101 | "### Academic Scholarship: Developed during bachelor studies\n", 102 | "\n", 103 | "**You will need to do this for your thesis.**\n", 104 | "\n", 105 | "- **Citing sources** of existing knowledge\n", 106 | " - Providing credit to scientists and programmers\n", 107 | " - Helps to enable reproducibility\n", 108 | " - Indicates that you are well-educated (and you pay attention to details)\n", 109 | "- **Communicating** your thoughts/ideas (less assumption occur then)\n", 110 | "- **Good Writing**\n", 111 | " - $\\textrm{C}^3$\n", 112 | " - K.I.S.S.\n", 113 | " - Using complete sentences (i.e., a subject, a verb and usually an object)\n", 114 | "- Providing **units** for numbers when appropriate\n", 115 | "\n", 116 | "
" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "## Scientific Programming\n", 124 | "\n", 125 | "\n",
126 | " ![](\"00_images/scientifc_programing_bing.jpeg\")
\n",
127 | "Generated with Bing AI ∙ September 25, 2024 at 12:21 PM\n",
128 | " \n",
129 | "\n",
130 | "**Definition**\n",
131 | "
" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 11 | "\n", 12 | "#
" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "
\n", 70 | "\n", 71 | "# Good Practices in Programming and Other Disciplines\n", 72 | "## (and keys to success in this course)\n", 73 | "\n", 74 | "### $\\textrm{C}^3$: code is written concisely, with a clear thought process, and placed into context\n", 75 | "\n", 76 | "1. **Concise**, cleanly written code and output\n", 77 | " - Easy to read and understand\n", 78 | " - Reduced chances of introducing programmer error\n", 79 | " - Easier to debug\n", 80 | "
\n", 81 | "\n", 82 | "2. **Clear** thought process with a logical grouping of code\n", 83 | " - User-defined functions that contain a single concept\n", 84 | " - Logical separation and isolation of individual ideas (e.g. using separate code cells & user-defined functions)\n", 85 | " - Promotes usability and reusability in future code (i.e., user-defined functions)\n", 86 | " - Easier to debug\n", 87 | "
\n", 88 | "\n", 89 | "3. **Context** for the code's a) purpose and b) usage are provided\n", 90 | " - Block comments, in-line comments and docstrings (e.g., user-defined functions) - purpose, usage, special notes\n", 91 | " - Jupyter-notebook markdown language (citations, data interpretation)\n", 92 | "\n", 93 | "\n", 94 | "### K.I.S.S.: Keep It (i.e., coding) Simple & Smart\n", 95 | "- $\\textrm{C}^3$ - concise, clear and context\n", 96 | "- Use of built-in functions over libraries with large overhead\n", 97 | "- User-defined functions for reproducibility, reuse, error reduction and isolating ideas\n", 98 | "\n", 99 | "$\\textrm{C}^3$ and K.I.S.S. are the same keys to success that are found in **all** scientific working.\n", 100 | "\n", 101 | "### Academic Scholarship: Developed during bachelor studies\n", 102 | "\n", 103 | "**You will need to do this for your thesis.**\n", 104 | "\n", 105 | "- **Citing sources** of existing knowledge\n", 106 | " - Providing credit to scientists and programmers\n", 107 | " - Helps to enable reproducibility\n", 108 | " - Indicates that you are well-educated (and you pay attention to details)\n", 109 | "- **Communicating** your thoughts/ideas (less assumption occur then)\n", 110 | "- **Good Writing**\n", 111 | " - $\\textrm{C}^3$\n", 112 | " - K.I.S.S.\n", 113 | " - Using complete sentences (i.e., a subject, a verb and usually an object)\n", 114 | "- Providing **units** for numbers when appropriate\n", 115 | "\n", 116 | "
" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "## Scientific Programming\n", 124 | "\n", 125 | "
- \n",
132 | "
- Programming whose goal is for scientific usage (e.g., workflows, data analysis) and visualization.\n", 133 | "
- Learning to program in an academic, scholarly manner (i.e., wissenschaftliches Arbeit).\n", 134 | "
- \n",
138 | "
- Usage: encoding ideas into mathematical models (from simple numerical computations to AI)\n",
139 | "
\n", 140 | " - Practice: to create code and knowledge while maintaining good scholarship\n",
141 | "
- \n",
142 | "
- Knowing what is the state-of-the-art\n", 143 | "
- Being careful, clear and supportive\n", 144 | "
- \"A machine requires precise instructions, which users often fail to supply\" [1]\n", 145 | "
\n", 147 | " - Target: to support science (doing research, data support and analysis)\n",
148 | "
- \n",
149 | "
- \"Scientists ... use ... Python ... to conduct and automate analyses ... speed data crunching, increase reproducibility ... and handle data sets that would overwhelm commercial applications.\" [1]\n", 150 | "
- Create workflows to help do the research\n", 151 | "
- Create simulations (very important in research)\n",
152 | "
- \n",
153 | "
- exploratory: for understanding raw data\n", 154 | "
- supportive: for strengthening interpretations of the data\n", 155 | "
- predictive: creating new ideas\n", 156 | "
" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "
\n",
171 | "\n",
172 | "## Why is this Important?\n",
173 | "\n",
174 | "- \"Societally important science relies on models and the software implementing them. The scientific community must ensure that the findings and recommendations put forth based on those models conform to the highest scientific expectation\" [2]\n",
175 | "\n",
176 | "[2] L. N. Joppa et al., “Troubling Trends in Scientific Software Use,” Science, 340(6134), 814–815, 2013."
177 | ]
178 | },
179 | {
180 | "cell_type": "markdown",
181 | "metadata": {},
182 | "source": [
183 | "Positive Example:\n",
184 | "- During the Covid pandemic (March 20th, 2021), 9 of the 10 ChemRxiv (preprint server) highlights had significant computer modeling.\n",
185 | " - Meaning: computer models were the first to provide new knowledge that targeted the Coronavirus.\n",
186 | "\n",
187 | "![](\"00_images/ChemRxiv_coronavirus_2020.png\"/)
![](\"00_images/ChangRRPCC2006.png\")
\n", 203 | "\n", 204 | "## Why Python?\n", 205 | "\n", 206 | "Python is increasingly regarded as an important programming language.\n", 207 | "\n", 208 | "\n", 209 | "Due to its ease of use, extensive libraries and readability, scientific researchers have made it their \"go-to\" programming language. For example researchers use Python to create customized workflow for simulations (i.e., modeling); to analyze data (e.g., statistics); to create professional data plots (i.e., visualization); and to enable data and workflows reproducibility\n", 210 | "\n", 211 | "\n", 212 | "- Accessible and human readable: especially to people outside of computer science (natural scientists; engineers)\n", 213 | "- Powerful: many libraries that are created by domain experts\n", 214 | "- Modular: good for creating larger programs by creating user-defined functions that do (i.e., isolate) specific tasks\n", 215 | "\n", 216 | "\n", 217 | "Most popular programming languages (via reference [3])\n", 218 | "1. JavaScript\n", 219 | "2. Python\n", 220 | "3. Java\n", 221 | "4. PHP\n", 222 | "5. C#\n", 223 | "6. TypeScript\n", 224 | "6. CSS\n", 225 | "8. C++\n", 226 | "9. Ruby\n", 227 | "\n", 228 | "![](\"00_images/redmonk_2024.png\")
\n", 233 | "\n", 234 | "## The Disciplines that use Python - Its \"Main\" Catagories\n", 235 | "\n", 236 | "For a Python job, one should know a) Python's core and b) one of the following categories:\n", 237 | "\n", 238 | "- Web - flask, django, html, javascript\n", 239 | "- Data engineering (collecting data) - sql, airflow, luigi\n", 240 | "- Software engineering - git, unit testing, large codebases\n", 241 | "- Cyber security - requests, volatility, pyew, peepdf, penetration tests\n", 242 | "- **Data science / scientific python** (seeking new knowledge)\n", 243 | "\n", 244 | "## What are the most important libraries to know?\n", 245 | "\n", 246 | "### Top libraries\n", 247 | "- 150 GitHub computational chemistry + machine learning (i.e., natural scientists who know about coding) repositories\n", 248 | "\n", 249 | "1. Numpy\n", 250 | "2. Pandas\n", 251 | "3. PyTorch\n", 252 | "4. sklearn\n", 253 | "5. Matplotlib\n", 254 | "6. SciPy\n", 255 | "7. TensorFlow\n", 256 | "\n", 257 | "![](\"00_images/top_compchem_libraries.png\")
" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "## Getting Python\n", 267 | "\n", 268 | "
" 287 | ] 288 | }, 289 | { 290 | "cell_type": "markdown", 291 | "metadata": {}, 292 | "source": [ 293 | "## Python Environments\n", 294 | "\n", 295 | "### Write and Execute a Code\n", 296 | "
" 334 | ] 335 | }, 336 | { 337 | "cell_type": "markdown", 338 | "metadata": {}, 339 | "source": [ 340 | "## Important Course Information\n", 341 | "
" 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "metadata": {}, 355 | "source": [ 356 | "\n", 357 | "# Python Easter Eggs" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "- Experience Antigravity" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": null, 370 | "metadata": {}, 371 | "outputs": [], 372 | "source": [ 373 | "import antigravity" 374 | ] 375 | } 376 | ], 377 | "metadata": { 378 | "kernelspec": { 379 | "display_name": "Python 3 (ipykernel)", 380 | "language": "python", 381 | "name": "python3" 382 | }, 383 | "language_info": { 384 | "codemirror_mode": { 385 | "name": "ipython", 386 | "version": 3 387 | }, 388 | "file_extension": ".py", 389 | "mimetype": "text/x-python", 390 | "name": "python", 391 | "nbconvert_exporter": "python", 392 | "pygments_lexer": "ipython3", 393 | "version": "3.13.2" 394 | } 395 | }, 396 | "nbformat": 4, 397 | "nbformat_minor": 4 398 | } 399 | -------------------------------------------------------------------------------- /jupyter_notebooks_intro.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Jupyter Notebooks\n", 8 | "\n", 9 | "### Cells\n", 10 | "\n", 11 | "When you are editing a cell in Jupyter notebook, you need to re-run the cell by pressing **` + `**. This will allow changes you made to be available to other cells.\n",
12 | "\n",
13 | "### Common Jupyter operations\n",
14 | "\n",
15 | "#### Inserting and removing cells\n",
16 | "\n",
17 | "- Use the \"plus sign\" icon to insert a cell below the currently selected cell\n",
18 | "- Use \"Insert\" -> \"Insert Cell Above\" from the menu to insert above\n",
19 | "\n",
20 | "#### Clear the output of all cells\n",
21 | "\n",
22 | "- Use \"Kernel\" -> \"Restart\" from the menu to restart the kernel\n",
23 | " - A kernel is a \"computational engine\" (a fundamental program) that executes a code that you write\n",
24 | " - click on \"clear all outputs & restart\" to have all the output cleared\n",
25 | "\n",
26 | "#### Save your notebook file locally\n",
27 | "\n",
28 | "- Clear the output of all cells\n",
29 | "- Use \"File\" -> \"Download as\" -> \"IPython Notebook (.ipynb)\"\n",
30 | "***"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": null,
36 | "metadata": {},
37 | "outputs": [],
38 | "source": []
39 | }
40 | ],
41 | "metadata": {
42 | "kernelspec": {
43 | "display_name": "Python 3 (ipykernel)",
44 | "language": "python",
45 | "name": "python3"
46 | },
47 | "language_info": {
48 | "codemirror_mode": {
49 | "name": "ipython",
50 | "version": 3
51 | },
52 | "file_extension": ".py",
53 | "mimetype": "text/x-python",
54 | "name": "python",
55 | "nbconvert_exporter": "python",
56 | "pygments_lexer": "ipython3",
57 | "version": "3.9.7"
58 | }
59 | },
60 | "nbformat": 4,
61 | "nbformat_minor": 2
62 | }
63 |
--------------------------------------------------------------------------------
/mid_semester_review.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "
\n", 203 | "\n", 204 | "## Why Python?\n", 205 | "\n", 206 | "Python is increasingly regarded as an important programming language.\n", 207 | "\n", 208 | "\n", 209 | "Due to its ease of use, extensive libraries and readability, scientific researchers have made it their \"go-to\" programming language. For example researchers use Python to create customized workflow for simulations (i.e., modeling); to analyze data (e.g., statistics); to create professional data plots (i.e., visualization); and to enable data and workflows reproducibility\n", 210 | "\n", 211 | "\n", 212 | "- Accessible and human readable: especially to people outside of computer science (natural scientists; engineers)\n", 213 | "- Powerful: many libraries that are created by domain experts\n", 214 | "- Modular: good for creating larger programs by creating user-defined functions that do (i.e., isolate) specific tasks\n", 215 | "\n", 216 | "\n", 217 | "Most popular programming languages (via reference [3])\n", 218 | "1. JavaScript\n", 219 | "2. Python\n", 220 | "3. Java\n", 221 | "4. PHP\n", 222 | "5. C#\n", 223 | "6. TypeScript\n", 224 | "6. CSS\n", 225 | "8. C++\n", 226 | "9. Ruby\n", 227 | "\n", 228 | "
\n", 233 | "\n", 234 | "## The Disciplines that use Python - Its \"Main\" Catagories\n", 235 | "\n", 236 | "For a Python job, one should know a) Python's core and b) one of the following categories:\n", 237 | "\n", 238 | "- Web - flask, django, html, javascript\n", 239 | "- Data engineering (collecting data) - sql, airflow, luigi\n", 240 | "- Software engineering - git, unit testing, large codebases\n", 241 | "- Cyber security - requests, volatility, pyew, peepdf, penetration tests\n", 242 | "- **Data science / scientific python** (seeking new knowledge)\n", 243 | "\n", 244 | "## What are the most important libraries to know?\n", 245 | "\n", 246 | "### Top libraries\n", 247 | "- 150 GitHub computational chemistry + machine learning (i.e., natural scientists who know about coding) repositories\n", 248 | "\n", 249 | "1. Numpy\n", 250 | "2. Pandas\n", 251 | "3. PyTorch\n", 252 | "4. sklearn\n", 253 | "5. Matplotlib\n", 254 | "6. SciPy\n", 255 | "7. TensorFlow\n", 256 | "\n", 257 | "
" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "## Getting Python\n", 267 | "\n", 268 | "
- \n",
269 | "
- Having Python (and desired libraries) installed onto your computer\n",
270 | "
- \n",
271 | "
- It may already be installed\n", 272 | "
- If not: https://www.python.org/downloads \n", 273 | "
\n", 276 | " - Miniconda (Conda) (https://docs.conda.io/en/latest/miniconda.html)\n",
277 | "
- \n",
278 | "
- manage isolated Python environments and libraries\n", 279 | "
- mamba (https://mamba.readthedocs.io)\n",
281 | "
- \n",
282 | "
- replaces conda\n", 283 | "
- generally faster and resolves dependencies better\n", 284 | "
" 287 | ] 288 | }, 289 | { 290 | "cell_type": "markdown", 291 | "metadata": {}, 292 | "source": [ 293 | "## Python Environments\n", 294 | "\n", 295 | "### Write and Execute a Code\n", 296 | "
- \n",
297 | "
- Google's Colaboratory: Colab\n",
298 | "
- \n",
299 | "
- Writing in a browser and online\n", 300 | "
- Execute: directly and online\n", 301 | "
\n", 304 | " - Jupyter Notebooks: Jupyter - Recommended for novice and experienced programmers\n",
305 | "
- \n",
306 | "
- Writing online and offline\n", 307 | "
- Execute: directly and offline\n", 308 | "
\n", 311 | " - Text Editor and Integrated Development Environment (IDE)\n",
312 | "
- \n",
313 | "
- Written using simple editors (e.g., texteditor, gedit, sublime)\n", 314 | "
- Written using IDE (Integrated Development Environment)\n",
315 | "
- \n",
316 | "
- IDLE\n", 317 | "
- PyCharm\n", 318 | "
- Visual Studio Code\n", 319 | "
\n",
321 | "
\n", 324 | "\n", 332 | "
" 334 | ] 335 | }, 336 | { 337 | "cell_type": "markdown", 338 | "metadata": {}, 339 | "source": [ 340 | "## Important Course Information\n", 341 | "
- \n",
342 | "
- Lectures will be ca. 90 minutes long\n", 343 | "
- We will make use of LEA\n", 344 | "
- Syllabus - much of the important course info (on LEA)\n", 345 | "
- I am available through university email (I tend to respond to these fairly quickly)\n", 346 | "
- Individual/group in-person and online meetings can be made upon request\n", 347 | "
