{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "Y30FK5CHTRZ9" }, "source": [ "#**Workshop 5: Functions**\n" ] }, { "cell_type": "markdown", "metadata": { "id": "cMHKRoQeTVbj" }, "source": [ "##**Objectives**\n", "At the end of this workshop, you will be able to:\n", "* Explain the purpose of functions\n", "* Write a function\n", "* Call a function\n", "* Identify the difference between local and global variables\n", "* Use help to display documentation for built-in functions.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "L1e6_Zo-Tj9O" }, "source": [ "##**What is a function? Why should I bother writing them?**\n", "A group of statements that exist within a program for the purpose of performing a specific task\n", "Many benefits:\n", "* Simpler code\n", "* Better testing\n", "* Easier facilitation of teamwork\n", "* Faster development\n" ] }, { "cell_type": "markdown", "metadata": { "id": "3jMP7smFaXF3" }, "source": [ "##**Writing a function**\n", "\n", "\n", "\n", "```\n", "def function_name():\n", " Statement\n", " Statement\n", " Etc\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "pI842Laiam5z" }, "source": [ "##**Calling a function**\n", "\n", "\n", "\n", "```\n", "Function_name()\n", "```\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "9oerHazwZT8G" }, "source": [ "def message():\