" 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "metadata": {}, 355 | "source": [ 356 | "\n", 357 | "# Python Easter Eggs" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "- Experience Antigravity" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": null, 370 | "metadata": {}, 371 | "outputs": [], 372 | "source": [ 373 | "import antigravity" 374 | ] 375 | } 376 | ], 377 | "metadata": { 378 | "kernelspec": { 379 | "display_name": "Python 3 (ipykernel)", 380 | "language": "python", 381 | "name": "python3" 382 | }, 383 | "language_info": { 384 | "codemirror_mode": { 385 | "name": "ipython", 386 | "version": 3 387 | }, 388 | "file_extension": ".py", 389 | "mimetype": "text/x-python", 390 | "name": "python", 391 | "nbconvert_exporter": "python", 392 | "pygments_lexer": "ipython3", 393 | "version": "3.13.2" 394 | } 395 | }, 396 | "nbformat": 4, 397 | "nbformat_minor": 4 398 | } 399 | -------------------------------------------------------------------------------- /jupyter_notebooks_intro.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Jupyter Notebooks\n", 8 | "\n", 9 | "### Cells\n", 10 | "\n", 11 | "When you are editing a cell in Jupyter notebook, you need to re-run the cell by pressing **`
\n",
8 | "\n",
9 | "# Scientific Programming in Python\n",
10 | "\n",
11 | "## Karl N. Kirschner
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 12 | "\n", 13 | "# ca. Mid Semseter Review\n",
14 | "### (plus a few new ideas)\n",
15 | "\n",
16 | "
" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "## Scientific Programming\n", 24 | "\n", 25 | "**Definition**\n", 26 | "1. Programming whose goal is for scientific usage (e.g. workflows, data analysis) and visualization.\n", 27 | "\n", 28 | "2. Learning to program is an academic, scholarly manner (i.e. wissenschaftliches Arbeit).\n", 29 | " - minimize errors\n", 30 | " - enable others to expand upon your ideas\n", 31 | " - reproducible results\n", 32 | " - convey knowledge (e.g. ideas are placed into context and explained)\n", 33 | " - being precise and explicit in what is done\n", 34 | " - following established standards within computer science (e.g. PEP8)\n", 35 | "\n", 36 | "**3 Ways to Think About It**\n", 37 | "1. **Usage**: to perform mathematics (simple numerical computations to some complex math models)\n", 38 | "\n", 39 | "2. **Practice**: to create while maintaining good scholarship\n", 40 | " - knowing what is state-of-the-art\n", 41 | " - being clear and supportive (e.g. citing sources)\n", 42 | " - write code in concise, explicit manner (i.e. do something with understanding and intention)\n", 43 | " - write code in a way that minimizes the chances of introducing an error\n", 44 | " - \"A machine requires precise instructions, which users often fail to supply\"[1]\n", 45 | "\n", 46 | "3. **Target**: to support science (doing research, data support and analysis)\n", 47 | " - \"Scientists commonly use languages such as Python and R to conduct and **automate analyses**, because in this way they can **speed data crunching**, **increase reproducibility**, protect data from accidental deletion or alteration and handle data sets that would overwhelm commercial applications.\"[1]\n", 48 | " - Create workflows to help do the research\n", 49 | " - Create simulations (increasingly becoming more important in research)\n", 50 | " - exploratory: for understanding raw data\n", 51 | " - supportive: for strengthening interpretations of the data\n", 52 | " - predictive: creating new ideas\n", 53 | " \n", 54 | "[1] Baker, Monya. \"Scientific computing: code alert.\" Nature 541, no. 7638 (2017): 563-565.\n", 55 | "\n", 56 | "
\n", 57 | "\n", 58 | "#### Conceptual highlights about the course\n", 59 | "1. Significant amount of personal feedback\n", 60 | "\n", 61 | "2. Emphasis on\n", 62 | " - **K**eep **I**t (i.e. coding) **S**imple & **S**mart (K.I.S.S.)\n", 63 | " - $C^3$: code is **clear** (i.e. clarity), **concise**, and place into **context**\n", 64 | " - easily readable and understandable\n", 65 | " - use of built-in functions over libraries with large overhead\n", 66 | " - user functions for reproducibility, reuse, and error reduction" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "#### Lots of built-in keywords and commands" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "**Built-in Functions**\n", 81 | "https://docs.python.org/3/library/functions.html\n", 82 | "\n", 83 | "`abs()\t\t\tdivmod()\t\tinput()\t\t open()\t staticmethod()`
\n", 84 | "`all()\t\t\tenumerate()\t int()\t\t ord()\t str()`
\n", 85 | "`any()\t\t\teval()\t\t isinstance()\tpow()\t sum()`
\n", 86 | "`basestring()\t execfile() \tissubclass()\tprint() super()`
\n", 87 | "`bin()\t\t file()\t\t iter()\t\t property() tuple()`
\n", 88 | "`bool()\t\t filter()\t len()\t\t range()\t type()`
\n", 89 | "`bytearray()\t float()\t list()\t\t raw_input() unichr()`
\n", 90 | "`callable()\t format()\t\tlocals()\t\treduce() unicode()`
\n", 91 | "`chr()\t\t\tfrozenset()\t long()\t\t reload() vars()`
\n", 92 | "`classmethod()\tgetattr()\t map()\t\t repr()\t\txrange()`
\n", 93 | "`cmp()\t\t\tglobals()\t max()\t\t reversed()\tzip()`
\n", 94 | "`compile()\t\thasattr()\t memoryview() round()\t __import__()`
\n", 95 | "`complex()\t\thash()\t\t min()\t\t set()`
\n", 96 | "`delattr()\t\thelp()\t\t next()\t setattr()`
\n", 97 | "`dict()\t\t hex()\t\t object()\t slice()`
\n", 98 | "`dir()\t\t\tid()\t\t oct()\t sorted()`
\n", 99 | "***" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "import keyword\n", 109 | "keyword.kwlist" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "#### A comprehensive list of built-in functions and modules\n", 117 | "\n", 118 | "Functions:" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "dir(__builtins__)" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "Modules:" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "help('modules')" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "
\n", 151 | "\n", 152 | "### What we have learned about so far\n", 153 | "\n", 154 | "1. User functions - creating one code block that focuses on a single idea/concept\n", 155 | " - typing - adds **clarity** to the code\n", 156 | " - **context** through good block comments, and\n", 157 | " - some internal code control ideas (e.g. isinstance, checking for None default assignments)\n", 158 | "\n", 159 | "\n", 160 | "2. Writing **concise** code without losing clarity (i.e. not making it confusing)\n", 161 | " - easier to understand\n", 162 | " - easier to debug\n", 163 | " - less prone to introducing errors\n", 164 | "\n", 165 | "\n", 166 | "3. Significant Figures and Rounding Numbers\n", 167 | "\n", 168 | "\n", 169 | "4. PEP8 styles recommendations\n", 170 | " - why it is important\n", 171 | " - order for importing libraries\n", 172 | " - indentation, spacing and line length\n", 173 | "\n", 174 | "\n", 175 | "5. Pandas library\n", 176 | " - reading in csv files (series and dataframes)\n", 177 | " - Pandas built in math functions\n", 178 | " - plotting data\n", 179 | "\n", 180 | "
" 181 | ] 182 | }, 183 | { 184 | "cell_type": "markdown", 185 | "metadata": {}, 186 | "source": [ 187 | "#### Data Structures Review\n", 188 | "\n", 189 | "- Lists\n", 190 | "- Dictionaries\n", 191 | "- Tuples\n", 192 | "\n", 193 | "**lists**" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "metadata": {}, 200 | "outputs": [], 201 | "source": [ 202 | "my_list = ['Christmas', 'Halloween', 'German Unity Day', \"New Year's Day\", \"Christmas\"]\n", 203 | "type(my_list)" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "for holiday in my_list:\n", 213 | " print(holiday)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": null, 219 | "metadata": {}, 220 | "outputs": [], 221 | "source": [ 222 | "my_list" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": null, 228 | "metadata": {}, 229 | "outputs": [], 230 | "source": [ 231 | "my_list[0] = 'Carnival'\n", 232 | "my_list" 233 | ] 234 | }, 235 | { 236 | "cell_type": "markdown", 237 | "metadata": {}, 238 | "source": [ 239 | "**tuples**\n", 240 | "- faster than lists, bacause they are\n", 241 | "- not mutable (i.e. immutable)" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "my_tuple = ('Christmas', 'Halloween', 'German Unity Day', \"New Year's Day\", \"Christmas\")\n", 251 | "type(my_tuple)" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": null, 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "for holiday in my_tuple:\n", 261 | " print(holiday)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": null, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [ 270 | "my_tuple[0] = 'Carnival'\n", 271 | "my_tuple" 272 | ] 273 | }, 274 | { 275 | "cell_type": "markdown", 276 | "metadata": {}, 277 | "source": [ 278 | "**dictionary**" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": null, 284 | "metadata": {}, 285 | "outputs": [], 286 | "source": [ 287 | "my_dictionary = {'German Holidays': ['Christmas', 'Halloween', 'German Unity Day', \"New Year's Day\", \"Christmas\"],\n", 288 | " 'US Holidays': ['Christmas', 'Halloween', 'Thanksgiving', \"New Year's Day\", \"Christmas\"]}\n", 289 | "my_dictionary" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": null, 295 | "metadata": {}, 296 | "outputs": [], 297 | "source": [ 298 | "type(my_dictionary)" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "execution_count": null, 304 | "metadata": {}, 305 | "outputs": [], 306 | "source": [ 307 | "my_dictionary.get('US Holidays')" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": null, 313 | "metadata": {}, 314 | "outputs": [], 315 | "source": [ 316 | "list(my_dictionary.keys())" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": null, 322 | "metadata": {}, 323 | "outputs": [], 324 | "source": [ 325 | "list(my_dictionary.values())" 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": null, 331 | "metadata": {}, 332 | "outputs": [], 333 | "source": [ 334 | "list(my_dictionary.items())" 335 | ] 336 | }, 337 | { 338 | "cell_type": "code", 339 | "execution_count": null, 340 | "metadata": {}, 341 | "outputs": [], 342 | "source": [ 343 | "for key, value in my_dictionary.items():\n", 344 | " print(key, value)" 345 | ] 346 | }, 347 | { 348 | "cell_type": "markdown", 349 | "metadata": {}, 350 | "source": [ 351 | "#### Identify unique entries\n", 352 | "\n", 353 | "- sets\n", 354 | " - mutable\n", 355 | "- frozen sets\n", 356 | " - immutable" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": null, 362 | "metadata": {}, 363 | "outputs": [], 364 | "source": [ 365 | "my_set = set(my_list)\n", 366 | "my_set" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": null, 372 | "metadata": {}, 373 | "outputs": [], 374 | "source": [ 375 | "type(my_set)" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "execution_count": null, 381 | "metadata": {}, 382 | "outputs": [], 383 | "source": [ 384 | "for holiday in my_set:\n", 385 | " print(holiday)" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": {}, 392 | "outputs": [], 393 | "source": [ 394 | "my_set.remove(\"New Year's Day\")\n", 395 | "my_set" 396 | ] 397 | }, 398 | { 399 | "cell_type": "markdown", 400 | "metadata": {}, 401 | "source": [ 402 | "**frozen set** (a small new idea that we haven't seen before)" 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "execution_count": null, 408 | "metadata": {}, 409 | "outputs": [], 410 | "source": [ 411 | "my_frozenset = frozenset(my_list)\n", 412 | "my_frozenset" 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": null, 418 | "metadata": {}, 419 | "outputs": [], 420 | "source": [ 421 | "type(my_frozenset)" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": null, 427 | "metadata": {}, 428 | "outputs": [], 429 | "source": [ 430 | "my_frozenset.remove(\"Christmas\")" 431 | ] 432 | } 433 | ], 434 | "metadata": { 435 | "kernelspec": { 436 | "display_name": "Python 3 (ipykernel)", 437 | "language": "python", 438 | "name": "python3" 439 | }, 440 | "language_info": { 441 | "codemirror_mode": { 442 | "name": "ipython", 443 | "version": 3 444 | }, 445 | "file_extension": ".py", 446 | "mimetype": "text/x-python", 447 | "name": "python", 448 | "nbconvert_exporter": "python", 449 | "pygments_lexer": "ipython3", 450 | "version": "3.8.10" 451 | } 452 | }, 453 | "nbformat": 4, 454 | "nbformat_minor": 2 455 | } 456 | -------------------------------------------------------------------------------- /ml_introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Machine Learning (ML)\n", 8 | "\n", 9 | "**\"Can machines think [in the way that we do]?\"** [1]\n", 10 | "\n", 11 | "\n", 12 | "- The ML term was first used in 1959 by Arthur Samuel (an IBM researcher)\n", 13 | "\n", 14 | "\n", 15 | "- Mathematical Foundation\n", 16 | " - Statistics (the \"work-horse\" of ML)\n", 17 | " - Calculus (derivatives; optimizations)\n", 18 | " - Algerbra (vectors, matrix, tensors)\n", 19 | "\n", 20 | "\n", 21 | "- Different components were developed by researchers for many years. Only recently they were collected into libraries that make the ideas more accessible.\n", 22 | "\n", 23 | "## Machine Learning Catagories\n", 24 | "\n", 25 | "1. Shallow learning (e.g. **s**ci**k**it-**learn** - a.k.a. **sklearn**)\n", 26 | " - predefined features\n", 27 | "\n", 28 | "1. Deep learning (e.g. TensorFlow, PyTorch)\n", 29 | " - feature learning\n", 30 | " - mostly combines shallow learning instances together into \"layers\"\n", 31 | "\n", 32 | "\n", 33 | "**Sources**:\n", 34 | "1. Turing, Alan M. \"Computing machinery and intelligence.\" Parsing the Turing test. Springer, Dordrecht, 2009. 23-65.\n", 35 | "\n", 36 | "**Additional Resources**:\n", 37 | "1. https://en.wikipedia.org/wiki/Machine_learning" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "
\n", 45 | "\n", 46 | "# Shallow Learning\n", 47 | "\n", 48 | "## Catagories\n", 49 | "\n", 50 | "| Regression | Classification | Clustering | Dimension Reduction|\n", 51 | "| :-: | :-: | :-: | :-: |\n", 52 | "| Linear | Logistic Regression | K-means | Principle Component Analysis |\n", 53 | "| Polynomial | Support Vector Machine | Mean-Shift | Linear Discriminant Analysis |\n", 54 | "| StepWise | Naive Bayes | DBScan | Gernalized Discriminant Analysis |\n", 55 | "| Ridge | Nearest Neighbor | Agglomerative Hierachcial | Autoencoder |\n", 56 | "| Lasso | Decision Tree | Spectral Clustering | Non-Negative Matrix Factorization |\n", 57 | "| ElasticNet | Random Forest | Gaussian Mixture | UMAP |" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "## Supervised vs. Unsupervised Learning\n", 65 | "\n", 66 | "1. **Supervised** - the **target information is known** in the data set, and we **train to reproduce** that information\n", 67 | " - regression\n", 68 | " - classification\n", 69 | "\n", 70 | "1. **Unsupervised** - the **target information is unknown**, with the goal to \n", 71 | " - cluster the data's similarity (clustering)\n", 72 | " - determine the distribution of data (density estimation)\n", 73 | " - dimensionality reduction for exploring and visualization" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "
\n", 90 | "\n", 91 | "# Deep Learning - Unsupervised Learning\n", 92 | "\n", 93 | "\n", 94 | "#### Neural network\n", 95 | "- **Input Layer**: features (observables) should have some degree of correlation (i.e., structure; nonlinear relationships)\n", 96 | "- Encoder: input $\\rightarrow$ hidden layers (data reduction)\n", 97 | "- **Hidden Layer**: a compressed knowledge representation of the original input\n", 98 | "- Decoder: hidden layers $\\rightarrow$ output\n", 99 | "- **Output Layer**" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "metadata": {}, 105 | "source": [ 106 | "
\n", 126 | "\n", 127 | "- Denoising Autoencoder\n", 128 | " - slightly **corrupt** the **input data** (i.e., add noise) to help make the encoding/decoding more generalizable\n", 129 | " - **target data** remains **uncorrupted**\n", 130 | " - make the decoding (reconstruction function) insensitive to small changes in the input\n", 131 | "\n", 132 | "
\n", 133 | "\n", 134 | "- Contractive Autoencoder\n", 135 | " - make the **encoding** (feature extraction function) **less sensitive** to **small changes** within the **input data**\n", 136 | " - learn similar encoding (hidden layer) for different inputs that vary slightly\n", 137 | "\n", 138 | "
\n", 139 | "\n", 140 | "- Variational Autoencoder (VAE)\n", 141 | " - https://arxiv.org/abs/1606.05908\n", 142 | " - training using **backpropagation** (aka **backward propagation of error**)\n", 143 | " - backpropagation - https://www.ibm.com/think/topics/backpropagation\n", 144 | " - starting from an **output**, compute the **importance** (measured as a gradient) that each neural network **parameter** has on the final model's **error** (predicted values) (i.e., loss function)\n", 145 | " - encoding is **regularized** (adding a penalty term to the model's loss function during the learning process) to ensure that the latent space has good properties (and thus, allowing us to have generative models to be created)\n", 146 | " - regularization - https://en.wikipedia.org/wiki/Regularization_(mathematics)\n", 147 | "\n", 148 | "\n", 149 | "\n", 150 | "" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": null, 157 | "metadata": {}, 158 | "outputs": [], 159 | "source": [] 160 | } 161 | ], 162 | "metadata": { 163 | "kernelspec": { 164 | "display_name": "Python 3 (ipykernel)", 165 | "language": "python", 166 | "name": "python3" 167 | }, 168 | "language_info": { 169 | "codemirror_mode": { 170 | "name": "ipython", 171 | "version": 3 172 | }, 173 | "file_extension": ".py", 174 | "mimetype": "text/x-python", 175 | "name": "python", 176 | "nbconvert_exporter": "python", 177 | "pygments_lexer": "ipython3", 178 | "version": "3.9.20" 179 | } 180 | }, 181 | "nbformat": 4, 182 | "nbformat_minor": 4 183 | } 184 | -------------------------------------------------------------------------------- /numpy_polynomials.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "1f0ce628", 6 | "metadata": {}, 7 | "source": [ 8 | "
Bonn-Rhein-Sieg University of Applied Sciences
Sankt Augustin, Germany\n", 12 | "\n", 13 | "#
" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "## Scientific Programming\n", 24 | "\n", 25 | "**Definition**\n", 26 | "1. Programming whose goal is for scientific usage (e.g. workflows, data analysis) and visualization.\n", 27 | "\n", 28 | "2. Learning to program is an academic, scholarly manner (i.e. wissenschaftliches Arbeit).\n", 29 | " - minimize errors\n", 30 | " - enable others to expand upon your ideas\n", 31 | " - reproducible results\n", 32 | " - convey knowledge (e.g. ideas are placed into context and explained)\n", 33 | " - being precise and explicit in what is done\n", 34 | " - following established standards within computer science (e.g. PEP8)\n", 35 | "\n", 36 | "**3 Ways to Think About It**\n", 37 | "1. **Usage**: to perform mathematics (simple numerical computations to some complex math models)\n", 38 | "\n", 39 | "2. **Practice**: to create while maintaining good scholarship\n", 40 | " - knowing what is state-of-the-art\n", 41 | " - being clear and supportive (e.g. citing sources)\n", 42 | " - write code in concise, explicit manner (i.e. do something with understanding and intention)\n", 43 | " - write code in a way that minimizes the chances of introducing an error\n", 44 | " - \"A machine requires precise instructions, which users often fail to supply\"[1]\n", 45 | "\n", 46 | "3. **Target**: to support science (doing research, data support and analysis)\n", 47 | " - \"Scientists commonly use languages such as Python and R to conduct and **automate analyses**, because in this way they can **speed data crunching**, **increase reproducibility**, protect data from accidental deletion or alteration and handle data sets that would overwhelm commercial applications.\"[1]\n", 48 | " - Create workflows to help do the research\n", 49 | " - Create simulations (increasingly becoming more important in research)\n", 50 | " - exploratory: for understanding raw data\n", 51 | " - supportive: for strengthening interpretations of the data\n", 52 | " - predictive: creating new ideas\n", 53 | " \n", 54 | "[1] Baker, Monya. \"Scientific computing: code alert.\" Nature 541, no. 7638 (2017): 563-565.\n", 55 | "\n", 56 | "
\n", 57 | "\n", 58 | "#### Conceptual highlights about the course\n", 59 | "1. Significant amount of personal feedback\n", 60 | "\n", 61 | "2. Emphasis on\n", 62 | " - **K**eep **I**t (i.e. coding) **S**imple & **S**mart (K.I.S.S.)\n", 63 | " - $C^3$: code is **clear** (i.e. clarity), **concise**, and place into **context**\n", 64 | " - easily readable and understandable\n", 65 | " - use of built-in functions over libraries with large overhead\n", 66 | " - user functions for reproducibility, reuse, and error reduction" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "#### Lots of built-in keywords and commands" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "**Built-in Functions**\n", 81 | "https://docs.python.org/3/library/functions.html\n", 82 | "\n", 83 | "`abs()\t\t\tdivmod()\t\tinput()\t\t open()\t staticmethod()`
\n", 84 | "`all()\t\t\tenumerate()\t int()\t\t ord()\t str()`
\n", 85 | "`any()\t\t\teval()\t\t isinstance()\tpow()\t sum()`
\n", 86 | "`basestring()\t execfile() \tissubclass()\tprint() super()`
\n", 87 | "`bin()\t\t file()\t\t iter()\t\t property() tuple()`
\n", 88 | "`bool()\t\t filter()\t len()\t\t range()\t type()`
\n", 89 | "`bytearray()\t float()\t list()\t\t raw_input() unichr()`
\n", 90 | "`callable()\t format()\t\tlocals()\t\treduce() unicode()`
\n", 91 | "`chr()\t\t\tfrozenset()\t long()\t\t reload() vars()`
\n", 92 | "`classmethod()\tgetattr()\t map()\t\t repr()\t\txrange()`
\n", 93 | "`cmp()\t\t\tglobals()\t max()\t\t reversed()\tzip()`
\n", 94 | "`compile()\t\thasattr()\t memoryview() round()\t __import__()`
\n", 95 | "`complex()\t\thash()\t\t min()\t\t set()`
\n", 96 | "`delattr()\t\thelp()\t\t next()\t setattr()`
\n", 97 | "`dict()\t\t hex()\t\t object()\t slice()`
\n", 98 | "`dir()\t\t\tid()\t\t oct()\t sorted()`
\n", 99 | "***" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "import keyword\n", 109 | "keyword.kwlist" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "#### A comprehensive list of built-in functions and modules\n", 117 | "\n", 118 | "Functions:" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "dir(__builtins__)" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "Modules:" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "help('modules')" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "
\n", 151 | "\n", 152 | "### What we have learned about so far\n", 153 | "\n", 154 | "1. User functions - creating one code block that focuses on a single idea/concept\n", 155 | " - typing - adds **clarity** to the code\n", 156 | " - **context** through good block comments, and\n", 157 | " - some internal code control ideas (e.g. isinstance, checking for None default assignments)\n", 158 | "\n", 159 | "\n", 160 | "2. Writing **concise** code without losing clarity (i.e. not making it confusing)\n", 161 | " - easier to understand\n", 162 | " - easier to debug\n", 163 | " - less prone to introducing errors\n", 164 | "\n", 165 | "\n", 166 | "3. Significant Figures and Rounding Numbers\n", 167 | "\n", 168 | "\n", 169 | "4. PEP8 styles recommendations\n", 170 | " - why it is important\n", 171 | " - order for importing libraries\n", 172 | " - indentation, spacing and line length\n", 173 | "\n", 174 | "\n", 175 | "5. Pandas library\n", 176 | " - reading in csv files (series and dataframes)\n", 177 | " - Pandas built in math functions\n", 178 | " - plotting data\n", 179 | "\n", 180 | "
" 181 | ] 182 | }, 183 | { 184 | "cell_type": "markdown", 185 | "metadata": {}, 186 | "source": [ 187 | "#### Data Structures Review\n", 188 | "\n", 189 | "- Lists\n", 190 | "- Dictionaries\n", 191 | "- Tuples\n", 192 | "\n", 193 | "**lists**" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "metadata": {}, 200 | "outputs": [], 201 | "source": [ 202 | "my_list = ['Christmas', 'Halloween', 'German Unity Day', \"New Year's Day\", \"Christmas\"]\n", 203 | "type(my_list)" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "for holiday in my_list:\n", 213 | " print(holiday)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": null, 219 | "metadata": {}, 220 | "outputs": [], 221 | "source": [ 222 | "my_list" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": null, 228 | "metadata": {}, 229 | "outputs": [], 230 | "source": [ 231 | "my_list[0] = 'Carnival'\n", 232 | "my_list" 233 | ] 234 | }, 235 | { 236 | "cell_type": "markdown", 237 | "metadata": {}, 238 | "source": [ 239 | "**tuples**\n", 240 | "- faster than lists, bacause they are\n", 241 | "- not mutable (i.e. immutable)" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "my_tuple = ('Christmas', 'Halloween', 'German Unity Day', \"New Year's Day\", \"Christmas\")\n", 251 | "type(my_tuple)" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": null, 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "for holiday in my_tuple:\n", 261 | " print(holiday)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": null, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [ 270 | "my_tuple[0] = 'Carnival'\n", 271 | "my_tuple" 272 | ] 273 | }, 274 | { 275 | "cell_type": "markdown", 276 | "metadata": {}, 277 | "source": [ 278 | "**dictionary**" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": null, 284 | "metadata": {}, 285 | "outputs": [], 286 | "source": [ 287 | "my_dictionary = {'German Holidays': ['Christmas', 'Halloween', 'German Unity Day', \"New Year's Day\", \"Christmas\"],\n", 288 | " 'US Holidays': ['Christmas', 'Halloween', 'Thanksgiving', \"New Year's Day\", \"Christmas\"]}\n", 289 | "my_dictionary" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": null, 295 | "metadata": {}, 296 | "outputs": [], 297 | "source": [ 298 | "type(my_dictionary)" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "execution_count": null, 304 | "metadata": {}, 305 | "outputs": [], 306 | "source": [ 307 | "my_dictionary.get('US Holidays')" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": null, 313 | "metadata": {}, 314 | "outputs": [], 315 | "source": [ 316 | "list(my_dictionary.keys())" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": null, 322 | "metadata": {}, 323 | "outputs": [], 324 | "source": [ 325 | "list(my_dictionary.values())" 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": null, 331 | "metadata": {}, 332 | "outputs": [], 333 | "source": [ 334 | "list(my_dictionary.items())" 335 | ] 336 | }, 337 | { 338 | "cell_type": "code", 339 | "execution_count": null, 340 | "metadata": {}, 341 | "outputs": [], 342 | "source": [ 343 | "for key, value in my_dictionary.items():\n", 344 | " print(key, value)" 345 | ] 346 | }, 347 | { 348 | "cell_type": "markdown", 349 | "metadata": {}, 350 | "source": [ 351 | "#### Identify unique entries\n", 352 | "\n", 353 | "- sets\n", 354 | " - mutable\n", 355 | "- frozen sets\n", 356 | " - immutable" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": null, 362 | "metadata": {}, 363 | "outputs": [], 364 | "source": [ 365 | "my_set = set(my_list)\n", 366 | "my_set" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": null, 372 | "metadata": {}, 373 | "outputs": [], 374 | "source": [ 375 | "type(my_set)" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "execution_count": null, 381 | "metadata": {}, 382 | "outputs": [], 383 | "source": [ 384 | "for holiday in my_set:\n", 385 | " print(holiday)" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": {}, 392 | "outputs": [], 393 | "source": [ 394 | "my_set.remove(\"New Year's Day\")\n", 395 | "my_set" 396 | ] 397 | }, 398 | { 399 | "cell_type": "markdown", 400 | "metadata": {}, 401 | "source": [ 402 | "**frozen set** (a small new idea that we haven't seen before)" 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "execution_count": null, 408 | "metadata": {}, 409 | "outputs": [], 410 | "source": [ 411 | "my_frozenset = frozenset(my_list)\n", 412 | "my_frozenset" 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": null, 418 | "metadata": {}, 419 | "outputs": [], 420 | "source": [ 421 | "type(my_frozenset)" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": null, 427 | "metadata": {}, 428 | "outputs": [], 429 | "source": [ 430 | "my_frozenset.remove(\"Christmas\")" 431 | ] 432 | } 433 | ], 434 | "metadata": { 435 | "kernelspec": { 436 | "display_name": "Python 3 (ipykernel)", 437 | "language": "python", 438 | "name": "python3" 439 | }, 440 | "language_info": { 441 | "codemirror_mode": { 442 | "name": "ipython", 443 | "version": 3 444 | }, 445 | "file_extension": ".py", 446 | "mimetype": "text/x-python", 447 | "name": "python", 448 | "nbconvert_exporter": "python", 449 | "pygments_lexer": "ipython3", 450 | "version": "3.8.10" 451 | } 452 | }, 453 | "nbformat": 4, 454 | "nbformat_minor": 2 455 | } 456 | -------------------------------------------------------------------------------- /ml_introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Machine Learning (ML)\n", 8 | "\n", 9 | "**\"Can machines think [in the way that we do]?\"** [1]\n", 10 | "\n", 11 | "\n", 12 | "- The ML term was first used in 1959 by Arthur Samuel (an IBM researcher)\n", 13 | "\n", 14 | "\n", 15 | "- Mathematical Foundation\n", 16 | " - Statistics (the \"work-horse\" of ML)\n", 17 | " - Calculus (derivatives; optimizations)\n", 18 | " - Algerbra (vectors, matrix, tensors)\n", 19 | "\n", 20 | "\n", 21 | "- Different components were developed by researchers for many years. Only recently they were collected into libraries that make the ideas more accessible.\n", 22 | "\n", 23 | "## Machine Learning Catagories\n", 24 | "\n", 25 | "1. Shallow learning (e.g. **s**ci**k**it-**learn** - a.k.a. **sklearn**)\n", 26 | " - predefined features\n", 27 | "\n", 28 | "1. Deep learning (e.g. TensorFlow, PyTorch)\n", 29 | " - feature learning\n", 30 | " - mostly combines shallow learning instances together into \"layers\"\n", 31 | "\n", 32 | "\n", 33 | "**Sources**:\n", 34 | "1. Turing, Alan M. \"Computing machinery and intelligence.\" Parsing the Turing test. Springer, Dordrecht, 2009. 23-65.\n", 35 | "\n", 36 | "**Additional Resources**:\n", 37 | "1. https://en.wikipedia.org/wiki/Machine_learning" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "
\n", 45 | "\n", 46 | "# Shallow Learning\n", 47 | "\n", 48 | "## Catagories\n", 49 | "\n", 50 | "| Regression | Classification | Clustering | Dimension Reduction|\n", 51 | "| :-: | :-: | :-: | :-: |\n", 52 | "| Linear | Logistic Regression | K-means | Principle Component Analysis |\n", 53 | "| Polynomial | Support Vector Machine | Mean-Shift | Linear Discriminant Analysis |\n", 54 | "| StepWise | Naive Bayes | DBScan | Gernalized Discriminant Analysis |\n", 55 | "| Ridge | Nearest Neighbor | Agglomerative Hierachcial | Autoencoder |\n", 56 | "| Lasso | Decision Tree | Spectral Clustering | Non-Negative Matrix Factorization |\n", 57 | "| ElasticNet | Random Forest | Gaussian Mixture | UMAP |" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "## Supervised vs. Unsupervised Learning\n", 65 | "\n", 66 | "1. **Supervised** - the **target information is known** in the data set, and we **train to reproduce** that information\n", 67 | " - regression\n", 68 | " - classification\n", 69 | "\n", 70 | "1. **Unsupervised** - the **target information is unknown**, with the goal to \n", 71 | " - cluster the data's similarity (clustering)\n", 72 | " - determine the distribution of data (density estimation)\n", 73 | " - dimensionality reduction for exploring and visualization" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "
![\"Accuracy](\"00_images/31_machine_learning/scikit_learn_ml_map.png\")
\n", 90 | "\n", 91 | "# Deep Learning - Unsupervised Learning\n", 92 | "\n", 93 | "\n", 94 | "#### Neural network\n", 95 | "- **Input Layer**: features (observables) should have some degree of correlation (i.e., structure; nonlinear relationships)\n", 96 | "- Encoder: input $\\rightarrow$ hidden layers (data reduction)\n", 97 | "- **Hidden Layer**: a compressed knowledge representation of the original input\n", 98 | "- Decoder: hidden layers $\\rightarrow$ output\n", 99 | "- **Output Layer**" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "metadata": {}, 105 | "source": [ 106 | "
![\"neural](\"00_images/31_machine_learning/deep_neural_network.png\")
\n", 126 | "\n", 127 | "- Denoising Autoencoder\n", 128 | " - slightly **corrupt** the **input data** (i.e., add noise) to help make the encoding/decoding more generalizable\n", 129 | " - **target data** remains **uncorrupted**\n", 130 | " - make the decoding (reconstruction function) insensitive to small changes in the input\n", 131 | "\n", 132 | "
\n", 133 | "\n", 134 | "- Contractive Autoencoder\n", 135 | " - make the **encoding** (feature extraction function) **less sensitive** to **small changes** within the **input data**\n", 136 | " - learn similar encoding (hidden layer) for different inputs that vary slightly\n", 137 | "\n", 138 | "
\n", 139 | "\n", 140 | "- Variational Autoencoder (VAE)\n", 141 | " - https://arxiv.org/abs/1606.05908\n", 142 | " - training using **backpropagation** (aka **backward propagation of error**)\n", 143 | " - backpropagation - https://www.ibm.com/think/topics/backpropagation\n", 144 | " - starting from an **output**, compute the **importance** (measured as a gradient) that each neural network **parameter** has on the final model's **error** (predicted values) (i.e., loss function)\n", 145 | " - encoding is **regularized** (adding a penalty term to the model's loss function during the learning process) to ensure that the latent space has good properties (and thus, allowing us to have generative models to be created)\n", 146 | " - regularization - https://en.wikipedia.org/wiki/Regularization_(mathematics)\n", 147 | "\n", 148 | "\n", 149 | "\n", 150 | "" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": null, 157 | "metadata": {}, 158 | "outputs": [], 159 | "source": [] 160 | } 161 | ], 162 | "metadata": { 163 | "kernelspec": { 164 | "display_name": "Python 3 (ipykernel)", 165 | "language": "python", 166 | "name": "python3" 167 | }, 168 | "language_info": { 169 | "codemirror_mode": { 170 | "name": "ipython", 171 | "version": 3 172 | }, 173 | "file_extension": ".py", 174 | "mimetype": "text/x-python", 175 | "name": "python", 176 | "nbconvert_exporter": "python", 177 | "pygments_lexer": "ipython3", 178 | "version": "3.9.20" 179 | } 180 | }, 181 | "nbformat": 4, 182 | "nbformat_minor": 4 183 | } 184 | -------------------------------------------------------------------------------- /numpy_polynomials.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "1f0ce628", 6 | "metadata": {}, 7 | "source": [ 8 | "
\n",
9 | "\n",
10 | "\n",
11 | "# Polynomials\n",
12 | "\n",
13 | "## Importance of polynomials in science\n",
14 | "\n",
15 | "They are prevalent in all fields - ranging from physics to economics.\n",
16 | "\n",
17 | "**Examples**:\n",
18 | "- chemistry: model potential energy surfaces\n",
19 | "- astronomy: model object (stars, planets, asteroids) trajectories, velocities, interactions \n",
20 | "- economics: model forecast money trends\n",
21 | "- meteorology: model weather patterns\n",
22 | "- engineering: create physical designs (roller coaster)\n",
23 | "- virology: predict contagion growth\n",
24 | "- statistics: regressions and interpolation\n",
25 | "\n",
26 | "Often in the real world, we can rarely evaluate functions exactly because they become **too complicated**. Instead, we evaluate functions **using approximations created using polynomials** (e.g., Taylor series expansions: https://en.wikipedia.org/wiki/Taylor_series).\n",
27 | "\n",
28 | "---\n",
29 | "\n",
30 | "## Polynomials Defintion and Components\n",
31 | "\n",
32 | "Example (2$^{nd}$-degree polynomial): $3x^2 + 2x + 1$\n",
33 | "\n",
34 | "where the overall polynomial degree is defined by the highest power value (i.e., $x^2$: 2$^{nd}$-degree), and the coefficients are [3, 2, 1].\n",
35 | "\n",
36 | "- https://en.wikipedia.org/wiki/Polynomial\n",
37 | "\n",
38 | "\n",
39 | "## Example of simple polynomials using Numpy functions\n",
40 | "\n",
41 | "#### Creating one-dimensional polynomials to various degrees: \n",
42 | "\n",
43 | "`poly1d`: https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html\n",
44 | "\n",
45 | "
\n", 46 | "\n", 47 | "Okay, let's revisit the idea of polynomials, and slowly build up our understanding together." 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": null, 53 | "id": "fe395a92", 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "import matplotlib.pyplot as plt\n", 58 | "import numpy as np" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "id": "f6c6139e", 64 | "metadata": {}, 65 | "source": [ 66 | "1. Create a one dimensional (i.e., has one variable...x) polynomials using a function\n", 67 | " - using different coefficient(s)\n", 68 | " - evaluate the resulting function using a variable (i.e., x) value of 2.\n", 69 | "\n", 70 | "To do this **concisely** and to **reduce error**, let's create a user-defined function that\n", 71 | "\n", 72 | "a) generates a polynomial using provided coefficients,\n", 73 | "\n", 74 | "b) prints out the polynomial equation and\n", 75 | "\n", 76 | "c) its value when evaluated at x=2\n", 77 | "\n", 78 | "**Note**: I do not provide a docstring comment to `isinstance` within this function to simplify the teaching message. Normally, you should include them." 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": null, 84 | "id": "055068d1", 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "def my_poly1d(coeff: int=None):\n", 89 | " \n", 90 | " polynomial = np.poly1d(coeff)\n", 91 | " \n", 92 | " print(f'The value of the polynomial')\n", 93 | " print()\n", 94 | " print(f'{polynomial} when x=2: {polynomial(2)}')" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "id": "2763e62c", 100 | "metadata": {}, 101 | "source": [ 102 | "#### One coefficient (not very exciting): [M] --> $M$" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": null, 108 | "id": "cd0c38b6", 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "coefficients = [3]\n", 113 | "my_poly1d(coefficients)" 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "id": "445825fa", 119 | "metadata": {}, 120 | "source": [ 121 | "Notice that the polynomial generated does not have an 'x' term...the equation is just a constant.\n", 122 | "\n", 123 | "That means the y-values of the \"polynomial\" equation \"3\" is 3 for all x-values:" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": null, 129 | "id": "e6ad295d", 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "plt.plot()\n", 134 | "plt.hlines(xmin=0, xmax=10, y=3)\n", 135 | "plt.show()" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "id": "9d9dff3d", 141 | "metadata": {}, 142 | "source": [ 143 | "#### Two coefficients: [M, N] --> $Mx + N$\n", 144 | "- Note how the M shifts to the x" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": null, 150 | "id": "39a33a98", 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "coefficients = [1, 2]\n", 155 | "my_poly1d(coefficients)" 156 | ] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "id": "a737dfe5", 161 | "metadata": {}, 162 | "source": [ 163 | "##### Including a negative coefficients: [1, -1]" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "id": "6e46edcd", 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "coefficients = [1, -1]\n", 174 | "my_poly1d(coefficients)" 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "id": "c8e49f19", 180 | "metadata": {}, 181 | "source": [ 182 | "#### Three coefficients: [M, N, O] --> $Mx^2 + Nx + O$" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": null, 188 | "id": "a5389129", 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "# coefficients = [x^2, x, constant]\n", 193 | "coefficients = [-4, 1, -2]\n", 194 | "my_poly1d(coefficients)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "id": "1dd9d533", 200 | "metadata": {}, 201 | "source": [ 202 | "#### Four coefficients: [M, N, O, P] --> $Mx^3 + Nx^2 + Ox + P$" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "id": "cc678750", 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "coefficients = [5, -4, 1, -1]\n", 213 | "my_poly1d(coefficients)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "id": "428af698", 219 | "metadata": {}, 220 | "source": [ 221 | "#### Access the Polynomial's Coefficients" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": null, 227 | "id": "78a7a86a", 228 | "metadata": {}, 229 | "outputs": [], 230 | "source": [ 231 | "polynomial = np.poly1d(coefficients)\n", 232 | "\n", 233 | "polynomial.coefficients" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "id": "668701f3", 239 | "metadata": {}, 240 | "source": [ 241 | "#### Access the Polynomial's Order" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "id": "6c8f5524", 248 | "metadata": {}, 249 | "outputs": [], 250 | "source": [ 251 | "polynomial.order" 252 | ] 253 | }, 254 | { 255 | "cell_type": "markdown", 256 | "id": "3071942b", 257 | "metadata": {}, 258 | "source": [ 259 | "## Math with polynomials\n", 260 | "\n", 261 | "#### Square of a polynomial\n", 262 | "\n", 263 | "The square of a polynomial\n", 264 | "\n", 265 | "\n", 266 | "$\n", 267 | "\\begin{align}\n", 268 | "(a + b)^2 &= (a + b)(a + b)\\\\\n", 269 | " &= a^2 + 2ab + b^2\n", 270 | "\\end{align}\n", 271 | "$\n", 272 | "\n", 273 | "**Example**:\n", 274 | "\n", 275 | "`np.poly1d([2, 1])` $\\rightarrow$ ${2x + 1}$\n", 276 | "\n", 277 | "Thus we when square this polynomial:\n", 278 | "\n", 279 | "$(2x + 1)(2x + 1) = 4x^2 + 4x + 1$\n", 280 | "\n", 281 | "So, how do we code this:" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": null, 287 | "id": "2b293c77", 288 | "metadata": {}, 289 | "outputs": [], 290 | "source": [ 291 | "coefficients = [2, 1]\n", 292 | "\n", 293 | "polynomial = np.poly1d(coefficients)\n", 294 | "\n", 295 | "poly_square = polynomial**2\n", 296 | "\n", 297 | "print(poly_square)" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "id": "2d91a4c8", 303 | "metadata": {}, 304 | "source": [ 305 | "Now evaluate the squared polynomial at x=2\n", 306 | "\n", 307 | "(i.e., $ (2x + 1)^2 \\text{ at }x=2 \\rightarrow 4*2^2 + 4*2 + 1$)" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": null, 313 | "id": "eafb76e6", 314 | "metadata": {}, 315 | "outputs": [], 316 | "source": [ 317 | "poly_square(2)" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "id": "8e6e0878", 323 | "metadata": {}, 324 | "source": [ 325 | "#### A polynomial cubed" 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": null, 331 | "id": "21e231c4", 332 | "metadata": {}, 333 | "outputs": [], 334 | "source": [ 335 | "print(polynomial**3)" 336 | ] 337 | }, 338 | { 339 | "cell_type": "markdown", 340 | "id": "e60ebef5", 341 | "metadata": {}, 342 | "source": [ 343 | "Summary so far:\n", 344 | "1. We reviewed what a polynomial is,\n", 345 | "2. We learned that `poly1d` allows us to create n-degree polynomials that depend on x data\n", 346 | "3. We learned that we can do some polynomial math easily\n", 347 | "\n", 348 | "
\n", 349 | "\n", 350 | "What happens though when you have **x- and y-data** that follow a **polynomial form**, but you **don't know** the **coefficients** to define the polynomial?\n", 351 | "\n", 352 | "\n", 353 | "## Numpy's `polyfit` Function\n", 354 | "\n", 355 | "Fit a polynomial of a specified degree to specific data (x, y).\n", 356 | "\n", 357 | "Returns a \"vector\" of coefficients that minimizes the squared error.\n", 358 | "\n", 359 | "- https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html\n", 360 | "\n", 361 | "First, let's create some **nonideal data** that follows a **cubic polynomial** form." 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": null, 367 | "id": "9f4b5b82", 368 | "metadata": {}, 369 | "outputs": [], 370 | "source": [ 371 | "x_values = [-12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1,\n", 372 | " 0, 1, 2, 3, 4, 5, 6, 7, 8]\n", 373 | "\n", 374 | "y_values = [2000, 1500, 1400, 700, 600, 500, 300, 100, 70, 30, 20, 5,\n", 375 | " 4, 7, 10, 6, -10, -50, -200, -220, -400]" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "execution_count": null, 381 | "id": "2ea722c2", 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [ 385 | "plt.plot()\n", 386 | "plt.plot(x_values, y_values, 'o', markersize=15)\n", 387 | "plt.show()" 388 | ] 389 | }, 390 | { 391 | "cell_type": "markdown", 392 | "id": "f833017f", 393 | "metadata": {}, 394 | "source": [ 395 | "Now, `polyfit` will fit a n-degree polynomial (i.e., 3$^{rd}$ degree) to the provided x- and y-data.\n", 396 | "\n", 397 | "The `polyfit` function returns coefficients.\n", 398 | "\n", 399 | "Recall from above: four coefficients are needed to define a 3$^{rd}$ degree polynomial\n", 400 | "\n", 401 | "[M, N, O, P] --> $Mx^3 + Nx^2 + Ox + P$" 402 | ] 403 | }, 404 | { 405 | "cell_type": "code", 406 | "execution_count": null, 407 | "id": "3c890c78", 408 | "metadata": {}, 409 | "outputs": [], 410 | "source": [ 411 | "coefficients = np.polyfit(x_values, y_values, 3)\n", 412 | "coefficients" 413 | ] 414 | }, 415 | { 416 | "cell_type": "markdown", 417 | "id": "203b43de", 418 | "metadata": {}, 419 | "source": [ 420 | "Therefore, we have the following polynomial (rounded coefficients):\n", 421 | "\n", 422 | "$-1.046*x^3 + 1.696*x^2 + 3.413*x + 2.793$\n", 423 | "\n", 424 | "Now we can use Numpy's `poly1d` to encode this polynomial exactly:" 425 | ] 426 | }, 427 | { 428 | "cell_type": "code", 429 | "execution_count": null, 430 | "id": "1f37527a", 431 | "metadata": {}, 432 | "outputs": [], 433 | "source": [ 434 | "cubic_polynomial = np.poly1d(coefficients)\n", 435 | "print(cubic_polynomial)" 436 | ] 437 | }, 438 | { 439 | "cell_type": "markdown", 440 | "id": "d7f69413", 441 | "metadata": {}, 442 | "source": [ 443 | "Using this cubic polynomial, we can generate \"ideal\" y-data given a range of x-data values." 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": null, 449 | "id": "819fc8e1", 450 | "metadata": {}, 451 | "outputs": [], 452 | "source": [ 453 | "y_ideal_cubic = cubic_polynomial(x_values)" 454 | ] 455 | }, 456 | { 457 | "cell_type": "markdown", 458 | "id": "375854cb", 459 | "metadata": {}, 460 | "source": [ 461 | "Plot both the original data and the data from the fitted cubic polynomial:" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": null, 467 | "id": "72511232", 468 | "metadata": {}, 469 | "outputs": [], 470 | "source": [ 471 | "plt.plot()\n", 472 | "plt.plot(x_values, y_values, 'o', markersize=15)\n", 473 | "plt.plot(x_values, y_ideal_cubic, linewidth=5)\n", 474 | "plt.show()" 475 | ] 476 | }, 477 | { 478 | "cell_type": "code", 479 | "execution_count": null, 480 | "id": "49a71e37-29f1-401e-9995-cbcb37630788", 481 | "metadata": {}, 482 | "outputs": [], 483 | "source": [] 484 | } 485 | ], 486 | "metadata": { 487 | "kernelspec": { 488 | "display_name": "Python 3 (ipykernel)", 489 | "language": "python", 490 | "name": "python3" 491 | }, 492 | "language_info": { 493 | "codemirror_mode": { 494 | "name": "ipython", 495 | "version": 3 496 | }, 497 | "file_extension": ".py", 498 | "mimetype": "text/x-python", 499 | "name": "python", 500 | "nbconvert_exporter": "python", 501 | "pygments_lexer": "ipython3", 502 | "version": "3.11.9" 503 | } 504 | }, 505 | "nbformat": 4, 506 | "nbformat_minor": 5 507 | } 508 | -------------------------------------------------------------------------------- /operations.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Basic Operators\n",
8 | "***"
9 | ]
10 | },
11 | {
12 | "cell_type": "markdown",
13 | "metadata": {},
14 | "source": [
15 | "Class exercise:\n",
16 | " Using a calculator (i.e. not via coding), calculate:\n",
17 | " \n",
18 | " 2 + 3 * 4 / 5\n",
19 | " \n",
20 | " Write your answer in the the 'chat box'\n",
21 | " \n",
22 | "
\n", 23 | "
" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "Answer = 4.4 (we will see how this done below)\n", 31 | "\n", 32 | "---\n", 33 | "# Operators\n", 34 | "\n", 35 | "In Python, there are different types of **operators** (special symbols) that operate on different values. Some of the basic operators include:\n", 36 | "\n", 37 | "- arithmetic operators\n", 38 | " - **`+`** (addition)\n", 39 | " - **`-`** (subtraction)\n", 40 | " - **`*`** (multiplication)\n", 41 | " - **`/`** (division)\n", 42 | " - __`**`__ (exponent)\n", 43 | "\n", 44 | "\n", 45 | "- assignment operators\n", 46 | " - **`=`** (assign a value)\n", 47 | " - **`+=`** (add and re-assign; increment)\n", 48 | " - **`-=`** (subtract and re-assign; decrement)\n", 49 | " - **`*=`** (multiply and re-assign)\n", 50 | "\n", 51 | "\n", 52 | "- comparison operators (return either `True` or `False`; booleans)\n", 53 | " - **`==`** (equal to)\n", 54 | " - **`!=`** (not equal to)\n", 55 | " - **`<`** (less than)\n", 56 | " - **`<=`** (less than or equal to)\n", 57 | " - **`>`** (greater than)\n", 58 | " - **`>=`** (greater than or equal to)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "---\n", 66 | "## Order of Operation\n", 67 | "\n", 68 | "When multiple operators are used in a single expression, **operator precedence** determines which parts of the expression are evaluated in which order.\n", 69 | "\n", 70 | "Operators with higher precedence are evaluated first.\n", 71 | "\n", 72 | "Operators with the same precedence are evaluated from **left to right**.\n", 73 | "\n", 74 | "Precedence importance ordering:\n", 75 | "1. `()` parentheses, for grouping\n", 76 | "2. `**` exponent\n", 77 | "3. `*`, `/` multiplication and division\n", 78 | "4. `+`, `-` addition and subtraction\n", 79 | "5. `==`, `!=`, `<`, `<=`, `>`, `>=` comparisons\n", 80 | "\n", 81 | "\n", 82 | "#### Additional information:\n", 83 | "\n", 84 | "1. https://docs.python.org/3/reference/expressions.html#operator-precedence\n", 85 | "\n", 86 | "2. https://en.wikipedia.org/wiki/Order_of_operations" 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "metadata": {}, 92 | "source": [ 93 | "#### Example 1: 2 + 3 * 4 / 5\n", 94 | "\n", 95 | "> Step 1. $3 * 4 = 12$ (**Notice**: `*` and `/` have the same precedence, but `*` is farthest to the left)\n", 96 | "\n", 97 | "> Step 2. $12 / 5 = 2.2$\n", 98 | "\n", 99 | "> Step 3. $2 + 2.2 = 4.4$\n", 100 | "\n", 101 | "Now, let's see what Python gives:" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "equation = 2.0 + 3.0 * 4.0 / 5.0\n", 111 | "\n", 112 | "print(\"2.0 + 3.0 * 4.0 / 5.0 = {0}\".format(equation))" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "Next, let's force a specific sequence of actions to occur by using `()`.\n", 120 | "\n", 121 | "#### Example 2: (2 + 3) * 4 / 5" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "equation = (2.0 + 3.0) * 4.0 / 5.0\n", 131 | "\n", 132 | "print(\"(2.0 + 3.0) * 4.0 / 5.0 = {0}\".format(equation))\n", 133 | "\n", 134 | "print('''\n", 135 | " i.e. step 1: 2.0 + 3.0 = 5.0\n", 136 | " step 2: 5.0 * 4.0 = 20.0 since * is further left than /\n", 137 | " step 3: 20.0 / 5.0 = 4.0\n", 138 | " ''')" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "#### Example 3: 2 + 3 * 4 ** 2 / 5" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "equation = 2 + 3 * 4 ** 2 / 5\n", 155 | "\n", 156 | "print(\"2 + 3 * 4 ** 2 / 5 = \", equation)\n", 157 | "\n", 158 | "print(\"\"\"\n", 159 | " i.e. 4**2 = 16\n", 160 | " 3*16 = 48\n", 161 | " 48/5 = 9.6\n", 162 | " 2+9.6 = 11.6\n", 163 | " \"\"\")" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "#### Example 4: 2 + (3 * 4) ** 2 / 5" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [ 179 | "equation = 2 + (3 * 4) ** 2 / 5\n", 180 | "\n", 181 | "print(\"2 + (3 * 4) ** 2 / 5 = \", equation)\n", 182 | "\n", 183 | "print(\"\"\"\n", 184 | " i.e. 3*4 = 12\n", 185 | " 12**2 = 144\n", 186 | " 144/5 = 28.8\n", 187 | " 2+28.8 = 30.8\n", 188 | " \"\"\")" 189 | ] 190 | }, 191 | { 192 | "cell_type": "markdown", 193 | "metadata": {}, 194 | "source": [ 195 | "**Take-home Message**: Be careful in encoding mathematical equations so that you do introduce and error.\n", 196 | "\n", 197 | "---\n", 198 | "## Increment (by reassigning variable)\n", 199 | "- right-hand-side happens first, and then the left-hand-side\n", 200 | "\n", 201 | "In the following assignment statements\n", 202 | "- evaluation of the right-hand-side is done first, then\n", 203 | "- it is assigned to the variable" 204 | ] 205 | }, 206 | { 207 | "cell_type": "markdown", 208 | "metadata": {}, 209 | "source": [ 210 | "Simple demonstration - counting plants, starting with 1" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": null, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "planets = 1\n", 220 | "print(planets)" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "Now lets add 1 more to the number of planets:\n", 228 | "- notice how `planets + 1` is computed first\n", 229 | "- then the new value is assigned to the variable `planets`" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": null, 235 | "metadata": {}, 236 | "outputs": [], 237 | "source": [ 238 | "planets = planets + 1\n", 239 | "print(planets)" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "Alternatively, we can use `+= 1` instead of `planets + 1`:" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": null, 252 | "metadata": {}, 253 | "outputs": [], 254 | "source": [ 255 | "\n", 256 | "planets += 1\n", 257 | "print(planets)" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "metadata": {}, 263 | "source": [ 264 | "`+=` will increment other numbers too" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": null, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [ 273 | "planets += 5\n", 274 | "print(planets)" 275 | ] 276 | }, 277 | { 278 | "cell_type": "markdown", 279 | "metadata": {}, 280 | "source": [ 281 | "---\n", 282 | "## Booleans\n", 283 | "- used through comparison operators (e.g. `<=`)\n", 284 | "\n", 285 | "Demo Scenerio - you have to discover more than 10 planets before you graduate." 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": null, 291 | "metadata": {}, 292 | "outputs": [], 293 | "source": [ 294 | "total_planets = 10\n", 295 | "print(total_planets < planets)" 296 | ] 297 | }, 298 | { 299 | "cell_type": "markdown", 300 | "metadata": {}, 301 | "source": [ 302 | "### Why could this be interesting?\n", 303 | "- you can create conditions" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": null, 309 | "metadata": {}, 310 | "outputs": [], 311 | "source": [ 312 | "planets = 0\n", 313 | "\n", 314 | "if (total_planets < planets) == False:\n", 315 | " print('You can not graduate yet - get back to work!')\n", 316 | "else:\n", 317 | " print('Congratulations - it is time to move on!')" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "metadata": {}, 323 | "source": [ 324 | "- you can create loops" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": null, 330 | "metadata": {}, 331 | "outputs": [], 332 | "source": [ 333 | "planets = 0\n", 334 | "graduating = False\n", 335 | "\n", 336 | "while not graduating:\n", 337 | " if (total_planets < planets) == False:\n", 338 | " print('You can not graduate yet. Number of planets discovered: {0}'.format(planets))\n", 339 | " planets += 1\n", 340 | " else:\n", 341 | " print('Congratulations - it is time to move on!')\n", 342 | " graduating = True" 343 | ] 344 | } 345 | ], 346 | "metadata": { 347 | "kernelspec": { 348 | "display_name": "Python 3 (ipykernel)", 349 | "language": "python", 350 | "name": "python3" 351 | }, 352 | "language_info": { 353 | "codemirror_mode": { 354 | "name": "ipython", 355 | "version": 3 356 | }, 357 | "file_extension": ".py", 358 | "mimetype": "text/x-python", 359 | "name": "python", 360 | "nbconvert_exporter": "python", 361 | "pygments_lexer": "ipython3", 362 | "version": "3.8.17" 363 | } 364 | }, 365 | "nbformat": 4, 366 | "nbformat_minor": 2 367 | } 368 | -------------------------------------------------------------------------------- /scientific_figures.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Scientific Figures\n",
8 | "\n",
9 | "- General Figure Making Advice\n",
10 | " - [Ten Simple Rules for Better Figures (PLoS)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833)\n",
11 | "\n",
12 | " \n",
13 | "- Colorblindness\n",
14 | " - [Wong, Bang. \"Points of view: Color blindness.\" Nature (2011) 441.](https://www.nature.com/articles/nmeth.1618#citeas)\n",
15 | " - [Colorbrewer (for pallete suggestions)](https://colorbrewer2.org)"
16 | ]
17 | },
18 | {
19 | "cell_type": "markdown",
20 | "metadata": {},
21 | "source": [
22 | "Ideas:\n",
23 | " - Find a figure that gives examples of possible plots\n",
24 | " - Talk about the components that go into making a good plot (e.g. ticks, legend, lables)"
25 | ]
26 | }
27 | ],
28 | "metadata": {
29 | "kernelspec": {
30 | "display_name": "Python 3 (ipykernel)",
31 | "language": "python",
32 | "name": "python3"
33 | },
34 | "language_info": {
35 | "codemirror_mode": {
36 | "name": "ipython",
37 | "version": 3
38 | },
39 | "file_extension": ".py",
40 | "mimetype": "text/x-python",
41 | "name": "python",
42 | "nbconvert_exporter": "python",
43 | "pygments_lexer": "ipython3",
44 | "version": "3.8.10"
45 | }
46 | },
47 | "nbformat": 4,
48 | "nbformat_minor": 2
49 | }
50 |
--------------------------------------------------------------------------------
/statistics_simple.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## The Statistics Library\n",
8 | "- Relaively Simple (compared to numpy or scipy)\n",
9 | " - low number of other library dependencies (i.e. more \"usable\" by the community)\n",
10 | "- Relaively Slow (compared to numpy) - use with smaller data sets\n",
11 | "- More precise (compared to numpy) - when high precision is important"
12 | ]
13 | },
14 | {
15 | "cell_type": "markdown",
16 | "metadata": {},
17 | "source": [
18 | "## Mean, median, standard deviation and variance\n",
19 | "- Measures of the center in a numerical data\n",
20 | " - Mean (i.e. average): can be influenced by several outliers\n",
21 | " - Median: the middle value of sorted data (less influenced by outliers)\n",
22 | "- Standard Deviation: a measure of the data's uncertainty\n",
23 | "- Variance: a measure of the data's spread"
24 | ]
25 | },
26 | {
27 | "cell_type": "code",
28 | "execution_count": null,
29 | "metadata": {},
30 | "outputs": [],
31 | "source": [
32 | "import matplotlib.pyplot as plt\n",
33 | "import statistics"
34 | ]
35 | },
36 | {
37 | "cell_type": "markdown",
38 | "metadata": {},
39 | "source": [
40 | "#### 3 Data sets \n",
41 | "- They have the same mean value\n",
42 | "- They have different median, standard deviation and variance\n",
43 | "- Asssumption: these values have 2 (e.g. 65.) or 3 (i.e. 110.) sigfigs"
44 | ]
45 | },
46 | {
47 | "cell_type": "code",
48 | "execution_count": null,
49 | "metadata": {},
50 | "outputs": [],
51 | "source": [
52 | "data_1 = [65, 75, 73, 50, 60, 64, 69, 62, 67, 85]\n",
53 | "data_2 = [85, 79, 57, 39, 45, 71, 67, 87, 91, 49]\n",
54 | "data_3 = [43, 51, 53, 110, 50, 48, 87, 69, 68, 91]"
55 | ]
56 | },
57 | {
58 | "cell_type": "code",
59 | "execution_count": null,
60 | "metadata": {},
61 | "outputs": [],
62 | "source": [
63 | "plt.figure()\n",
64 | "plt.xlim(0, 9)\n",
65 | "plt.ylim(30, 120)\n",
66 | "\n",
67 | "plt.plot(data_1, marker='.', markersize=10)\n",
68 | "plt.show()"
69 | ]
70 | },
71 | {
72 | "cell_type": "markdown",
73 | "metadata": {},
74 | "source": [
75 | "https://matplotlib.org/3.2.1/api/markers_api.html"
76 | ]
77 | },
78 | {
79 | "cell_type": "code",
80 | "execution_count": null,
81 | "metadata": {},
82 | "outputs": [],
83 | "source": [
84 | "plt.figure()\n",
85 | "plt.xlim(0, 9)\n",
86 | "plt.ylim(30, 120)\n",
87 | "\n",
88 | "plt.plot(data_2)\n",
89 | "plt.show()"
90 | ]
91 | },
92 | {
93 | "cell_type": "code",
94 | "execution_count": null,
95 | "metadata": {},
96 | "outputs": [],
97 | "source": [
98 | "plt.figure()\n",
99 | "plt.xlim(0, 9)\n",
100 | "plt.ylim(30, 120)\n",
101 | "\n",
102 | "plt.plot(data_3)\n",
103 | "plt.show()"
104 | ]
105 | },
106 | {
107 | "cell_type": "markdown",
108 | "metadata": {},
109 | "source": [
110 | "---\n",
111 | "## Calculate some common statistics (and show on plot)"
112 | ]
113 | },
114 | {
115 | "cell_type": "markdown",
116 | "metadata": {},
117 | "source": [
118 | "### Data Set 1"
119 | ]
120 | },
121 | {
122 | "cell_type": "code",
123 | "execution_count": null,
124 | "metadata": {},
125 | "outputs": [],
126 | "source": [
127 | "data_1.sort()\n",
128 | "data_1"
129 | ]
130 | },
131 | {
132 | "cell_type": "code",
133 | "execution_count": null,
134 | "metadata": {},
135 | "outputs": [],
136 | "source": [
137 | "print(f'Mean: {statistics.mean(data_1)}')\n",
138 | "print(f'Median: {statistics.median(data_1)}')\n",
139 | "print(f'StDev: {statistics.stdev(data_1)}')\n",
140 | "print(f'Variance: {statistics.variance(data_1)}')"
141 | ]
142 | },
143 | {
144 | "cell_type": "markdown",
145 | "metadata": {},
146 | "source": [
147 | "Mean: read\n",
148 | "\n",
149 | "Median: green"
150 | ]
151 | },
152 | {
153 | "cell_type": "code",
154 | "execution_count": null,
155 | "metadata": {},
156 | "outputs": [],
157 | "source": [
158 | "plt.figure()\n",
159 | "plt.xlim(0, 9)\n",
160 | "plt.ylim(30, 120)\n",
161 | "\n",
162 | "plt.axhline(y=67, color='red')\n",
163 | "plt.axhline(y=66, color='green')\n",
164 | "\n",
165 | "plt.plot(data_1, marker='.', markersize=10)\n",
166 | "plt.show()"
167 | ]
168 | },
169 | {
170 | "cell_type": "markdown",
171 | "metadata": {},
172 | "source": [
173 | "### Data Set 2"
174 | ]
175 | },
176 | {
177 | "cell_type": "code",
178 | "execution_count": null,
179 | "metadata": {},
180 | "outputs": [],
181 | "source": [
182 | "print(f'Mean: {statistics.mean(data_2)}')\n",
183 | "print(f'Median: {statistics.median(data_2)}')\n",
184 | "print(f'StDev: {statistics.stdev(data_2)}')\n",
185 | "print(f'Variance: {statistics.variance(data_2)}')"
186 | ]
187 | },
188 | {
189 | "cell_type": "code",
190 | "execution_count": null,
191 | "metadata": {},
192 | "outputs": [],
193 | "source": [
194 | "plt.figure()\n",
195 | "plt.xlim(0, 9)\n",
196 | "plt.ylim(30, 120)\n",
197 | "\n",
198 | "plt.axhline(y=67, color='red')\n",
199 | "plt.axhline(y=69, color='green')\n",
200 | "\n",
201 | "plt.plot(data_2, marker='.', markersize=10)\n",
202 | "plt.show()"
203 | ]
204 | },
205 | {
206 | "cell_type": "markdown",
207 | "metadata": {},
208 | "source": [
209 | "### Data Set 3"
210 | ]
211 | },
212 | {
213 | "cell_type": "code",
214 | "execution_count": null,
215 | "metadata": {},
216 | "outputs": [],
217 | "source": [
218 | "print(f'Mean: {statistics.mean(data_3)}')\n",
219 | "print(f'Median: {statistics.median(data_3)}')\n",
220 | "print(f'StDev: {statistics.stdev(data_3)}')\n",
221 | "print(f'Variance: {statistics.variance(data_3)}')"
222 | ]
223 | },
224 | {
225 | "cell_type": "code",
226 | "execution_count": null,
227 | "metadata": {},
228 | "outputs": [],
229 | "source": [
230 | "plt.figure()\n",
231 | "plt.xlim(0, 9)\n",
232 | "plt.ylim(30, 120)\n",
233 | "\n",
234 | "plt.axhline(y=67, color='red')\n",
235 | "plt.axhline(y=60, color='green')\n",
236 | "\n",
237 | "plt.plot(data_3, marker='.', markersize=10)\n",
238 | "plt.show()"
239 | ]
240 | },
241 | {
242 | "cell_type": "markdown",
243 | "metadata": {},
244 | "source": [
245 | "## How might the above code been done better?\n",
246 | "\n",
247 | "- reduce possible inclusion of errors"
248 | ]
249 | },
250 | {
251 | "cell_type": "code",
252 | "execution_count": null,
253 | "metadata": {},
254 | "outputs": [],
255 | "source": [
256 | "def do_statistics(working_data=None):\n",
257 | " print(f'Mean: {statistics.mean(working_data)}')\n",
258 | " print(f'Median: {statistics.median(working_data)}')\n",
259 | " print(f'StDev: {statistics.stdev(working_data)}')\n",
260 | " print(f'Variance: {statistics.variance(working_data)}')\n",
261 | "\n",
262 | "do_statistics(data_1)"
263 | ]
264 | },
265 | {
266 | "cell_type": "markdown",
267 | "metadata": {},
268 | "source": [
269 | "---\n",
270 | "Source: https://www.siyavula.com/read/maths/grade-11/statistics/11-statistics-04"
271 | ]
272 | },
273 | {
274 | "cell_type": "code",
275 | "execution_count": null,
276 | "metadata": {},
277 | "outputs": [],
278 | "source": []
279 | }
280 | ],
281 | "metadata": {
282 | "kernelspec": {
283 | "display_name": "Python 3 (ipykernel)",
284 | "language": "python",
285 | "name": "python3"
286 | },
287 | "language_info": {
288 | "codemirror_mode": {
289 | "name": "ipython",
290 | "version": 3
291 | },
292 | "file_extension": ".py",
293 | "mimetype": "text/x-python",
294 | "name": "python",
295 | "nbconvert_exporter": "python",
296 | "pygments_lexer": "ipython3",
297 | "version": "3.8.10"
298 | }
299 | },
300 | "nbformat": 4,
301 | "nbformat_minor": 2
302 | }
303 |
--------------------------------------------------------------------------------
/student_questions.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Questions during course\n",
8 | "\n",
9 | "## Lecture 1\n",
10 | "1. Does Python have Double object type?\n",
11 | "\n",
12 | " Answer: Python's float type has double precision. NumPy has options for higher percision."
13 | ]
14 | },
15 | {
16 | "cell_type": "code",
17 | "execution_count": null,
18 | "metadata": {},
19 | "outputs": [],
20 | "source": []
21 | }
22 | ],
23 | "metadata": {
24 | "kernelspec": {
25 | "display_name": "Python 3",
26 | "language": "python",
27 | "name": "python3"
28 | },
29 | "language_info": {
30 | "codemirror_mode": {
31 | "name": "ipython",
32 | "version": 3
33 | },
34 | "file_extension": ".py",
35 | "mimetype": "text/x-python",
36 | "name": "python",
37 | "nbconvert_exporter": "python",
38 | "pygments_lexer": "ipython3",
39 | "version": "3.8.1"
40 | }
41 | },
42 | "nbformat": 4,
43 | "nbformat_minor": 2
44 | }
45 |
--------------------------------------------------------------------------------
/t-test.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## T-Tests\n",
8 | "- Used on two samples data sets that are numeric\n",
9 | " - if you have more than two data sets, then you must either do it a) pairwise T-tests, or something else (e.g. Anova)\n",
10 | "\n",
11 | "A T-test does two things:\n",
12 | "1. How **different** two means values (obtained from two sets of distributed data) are - T value\n",
13 | " - Samller values indicate less differences\n",
14 | " \n",
15 | "2. How **significant** are these differences (i.e. did they occur randomly/by chance) - P value\n",
16 | " - Smaller values indicate that they were not random (i.e. P=0.05 means there is a 5% probability that the results happened by chance).\n",
17 | "\n",
18 | "\n",
19 | "Random, Gaussian (i.e. normal) distributin of data points\n",
20 | "- random.normal: https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html\n",
21 | " - creates a Gaussian distribution with a curve width based on a standard deviation value"
22 | ]
23 | },
24 | {
25 | "cell_type": "code",
26 | "execution_count": null,
27 | "metadata": {},
28 | "outputs": [],
29 | "source": [
30 | "from scipy import stats\n",
31 | "import matplotlib.pyplot as plt\n",
32 | "import numpy as np"
33 | ]
34 | },
35 | {
36 | "cell_type": "code",
37 | "execution_count": null,
38 | "metadata": {},
39 | "outputs": [],
40 | "source": [
41 | "## Three data sets - with means at 0.0 and 2.0, and standard deviations of 1.0 and 0.5\n",
42 | "data1 = np.random.normal(0.0, 1.0, size=50)\n",
43 | "data2 = np.random.normal(0.0, 0.5, size=50)\n",
44 | "data3 = np.random.normal(2.0, 1.0, size=50)"
45 | ]
46 | },
47 | {
48 | "cell_type": "markdown",
49 | "metadata": {},
50 | "source": [
51 | "Note: std = 0, would provide perfect resulting means of either 0.0 and 2.0.\n",
52 | "\n",
53 | "So, let's check what the means actually are (i.e. the effect of the std. dev.)"
54 | ]
55 | },
56 | {
57 | "cell_type": "code",
58 | "execution_count": null,
59 | "metadata": {},
60 | "outputs": [],
61 | "source": [
62 | "np.mean(data1)"
63 | ]
64 | },
65 | {
66 | "cell_type": "code",
67 | "execution_count": null,
68 | "metadata": {},
69 | "outputs": [],
70 | "source": [
71 | "np.mean(data2)"
72 | ]
73 | },
74 | {
75 | "cell_type": "code",
76 | "execution_count": null,
77 | "metadata": {},
78 | "outputs": [],
79 | "source": [
80 | "np.mean(data3)"
81 | ]
82 | },
83 | {
84 | "cell_type": "code",
85 | "execution_count": null,
86 | "metadata": {},
87 | "outputs": [],
88 | "source": [
89 | "plt.plot()\n",
90 | "plt.plot(data1, '-o')\n",
91 | "plt.plot(data2, '-r')\n",
92 | "plt.plot(data3, '-g')\n",
93 | "\n",
94 | "plt.hlines(np.mean(data1), 0, 50, colors='blue')\n",
95 | "plt.hlines(np.mean(data2), 0, 50, colors='red')\n",
96 | "plt.hlines(np.mean(data3), 0, 50, colors='green')\n",
97 | "\n",
98 | "plt.show()"
99 | ]
100 | },
101 | {
102 | "cell_type": "markdown",
103 | "metadata": {},
104 | "source": [
105 | "Use seaborn to easily plot the histogram of the data"
106 | ]
107 | },
108 | {
109 | "cell_type": "code",
110 | "execution_count": null,
111 | "metadata": {},
112 | "outputs": [],
113 | "source": [
114 | "import seaborn as sns\n",
115 | "\n",
116 | "plt.plot()\n",
117 | "sns.kdeplot(data1, color='blue', shade=True)\n",
118 | "sns.kdeplot(data2, color='red', shade=True)\n",
119 | "sns.kdeplot(data3, color='green', shade=True)\n",
120 | "plt.title(\"Histogram of Data\")\n",
121 | "plt.show()"
122 | ]
123 | },
124 | {
125 | "cell_type": "markdown",
126 | "metadata": {},
127 | "source": [
128 | "#### t-test\n",
129 | "- https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html\n",
130 | "\n",
131 | "- \"The test measures **whether the mean (expected) value differs significantly across samples**.\"\n",
132 | "\n",
133 | "- A **large p-value** (e.g. **0.05, 0.1**), then it is likely that the averages are not distinguishable.\"\n",
134 | "\n",
135 | "- \"If the p-value is smaller than the threshold, e.g. 1%, 5% or 10%, then we reject the null hypothesis of equal averages.\""
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": null,
141 | "metadata": {},
142 | "outputs": [],
143 | "source": [
144 | "t_stat, p_value = stats.ttest_ind(data1, data3, equal_var = False)"
145 | ]
146 | },
147 | {
148 | "cell_type": "code",
149 | "execution_count": null,
150 | "metadata": {},
151 | "outputs": [],
152 | "source": [
153 | "t_stat"
154 | ]
155 | },
156 | {
157 | "cell_type": "code",
158 | "execution_count": null,
159 | "metadata": {},
160 | "outputs": [],
161 | "source": [
162 | "p_value"
163 | ]
164 | },
165 | {
166 | "cell_type": "markdown",
167 | "metadata": {},
168 | "source": [
169 | "---\n",
170 | "#### Real-world example\n",
171 | "\n",
172 | "Research task: Investigate the height, weight, gender and age of a population of people (e.g. Germans)\n",
173 | "- We can't investigate the full population, so we must sample a random subset of people"
174 | ]
175 | },
176 | {
177 | "cell_type": "code",
178 | "execution_count": null,
179 | "metadata": {},
180 | "outputs": [],
181 | "source": [
182 | "len(np.random.random_sample((50,)))"
183 | ]
184 | },
185 | {
186 | "cell_type": "code",
187 | "execution_count": null,
188 | "metadata": {},
189 | "outputs": [],
190 | "source": [
191 | "import pandas as pd\n",
192 | "\n",
193 | "np.random.seed(10)\n",
194 | "\n",
195 | "height = np.random.uniform(0.5, 2.0, size=50)\n",
196 | "weight = np.random.uniform(15.0, 90.0, size=50)\n",
197 | "gender = np.random.choice(('Male', 'Female'), size=50, p=[0.4, 0.6]) ## With a 40:60 ratio of male:female\n",
198 | "age = np.random.uniform(3.0, 80.0, size=50)"
199 | ]
200 | },
201 | {
202 | "cell_type": "code",
203 | "execution_count": null,
204 | "metadata": {},
205 | "outputs": [],
206 | "source": [
207 | "height"
208 | ]
209 | },
210 | {
211 | "cell_type": "code",
212 | "execution_count": null,
213 | "metadata": {},
214 | "outputs": [],
215 | "source": [
216 | "np.array([height, weight, gender, age])"
217 | ]
218 | },
219 | {
220 | "cell_type": "markdown",
221 | "metadata": {},
222 | "source": [
223 | "Create a dataframe that contains the information of our variables.\n",
224 | "- 1 catagorical type data: gender\n",
225 | "- 3 numeric type data: height, weight and age"
226 | ]
227 | },
228 | {
229 | "cell_type": "code",
230 | "execution_count": null,
231 | "metadata": {},
232 | "outputs": [],
233 | "source": [
234 | "df = pd.DataFrame(list(zip(height, weight, gender, age)), columns=['height (m)', 'weight (kg)', 'gender', 'age'])"
235 | ]
236 | },
237 | {
238 | "cell_type": "code",
239 | "execution_count": null,
240 | "metadata": {},
241 | "outputs": [],
242 | "source": [
243 | "df"
244 | ]
245 | },
246 | {
247 | "cell_type": "code",
248 | "execution_count": null,
249 | "metadata": {},
250 | "outputs": [],
251 | "source": [
252 | "df[df[\"gender\"] == \"Male\"].count()"
253 | ]
254 | },
255 | {
256 | "cell_type": "code",
257 | "execution_count": null,
258 | "metadata": {},
259 | "outputs": [],
260 | "source": [
261 | "df[df[\"gender\"] == \"Female\"].count()"
262 | ]
263 | },
264 | {
265 | "cell_type": "markdown",
266 | "metadata": {},
267 | "source": [
268 | "Now let's look at our random distribution and think about correlation between the data."
269 | ]
270 | },
271 | {
272 | "cell_type": "code",
273 | "execution_count": null,
274 | "metadata": {},
275 | "outputs": [],
276 | "source": [
277 | "hist = sns.FacetGrid(df, col=\"gender\")\n",
278 | "hist.map(sns.distplot, \"height (m)\", bins=20)\n",
279 | "hist.add_legend()"
280 | ]
281 | },
282 | {
283 | "cell_type": "code",
284 | "execution_count": null,
285 | "metadata": {},
286 | "outputs": [],
287 | "source": [
288 | "hist = sns.FacetGrid(df, col=\"gender\")\n",
289 | "hist.map(sns.distplot, \"weight (kg)\", bins=20)\n",
290 | "hist.add_legend()"
291 | ]
292 | },
293 | {
294 | "cell_type": "code",
295 | "execution_count": null,
296 | "metadata": {},
297 | "outputs": [],
298 | "source": [
299 | "hist = sns.FacetGrid(df, col=\"gender\")\n",
300 | "hist.map(sns.distplot, \"age\", bins=20)\n",
301 | "hist.add_legend()"
302 | ]
303 | },
304 | {
305 | "cell_type": "markdown",
306 | "metadata": {},
307 | "source": [
308 | "Compute the mean and median as a function of the gender.\n",
309 | "- use pandas groupby function"
310 | ]
311 | },
312 | {
313 | "cell_type": "code",
314 | "execution_count": null,
315 | "metadata": {},
316 | "outputs": [],
317 | "source": [
318 | "df.groupby(['gender']).mean()"
319 | ]
320 | },
321 | {
322 | "cell_type": "code",
323 | "execution_count": null,
324 | "metadata": {},
325 | "outputs": [],
326 | "source": [
327 | "df.groupby(['gender']).median()"
328 | ]
329 | },
330 | {
331 | "cell_type": "code",
332 | "execution_count": null,
333 | "metadata": {},
334 | "outputs": [],
335 | "source": [
336 | "sns.lmplot(x='weight (kg)', y='age', data=df, hue='gender', fit_reg=True, legend=True)\n",
337 | "plt.show()"
338 | ]
339 | },
340 | {
341 | "cell_type": "code",
342 | "execution_count": null,
343 | "metadata": {},
344 | "outputs": [],
345 | "source": [
346 | "sns.lmplot(x='height (m)', y='age', data=df, hue='gender', fit_reg=True, legend=True)\n",
347 | "plt.show()"
348 | ]
349 | },
350 | {
351 | "cell_type": "code",
352 | "execution_count": null,
353 | "metadata": {},
354 | "outputs": [],
355 | "source": [
356 | "sns.lmplot(x='height (m)', y='weight (kg)', data=df, hue='gender', fit_reg=True, legend=True)\n",
357 | "plt.show()"
358 | ]
359 | },
360 | {
361 | "cell_type": "code",
362 | "execution_count": null,
363 | "metadata": {},
364 | "outputs": [],
365 | "source": [
366 | "sns.jointplot(x='height (m)', y='age', data=df, kind='reg', color='b')\n",
367 | "plt.show()"
368 | ]
369 | },
370 | {
371 | "cell_type": "markdown",
372 | "metadata": {},
373 | "source": [
374 | "####\n",
375 | "Research Question: Is there a difference between the number of mean and women in the population?\n",
376 | "- Hypothesis Zero (aka Null Hypotheis): there is no difference\n",
377 | "- Hypothesis One: there is a difference\n",
378 | "\n",
379 | "In other words - is our sampling of the real population artifically skewed, or is it likely to be real?"
380 | ]
381 | },
382 | {
383 | "cell_type": "code",
384 | "execution_count": null,
385 | "metadata": {},
386 | "outputs": [],
387 | "source": [
388 | "sns.countplot(x=\"gender\", data=df)"
389 | ]
390 | },
391 | {
392 | "cell_type": "markdown",
393 | "metadata": {},
394 | "source": [
395 | "We must first determine a threshold for how low our statisical result is for use to have confidence that the Null Hypothesis is wrong - i.e. the probability value (P-value) that the Null Hypothesis is wrong.\n",
396 | "\n",
397 | "Common convention is 5%\n",
398 | "\n",
399 | "One-Sample Proportion Test"
400 | ]
401 | },
402 | {
403 | "cell_type": "code",
404 | "execution_count": null,
405 | "metadata": {},
406 | "outputs": [],
407 | "source": [
408 | "from scipy import stats\n",
409 | "\n",
410 | "test = np.array([60,40])\n",
411 | "stats.zscore(test)\n"
412 | ]
413 | },
414 | {
415 | "cell_type": "code",
416 | "execution_count": null,
417 | "metadata": {},
418 | "outputs": [],
419 | "source": [
420 | "df.groupby('gender')['age'].transform(stats.zscore)"
421 | ]
422 | },
423 | {
424 | "cell_type": "code",
425 | "execution_count": null,
426 | "metadata": {},
427 | "outputs": [],
428 | "source": [
429 | "x = (df.groupby('gender')['age'])"
430 | ]
431 | },
432 | {
433 | "cell_type": "code",
434 | "execution_count": null,
435 | "metadata": {},
436 | "outputs": [],
437 | "source": [
438 | "for i in x:\n",
439 | " print(i)"
440 | ]
441 | }
442 | ],
443 | "metadata": {
444 | "kernelspec": {
445 | "display_name": "Python 3",
446 | "language": "python",
447 | "name": "python3"
448 | },
449 | "language_info": {
450 | "codemirror_mode": {
451 | "name": "ipython",
452 | "version": 3
453 | },
454 | "file_extension": ".py",
455 | "mimetype": "text/x-python",
456 | "name": "python",
457 | "nbconvert_exporter": "python",
458 | "pygments_lexer": "ipython3",
459 | "version": "3.8.1"
460 | }
461 | },
462 | "nbformat": 4,
463 | "nbformat_minor": 2
464 | }
465 |
--------------------------------------------------------------------------------
/tips.csv:
--------------------------------------------------------------------------------
1 | "total_bill","tip","sex","smoker","day","time","size"
2 | 16.99,1.01,"Female","No","Sun","Dinner",2
3 | 10.34,1.66,"Male","No","Sun","Dinner",3
4 | 21.01,3.5,"Male","No","Sun","Dinner",3
5 | 23.68,3.31,"Male","No","Sun","Dinner",2
6 | 24.59,3.61,"Female","No","Sun","Dinner",4
7 | 25.29,4.71,"Male","No","Sun","Dinner",4
8 | 8.77,2,"Male","No","Sun","Dinner",2
9 | 26.88,3.12,"Male","No","Sun","Dinner",4
10 | 15.04,1.96,"Male","No","Sun","Dinner",2
11 | 14.78,3.23,"Male","No","Sun","Dinner",2
12 | 10.27,1.71,"Male","No","Sun","Dinner",2
13 | 35.26,5,"Female","No","Sun","Dinner",4
14 | 15.42,1.57,"Male","No","Sun","Dinner",2
15 | 18.43,3,"Male","No","Sun","Dinner",4
16 | 14.83,3.02,"Female","No","Sun","Dinner",2
17 | 21.58,3.92,"Male","No","Sun","Dinner",2
18 | 10.33,1.67,"Female","No","Sun","Dinner",3
19 | 16.29,3.71,"Male","No","Sun","Dinner",3
20 | 16.97,3.5,"Female","No","Sun","Dinner",3
21 | 20.65,3.35,"Male","No","Sat","Dinner",3
22 | 17.92,4.08,"Male","No","Sat","Dinner",2
23 | 20.29,2.75,"Female","No","Sat","Dinner",2
24 | 15.77,2.23,"Female","No","Sat","Dinner",2
25 | 39.42,7.58,"Male","No","Sat","Dinner",4
26 | 19.82,3.18,"Male","No","Sat","Dinner",2
27 | 17.81,2.34,"Male","No","Sat","Dinner",4
28 | 13.37,2,"Male","No","Sat","Dinner",2
29 | 12.69,2,"Male","No","Sat","Dinner",2
30 | 21.7,4.3,"Male","No","Sat","Dinner",2
31 | 19.65,3,"Female","No","Sat","Dinner",2
32 | 9.55,1.45,"Male","No","Sat","Dinner",2
33 | 18.35,2.5,"Male","No","Sat","Dinner",4
34 | 15.06,3,"Female","No","Sat","Dinner",2
35 | 20.69,2.45,"Female","No","Sat","Dinner",4
36 | 17.78,3.27,"Male","No","Sat","Dinner",2
37 | 24.06,3.6,"Male","No","Sat","Dinner",3
38 | 16.31,2,"Male","No","Sat","Dinner",3
39 | 16.93,3.07,"Female","No","Sat","Dinner",3
40 | 18.69,2.31,"Male","No","Sat","Dinner",3
41 | 31.27,5,"Male","No","Sat","Dinner",3
42 | 16.04,2.24,"Male","No","Sat","Dinner",3
43 | 17.46,2.54,"Male","No","Sun","Dinner",2
44 | 13.94,3.06,"Male","No","Sun","Dinner",2
45 | 9.68,1.32,"Male","No","Sun","Dinner",2
46 | 30.4,5.6,"Male","No","Sun","Dinner",4
47 | 18.29,3,"Male","No","Sun","Dinner",2
48 | 22.23,5,"Male","No","Sun","Dinner",2
49 | 32.4,6,"Male","No","Sun","Dinner",4
50 | 28.55,2.05,"Male","No","Sun","Dinner",3
51 | 18.04,3,"Male","No","Sun","Dinner",2
52 | 12.54,2.5,"Male","No","Sun","Dinner",2
53 | 10.29,2.6,"Female","No","Sun","Dinner",2
54 | 34.81,5.2,"Female","No","Sun","Dinner",4
55 | 9.94,1.56,"Male","No","Sun","Dinner",2
56 | 25.56,4.34,"Male","No","Sun","Dinner",4
57 | 19.49,3.51,"Male","No","Sun","Dinner",2
58 | 38.01,3,"Male","Yes","Sat","Dinner",4
59 | 26.41,1.5,"Female","No","Sat","Dinner",2
60 | 11.24,1.76,"Male","Yes","Sat","Dinner",2
61 | 48.27,6.73,"Male","No","Sat","Dinner",4
62 | 20.29,3.21,"Male","Yes","Sat","Dinner",2
63 | 13.81,2,"Male","Yes","Sat","Dinner",2
64 | 11.02,1.98,"Male","Yes","Sat","Dinner",2
65 | 18.29,3.76,"Male","Yes","Sat","Dinner",4
66 | 17.59,2.64,"Male","No","Sat","Dinner",3
67 | 20.08,3.15,"Male","No","Sat","Dinner",3
68 | 16.45,2.47,"Female","No","Sat","Dinner",2
69 | 3.07,1,"Female","Yes","Sat","Dinner",1
70 | 20.23,2.01,"Male","No","Sat","Dinner",2
71 | 15.01,2.09,"Male","Yes","Sat","Dinner",2
72 | 12.02,1.97,"Male","No","Sat","Dinner",2
73 | 17.07,3,"Female","No","Sat","Dinner",3
74 | 26.86,3.14,"Female","Yes","Sat","Dinner",2
75 | 25.28,5,"Female","Yes","Sat","Dinner",2
76 | 14.73,2.2,"Female","No","Sat","Dinner",2
77 | 10.51,1.25,"Male","No","Sat","Dinner",2
78 | 17.92,3.08,"Male","Yes","Sat","Dinner",2
79 | 27.2,4,"Male","No","Thur","Lunch",4
80 | 22.76,3,"Male","No","Thur","Lunch",2
81 | 17.29,2.71,"Male","No","Thur","Lunch",2
82 | 19.44,3,"Male","Yes","Thur","Lunch",2
83 | 16.66,3.4,"Male","No","Thur","Lunch",2
84 | 10.07,1.83,"Female","No","Thur","Lunch",1
85 | 32.68,5,"Male","Yes","Thur","Lunch",2
86 | 15.98,2.03,"Male","No","Thur","Lunch",2
87 | 34.83,5.17,"Female","No","Thur","Lunch",4
88 | 13.03,2,"Male","No","Thur","Lunch",2
89 | 18.28,4,"Male","No","Thur","Lunch",2
90 | 24.71,5.85,"Male","No","Thur","Lunch",2
91 | 21.16,3,"Male","No","Thur","Lunch",2
92 | 28.97,3,"Male","Yes","Fri","Dinner",2
93 | 22.49,3.5,"Male","No","Fri","Dinner",2
94 | 5.75,1,"Female","Yes","Fri","Dinner",2
95 | 16.32,4.3,"Female","Yes","Fri","Dinner",2
96 | 22.75,3.25,"Female","No","Fri","Dinner",2
97 | 40.17,4.73,"Male","Yes","Fri","Dinner",4
98 | 27.28,4,"Male","Yes","Fri","Dinner",2
99 | 12.03,1.5,"Male","Yes","Fri","Dinner",2
100 | 21.01,3,"Male","Yes","Fri","Dinner",2
101 | 12.46,1.5,"Male","No","Fri","Dinner",2
102 | 11.35,2.5,"Female","Yes","Fri","Dinner",2
103 | 15.38,3,"Female","Yes","Fri","Dinner",2
104 | 44.3,2.5,"Female","Yes","Sat","Dinner",3
105 | 22.42,3.48,"Female","Yes","Sat","Dinner",2
106 | 20.92,4.08,"Female","No","Sat","Dinner",2
107 | 15.36,1.64,"Male","Yes","Sat","Dinner",2
108 | 20.49,4.06,"Male","Yes","Sat","Dinner",2
109 | 25.21,4.29,"Male","Yes","Sat","Dinner",2
110 | 18.24,3.76,"Male","No","Sat","Dinner",2
111 | 14.31,4,"Female","Yes","Sat","Dinner",2
112 | 14,3,"Male","No","Sat","Dinner",2
113 | 7.25,1,"Female","No","Sat","Dinner",1
114 | 38.07,4,"Male","No","Sun","Dinner",3
115 | 23.95,2.55,"Male","No","Sun","Dinner",2
116 | 25.71,4,"Female","No","Sun","Dinner",3
117 | 17.31,3.5,"Female","No","Sun","Dinner",2
118 | 29.93,5.07,"Male","No","Sun","Dinner",4
119 | 10.65,1.5,"Female","No","Thur","Lunch",2
120 | 12.43,1.8,"Female","No","Thur","Lunch",2
121 | 24.08,2.92,"Female","No","Thur","Lunch",4
122 | 11.69,2.31,"Male","No","Thur","Lunch",2
123 | 13.42,1.68,"Female","No","Thur","Lunch",2
124 | 14.26,2.5,"Male","No","Thur","Lunch",2
125 | 15.95,2,"Male","No","Thur","Lunch",2
126 | 12.48,2.52,"Female","No","Thur","Lunch",2
127 | 29.8,4.2,"Female","No","Thur","Lunch",6
128 | 8.52,1.48,"Male","No","Thur","Lunch",2
129 | 14.52,2,"Female","No","Thur","Lunch",2
130 | 11.38,2,"Female","No","Thur","Lunch",2
131 | 22.82,2.18,"Male","No","Thur","Lunch",3
132 | 19.08,1.5,"Male","No","Thur","Lunch",2
133 | 20.27,2.83,"Female","No","Thur","Lunch",2
134 | 11.17,1.5,"Female","No","Thur","Lunch",2
135 | 12.26,2,"Female","No","Thur","Lunch",2
136 | 18.26,3.25,"Female","No","Thur","Lunch",2
137 | 8.51,1.25,"Female","No","Thur","Lunch",2
138 | 10.33,2,"Female","No","Thur","Lunch",2
139 | 14.15,2,"Female","No","Thur","Lunch",2
140 | 16,2,"Male","Yes","Thur","Lunch",2
141 | 13.16,2.75,"Female","No","Thur","Lunch",2
142 | 17.47,3.5,"Female","No","Thur","Lunch",2
143 | 34.3,6.7,"Male","No","Thur","Lunch",6
144 | 41.19,5,"Male","No","Thur","Lunch",5
145 | 27.05,5,"Female","No","Thur","Lunch",6
146 | 16.43,2.3,"Female","No","Thur","Lunch",2
147 | 8.35,1.5,"Female","No","Thur","Lunch",2
148 | 18.64,1.36,"Female","No","Thur","Lunch",3
149 | 11.87,1.63,"Female","No","Thur","Lunch",2
150 | 9.78,1.73,"Male","No","Thur","Lunch",2
151 | 7.51,2,"Male","No","Thur","Lunch",2
152 | 14.07,2.5,"Male","No","Sun","Dinner",2
153 | 13.13,2,"Male","No","Sun","Dinner",2
154 | 17.26,2.74,"Male","No","Sun","Dinner",3
155 | 24.55,2,"Male","No","Sun","Dinner",4
156 | 19.77,2,"Male","No","Sun","Dinner",4
157 | 29.85,5.14,"Female","No","Sun","Dinner",5
158 | 48.17,5,"Male","No","Sun","Dinner",6
159 | 25,3.75,"Female","No","Sun","Dinner",4
160 | 13.39,2.61,"Female","No","Sun","Dinner",2
161 | 16.49,2,"Male","No","Sun","Dinner",4
162 | 21.5,3.5,"Male","No","Sun","Dinner",4
163 | 12.66,2.5,"Male","No","Sun","Dinner",2
164 | 16.21,2,"Female","No","Sun","Dinner",3
165 | 13.81,2,"Male","No","Sun","Dinner",2
166 | 17.51,3,"Female","Yes","Sun","Dinner",2
167 | 24.52,3.48,"Male","No","Sun","Dinner",3
168 | 20.76,2.24,"Male","No","Sun","Dinner",2
169 | 31.71,4.5,"Male","No","Sun","Dinner",4
170 | 10.59,1.61,"Female","Yes","Sat","Dinner",2
171 | 10.63,2,"Female","Yes","Sat","Dinner",2
172 | 50.81,10,"Male","Yes","Sat","Dinner",3
173 | 15.81,3.16,"Male","Yes","Sat","Dinner",2
174 | 7.25,5.15,"Male","Yes","Sun","Dinner",2
175 | 31.85,3.18,"Male","Yes","Sun","Dinner",2
176 | 16.82,4,"Male","Yes","Sun","Dinner",2
177 | 32.9,3.11,"Male","Yes","Sun","Dinner",2
178 | 17.89,2,"Male","Yes","Sun","Dinner",2
179 | 14.48,2,"Male","Yes","Sun","Dinner",2
180 | 9.6,4,"Female","Yes","Sun","Dinner",2
181 | 34.63,3.55,"Male","Yes","Sun","Dinner",2
182 | 34.65,3.68,"Male","Yes","Sun","Dinner",4
183 | 23.33,5.65,"Male","Yes","Sun","Dinner",2
184 | 45.35,3.5,"Male","Yes","Sun","Dinner",3
185 | 23.17,6.5,"Male","Yes","Sun","Dinner",4
186 | 40.55,3,"Male","Yes","Sun","Dinner",2
187 | 20.69,5,"Male","No","Sun","Dinner",5
188 | 20.9,3.5,"Female","Yes","Sun","Dinner",3
189 | 30.46,2,"Male","Yes","Sun","Dinner",5
190 | 18.15,3.5,"Female","Yes","Sun","Dinner",3
191 | 23.1,4,"Male","Yes","Sun","Dinner",3
192 | 15.69,1.5,"Male","Yes","Sun","Dinner",2
193 | 19.81,4.19,"Female","Yes","Thur","Lunch",2
194 | 28.44,2.56,"Male","Yes","Thur","Lunch",2
195 | 15.48,2.02,"Male","Yes","Thur","Lunch",2
196 | 16.58,4,"Male","Yes","Thur","Lunch",2
197 | 7.56,1.44,"Male","No","Thur","Lunch",2
198 | 10.34,2,"Male","Yes","Thur","Lunch",2
199 | 43.11,5,"Female","Yes","Thur","Lunch",4
200 | 13,2,"Female","Yes","Thur","Lunch",2
201 | 13.51,2,"Male","Yes","Thur","Lunch",2
202 | 18.71,4,"Male","Yes","Thur","Lunch",3
203 | 12.74,2.01,"Female","Yes","Thur","Lunch",2
204 | 13,2,"Female","Yes","Thur","Lunch",2
205 | 16.4,2.5,"Female","Yes","Thur","Lunch",2
206 | 20.53,4,"Male","Yes","Thur","Lunch",4
207 | 16.47,3.23,"Female","Yes","Thur","Lunch",3
208 | 26.59,3.41,"Male","Yes","Sat","Dinner",3
209 | 38.73,3,"Male","Yes","Sat","Dinner",4
210 | 24.27,2.03,"Male","Yes","Sat","Dinner",2
211 | 12.76,2.23,"Female","Yes","Sat","Dinner",2
212 | 30.06,2,"Male","Yes","Sat","Dinner",3
213 | 25.89,5.16,"Male","Yes","Sat","Dinner",4
214 | 48.33,9,"Male","No","Sat","Dinner",4
215 | 13.27,2.5,"Female","Yes","Sat","Dinner",2
216 | 28.17,6.5,"Female","Yes","Sat","Dinner",3
217 | 12.9,1.1,"Female","Yes","Sat","Dinner",2
218 | 28.15,3,"Male","Yes","Sat","Dinner",5
219 | 11.59,1.5,"Male","Yes","Sat","Dinner",2
220 | 7.74,1.44,"Male","Yes","Sat","Dinner",2
221 | 30.14,3.09,"Female","Yes","Sat","Dinner",4
222 | 12.16,2.2,"Male","Yes","Fri","Lunch",2
223 | 13.42,3.48,"Female","Yes","Fri","Lunch",2
224 | 8.58,1.92,"Male","Yes","Fri","Lunch",1
225 | 15.98,3,"Female","No","Fri","Lunch",3
226 | 13.42,1.58,"Male","Yes","Fri","Lunch",2
227 | 16.27,2.5,"Female","Yes","Fri","Lunch",2
228 | 10.09,2,"Female","Yes","Fri","Lunch",2
229 | 20.45,3,"Male","No","Sat","Dinner",4
230 | 13.28,2.72,"Male","No","Sat","Dinner",2
231 | 22.12,2.88,"Female","Yes","Sat","Dinner",2
232 | 24.01,2,"Male","Yes","Sat","Dinner",4
233 | 15.69,3,"Male","Yes","Sat","Dinner",3
234 | 11.61,3.39,"Male","No","Sat","Dinner",2
235 | 10.77,1.47,"Male","No","Sat","Dinner",2
236 | 15.53,3,"Male","Yes","Sat","Dinner",2
237 | 10.07,1.25,"Male","No","Sat","Dinner",2
238 | 12.6,1,"Male","Yes","Sat","Dinner",2
239 | 32.83,1.17,"Male","Yes","Sat","Dinner",2
240 | 35.83,4.67,"Female","No","Sat","Dinner",3
241 | 29.03,5.92,"Male","No","Sat","Dinner",3
242 | 27.18,2,"Female","Yes","Sat","Dinner",2
243 | 22.67,2,"Male","Yes","Sat","Dinner",2
244 | 17.82,1.75,"Male","No","Sat","Dinner",2
245 | 18.78,3,"Female","No","Thur","Dinner",2
246 |
--------------------------------------------------------------------------------
\n", 46 | "\n", 47 | "Okay, let's revisit the idea of polynomials, and slowly build up our understanding together." 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": null, 53 | "id": "fe395a92", 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "import matplotlib.pyplot as plt\n", 58 | "import numpy as np" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "id": "f6c6139e", 64 | "metadata": {}, 65 | "source": [ 66 | "1. Create a one dimensional (i.e., has one variable...x) polynomials using a function\n", 67 | " - using different coefficient(s)\n", 68 | " - evaluate the resulting function using a variable (i.e., x) value of 2.\n", 69 | "\n", 70 | "To do this **concisely** and to **reduce error**, let's create a user-defined function that\n", 71 | "\n", 72 | "a) generates a polynomial using provided coefficients,\n", 73 | "\n", 74 | "b) prints out the polynomial equation and\n", 75 | "\n", 76 | "c) its value when evaluated at x=2\n", 77 | "\n", 78 | "**Note**: I do not provide a docstring comment to `isinstance` within this function to simplify the teaching message. Normally, you should include them." 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": null, 84 | "id": "055068d1", 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "def my_poly1d(coeff: int=None):\n", 89 | " \n", 90 | " polynomial = np.poly1d(coeff)\n", 91 | " \n", 92 | " print(f'The value of the polynomial')\n", 93 | " print()\n", 94 | " print(f'{polynomial} when x=2: {polynomial(2)}')" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "id": "2763e62c", 100 | "metadata": {}, 101 | "source": [ 102 | "#### One coefficient (not very exciting): [M] --> $M$" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": null, 108 | "id": "cd0c38b6", 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "coefficients = [3]\n", 113 | "my_poly1d(coefficients)" 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "id": "445825fa", 119 | "metadata": {}, 120 | "source": [ 121 | "Notice that the polynomial generated does not have an 'x' term...the equation is just a constant.\n", 122 | "\n", 123 | "That means the y-values of the \"polynomial\" equation \"3\" is 3 for all x-values:" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": null, 129 | "id": "e6ad295d", 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "plt.plot()\n", 134 | "plt.hlines(xmin=0, xmax=10, y=3)\n", 135 | "plt.show()" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "id": "9d9dff3d", 141 | "metadata": {}, 142 | "source": [ 143 | "#### Two coefficients: [M, N] --> $Mx + N$\n", 144 | "- Note how the M shifts to the x" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": null, 150 | "id": "39a33a98", 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "coefficients = [1, 2]\n", 155 | "my_poly1d(coefficients)" 156 | ] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "id": "a737dfe5", 161 | "metadata": {}, 162 | "source": [ 163 | "##### Including a negative coefficients: [1, -1]" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "id": "6e46edcd", 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "coefficients = [1, -1]\n", 174 | "my_poly1d(coefficients)" 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "id": "c8e49f19", 180 | "metadata": {}, 181 | "source": [ 182 | "#### Three coefficients: [M, N, O] --> $Mx^2 + Nx + O$" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": null, 188 | "id": "a5389129", 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "# coefficients = [x^2, x, constant]\n", 193 | "coefficients = [-4, 1, -2]\n", 194 | "my_poly1d(coefficients)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "id": "1dd9d533", 200 | "metadata": {}, 201 | "source": [ 202 | "#### Four coefficients: [M, N, O, P] --> $Mx^3 + Nx^2 + Ox + P$" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "id": "cc678750", 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "coefficients = [5, -4, 1, -1]\n", 213 | "my_poly1d(coefficients)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "id": "428af698", 219 | "metadata": {}, 220 | "source": [ 221 | "#### Access the Polynomial's Coefficients" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": null, 227 | "id": "78a7a86a", 228 | "metadata": {}, 229 | "outputs": [], 230 | "source": [ 231 | "polynomial = np.poly1d(coefficients)\n", 232 | "\n", 233 | "polynomial.coefficients" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "id": "668701f3", 239 | "metadata": {}, 240 | "source": [ 241 | "#### Access the Polynomial's Order" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "id": "6c8f5524", 248 | "metadata": {}, 249 | "outputs": [], 250 | "source": [ 251 | "polynomial.order" 252 | ] 253 | }, 254 | { 255 | "cell_type": "markdown", 256 | "id": "3071942b", 257 | "metadata": {}, 258 | "source": [ 259 | "## Math with polynomials\n", 260 | "\n", 261 | "#### Square of a polynomial\n", 262 | "\n", 263 | "The square of a polynomial\n", 264 | "\n", 265 | "\n", 266 | "$\n", 267 | "\\begin{align}\n", 268 | "(a + b)^2 &= (a + b)(a + b)\\\\\n", 269 | " &= a^2 + 2ab + b^2\n", 270 | "\\end{align}\n", 271 | "$\n", 272 | "\n", 273 | "**Example**:\n", 274 | "\n", 275 | "`np.poly1d([2, 1])` $\\rightarrow$ ${2x + 1}$\n", 276 | "\n", 277 | "Thus we when square this polynomial:\n", 278 | "\n", 279 | "$(2x + 1)(2x + 1) = 4x^2 + 4x + 1$\n", 280 | "\n", 281 | "So, how do we code this:" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": null, 287 | "id": "2b293c77", 288 | "metadata": {}, 289 | "outputs": [], 290 | "source": [ 291 | "coefficients = [2, 1]\n", 292 | "\n", 293 | "polynomial = np.poly1d(coefficients)\n", 294 | "\n", 295 | "poly_square = polynomial**2\n", 296 | "\n", 297 | "print(poly_square)" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "id": "2d91a4c8", 303 | "metadata": {}, 304 | "source": [ 305 | "Now evaluate the squared polynomial at x=2\n", 306 | "\n", 307 | "(i.e., $ (2x + 1)^2 \\text{ at }x=2 \\rightarrow 4*2^2 + 4*2 + 1$)" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": null, 313 | "id": "eafb76e6", 314 | "metadata": {}, 315 | "outputs": [], 316 | "source": [ 317 | "poly_square(2)" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "id": "8e6e0878", 323 | "metadata": {}, 324 | "source": [ 325 | "#### A polynomial cubed" 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": null, 331 | "id": "21e231c4", 332 | "metadata": {}, 333 | "outputs": [], 334 | "source": [ 335 | "print(polynomial**3)" 336 | ] 337 | }, 338 | { 339 | "cell_type": "markdown", 340 | "id": "e60ebef5", 341 | "metadata": {}, 342 | "source": [ 343 | "Summary so far:\n", 344 | "1. We reviewed what a polynomial is,\n", 345 | "2. We learned that `poly1d` allows us to create n-degree polynomials that depend on x data\n", 346 | "3. We learned that we can do some polynomial math easily\n", 347 | "\n", 348 | "
\n", 349 | "\n", 350 | "What happens though when you have **x- and y-data** that follow a **polynomial form**, but you **don't know** the **coefficients** to define the polynomial?\n", 351 | "\n", 352 | "\n", 353 | "## Numpy's `polyfit` Function\n", 354 | "\n", 355 | "Fit a polynomial of a specified degree to specific data (x, y).\n", 356 | "\n", 357 | "Returns a \"vector\" of coefficients that minimizes the squared error.\n", 358 | "\n", 359 | "- https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html\n", 360 | "\n", 361 | "First, let's create some **nonideal data** that follows a **cubic polynomial** form." 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": null, 367 | "id": "9f4b5b82", 368 | "metadata": {}, 369 | "outputs": [], 370 | "source": [ 371 | "x_values = [-12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1,\n", 372 | " 0, 1, 2, 3, 4, 5, 6, 7, 8]\n", 373 | "\n", 374 | "y_values = [2000, 1500, 1400, 700, 600, 500, 300, 100, 70, 30, 20, 5,\n", 375 | " 4, 7, 10, 6, -10, -50, -200, -220, -400]" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "execution_count": null, 381 | "id": "2ea722c2", 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [ 385 | "plt.plot()\n", 386 | "plt.plot(x_values, y_values, 'o', markersize=15)\n", 387 | "plt.show()" 388 | ] 389 | }, 390 | { 391 | "cell_type": "markdown", 392 | "id": "f833017f", 393 | "metadata": {}, 394 | "source": [ 395 | "Now, `polyfit` will fit a n-degree polynomial (i.e., 3$^{rd}$ degree) to the provided x- and y-data.\n", 396 | "\n", 397 | "The `polyfit` function returns coefficients.\n", 398 | "\n", 399 | "Recall from above: four coefficients are needed to define a 3$^{rd}$ degree polynomial\n", 400 | "\n", 401 | "[M, N, O, P] --> $Mx^3 + Nx^2 + Ox + P$" 402 | ] 403 | }, 404 | { 405 | "cell_type": "code", 406 | "execution_count": null, 407 | "id": "3c890c78", 408 | "metadata": {}, 409 | "outputs": [], 410 | "source": [ 411 | "coefficients = np.polyfit(x_values, y_values, 3)\n", 412 | "coefficients" 413 | ] 414 | }, 415 | { 416 | "cell_type": "markdown", 417 | "id": "203b43de", 418 | "metadata": {}, 419 | "source": [ 420 | "Therefore, we have the following polynomial (rounded coefficients):\n", 421 | "\n", 422 | "$-1.046*x^3 + 1.696*x^2 + 3.413*x + 2.793$\n", 423 | "\n", 424 | "Now we can use Numpy's `poly1d` to encode this polynomial exactly:" 425 | ] 426 | }, 427 | { 428 | "cell_type": "code", 429 | "execution_count": null, 430 | "id": "1f37527a", 431 | "metadata": {}, 432 | "outputs": [], 433 | "source": [ 434 | "cubic_polynomial = np.poly1d(coefficients)\n", 435 | "print(cubic_polynomial)" 436 | ] 437 | }, 438 | { 439 | "cell_type": "markdown", 440 | "id": "d7f69413", 441 | "metadata": {}, 442 | "source": [ 443 | "Using this cubic polynomial, we can generate \"ideal\" y-data given a range of x-data values." 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": null, 449 | "id": "819fc8e1", 450 | "metadata": {}, 451 | "outputs": [], 452 | "source": [ 453 | "y_ideal_cubic = cubic_polynomial(x_values)" 454 | ] 455 | }, 456 | { 457 | "cell_type": "markdown", 458 | "id": "375854cb", 459 | "metadata": {}, 460 | "source": [ 461 | "Plot both the original data and the data from the fitted cubic polynomial:" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": null, 467 | "id": "72511232", 468 | "metadata": {}, 469 | "outputs": [], 470 | "source": [ 471 | "plt.plot()\n", 472 | "plt.plot(x_values, y_values, 'o', markersize=15)\n", 473 | "plt.plot(x_values, y_ideal_cubic, linewidth=5)\n", 474 | "plt.show()" 475 | ] 476 | }, 477 | { 478 | "cell_type": "code", 479 | "execution_count": null, 480 | "id": "49a71e37-29f1-401e-9995-cbcb37630788", 481 | "metadata": {}, 482 | "outputs": [], 483 | "source": [] 484 | } 485 | ], 486 | "metadata": { 487 | "kernelspec": { 488 | "display_name": "Python 3 (ipykernel)", 489 | "language": "python", 490 | "name": "python3" 491 | }, 492 | "language_info": { 493 | "codemirror_mode": { 494 | "name": "ipython", 495 | "version": 3 496 | }, 497 | "file_extension": ".py", 498 | "mimetype": "text/x-python", 499 | "name": "python", 500 | "nbconvert_exporter": "python", 501 | "pygments_lexer": "ipython3", 502 | "version": "3.11.9" 503 | } 504 | }, 505 | "nbformat": 4, 506 | "nbformat_minor": 5 507 | } 508 | -------------------------------------------------------------------------------- /operations.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "#
\n", 23 | "
" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "Answer = 4.4 (we will see how this done below)\n", 31 | "\n", 32 | "---\n", 33 | "# Operators\n", 34 | "\n", 35 | "In Python, there are different types of **operators** (special symbols) that operate on different values. Some of the basic operators include:\n", 36 | "\n", 37 | "- arithmetic operators\n", 38 | " - **`+`** (addition)\n", 39 | " - **`-`** (subtraction)\n", 40 | " - **`*`** (multiplication)\n", 41 | " - **`/`** (division)\n", 42 | " - __`**`__ (exponent)\n", 43 | "\n", 44 | "\n", 45 | "- assignment operators\n", 46 | " - **`=`** (assign a value)\n", 47 | " - **`+=`** (add and re-assign; increment)\n", 48 | " - **`-=`** (subtract and re-assign; decrement)\n", 49 | " - **`*=`** (multiply and re-assign)\n", 50 | "\n", 51 | "\n", 52 | "- comparison operators (return either `True` or `False`; booleans)\n", 53 | " - **`==`** (equal to)\n", 54 | " - **`!=`** (not equal to)\n", 55 | " - **`<`** (less than)\n", 56 | " - **`<=`** (less than or equal to)\n", 57 | " - **`>`** (greater than)\n", 58 | " - **`>=`** (greater than or equal to)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "---\n", 66 | "## Order of Operation\n", 67 | "\n", 68 | "When multiple operators are used in a single expression, **operator precedence** determines which parts of the expression are evaluated in which order.\n", 69 | "\n", 70 | "Operators with higher precedence are evaluated first.\n", 71 | "\n", 72 | "Operators with the same precedence are evaluated from **left to right**.\n", 73 | "\n", 74 | "Precedence importance ordering:\n", 75 | "1. `()` parentheses, for grouping\n", 76 | "2. `**` exponent\n", 77 | "3. `*`, `/` multiplication and division\n", 78 | "4. `+`, `-` addition and subtraction\n", 79 | "5. `==`, `!=`, `<`, `<=`, `>`, `>=` comparisons\n", 80 | "\n", 81 | "\n", 82 | "#### Additional information:\n", 83 | "\n", 84 | "1. https://docs.python.org/3/reference/expressions.html#operator-precedence\n", 85 | "\n", 86 | "2. https://en.wikipedia.org/wiki/Order_of_operations" 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "metadata": {}, 92 | "source": [ 93 | "#### Example 1: 2 + 3 * 4 / 5\n", 94 | "\n", 95 | "> Step 1. $3 * 4 = 12$ (**Notice**: `*` and `/` have the same precedence, but `*` is farthest to the left)\n", 96 | "\n", 97 | "> Step 2. $12 / 5 = 2.2$\n", 98 | "\n", 99 | "> Step 3. $2 + 2.2 = 4.4$\n", 100 | "\n", 101 | "Now, let's see what Python gives:" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "equation = 2.0 + 3.0 * 4.0 / 5.0\n", 111 | "\n", 112 | "print(\"2.0 + 3.0 * 4.0 / 5.0 = {0}\".format(equation))" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "Next, let's force a specific sequence of actions to occur by using `()`.\n", 120 | "\n", 121 | "#### Example 2: (2 + 3) * 4 / 5" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "equation = (2.0 + 3.0) * 4.0 / 5.0\n", 131 | "\n", 132 | "print(\"(2.0 + 3.0) * 4.0 / 5.0 = {0}\".format(equation))\n", 133 | "\n", 134 | "print('''\n", 135 | " i.e. step 1: 2.0 + 3.0 = 5.0\n", 136 | " step 2: 5.0 * 4.0 = 20.0 since * is further left than /\n", 137 | " step 3: 20.0 / 5.0 = 4.0\n", 138 | " ''')" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "#### Example 3: 2 + 3 * 4 ** 2 / 5" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "equation = 2 + 3 * 4 ** 2 / 5\n", 155 | "\n", 156 | "print(\"2 + 3 * 4 ** 2 / 5 = \", equation)\n", 157 | "\n", 158 | "print(\"\"\"\n", 159 | " i.e. 4**2 = 16\n", 160 | " 3*16 = 48\n", 161 | " 48/5 = 9.6\n", 162 | " 2+9.6 = 11.6\n", 163 | " \"\"\")" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "#### Example 4: 2 + (3 * 4) ** 2 / 5" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [ 179 | "equation = 2 + (3 * 4) ** 2 / 5\n", 180 | "\n", 181 | "print(\"2 + (3 * 4) ** 2 / 5 = \", equation)\n", 182 | "\n", 183 | "print(\"\"\"\n", 184 | " i.e. 3*4 = 12\n", 185 | " 12**2 = 144\n", 186 | " 144/5 = 28.8\n", 187 | " 2+28.8 = 30.8\n", 188 | " \"\"\")" 189 | ] 190 | }, 191 | { 192 | "cell_type": "markdown", 193 | "metadata": {}, 194 | "source": [ 195 | "**Take-home Message**: Be careful in encoding mathematical equations so that you do introduce and error.\n", 196 | "\n", 197 | "---\n", 198 | "## Increment (by reassigning variable)\n", 199 | "- right-hand-side happens first, and then the left-hand-side\n", 200 | "\n", 201 | "In the following assignment statements\n", 202 | "- evaluation of the right-hand-side is done first, then\n", 203 | "- it is assigned to the variable" 204 | ] 205 | }, 206 | { 207 | "cell_type": "markdown", 208 | "metadata": {}, 209 | "source": [ 210 | "Simple demonstration - counting plants, starting with 1" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": null, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "planets = 1\n", 220 | "print(planets)" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "Now lets add 1 more to the number of planets:\n", 228 | "- notice how `planets + 1` is computed first\n", 229 | "- then the new value is assigned to the variable `planets`" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": null, 235 | "metadata": {}, 236 | "outputs": [], 237 | "source": [ 238 | "planets = planets + 1\n", 239 | "print(planets)" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "Alternatively, we can use `+= 1` instead of `planets + 1`:" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": null, 252 | "metadata": {}, 253 | "outputs": [], 254 | "source": [ 255 | "\n", 256 | "planets += 1\n", 257 | "print(planets)" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "metadata": {}, 263 | "source": [ 264 | "`+=` will increment other numbers too" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": null, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [ 273 | "planets += 5\n", 274 | "print(planets)" 275 | ] 276 | }, 277 | { 278 | "cell_type": "markdown", 279 | "metadata": {}, 280 | "source": [ 281 | "---\n", 282 | "## Booleans\n", 283 | "- used through comparison operators (e.g. `<=`)\n", 284 | "\n", 285 | "Demo Scenerio - you have to discover more than 10 planets before you graduate." 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": null, 291 | "metadata": {}, 292 | "outputs": [], 293 | "source": [ 294 | "total_planets = 10\n", 295 | "print(total_planets < planets)" 296 | ] 297 | }, 298 | { 299 | "cell_type": "markdown", 300 | "metadata": {}, 301 | "source": [ 302 | "### Why could this be interesting?\n", 303 | "- you can create conditions" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": null, 309 | "metadata": {}, 310 | "outputs": [], 311 | "source": [ 312 | "planets = 0\n", 313 | "\n", 314 | "if (total_planets < planets) == False:\n", 315 | " print('You can not graduate yet - get back to work!')\n", 316 | "else:\n", 317 | " print('Congratulations - it is time to move on!')" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "metadata": {}, 323 | "source": [ 324 | "- you can create loops" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": null, 330 | "metadata": {}, 331 | "outputs": [], 332 | "source": [ 333 | "planets = 0\n", 334 | "graduating = False\n", 335 | "\n", 336 | "while not graduating:\n", 337 | " if (total_planets < planets) == False:\n", 338 | " print('You can not graduate yet. Number of planets discovered: {0}'.format(planets))\n", 339 | " planets += 1\n", 340 | " else:\n", 341 | " print('Congratulations - it is time to move on!')\n", 342 | " graduating = True" 343 | ] 344 | } 345 | ], 346 | "metadata": { 347 | "kernelspec": { 348 | "display_name": "Python 3 (ipykernel)", 349 | "language": "python", 350 | "name": "python3" 351 | }, 352 | "language_info": { 353 | "codemirror_mode": { 354 | "name": "ipython", 355 | "version": 3 356 | }, 357 | "file_extension": ".py", 358 | "mimetype": "text/x-python", 359 | "name": "python", 360 | "nbconvert_exporter": "python", 361 | "pygments_lexer": "ipython3", 362 | "version": "3.8.17" 363 | } 364 | }, 365 | "nbformat": 4, 366 | "nbformat_minor": 2 367 | } 368 | -------------------------------------------------------------------------------- /scientific_figures.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "#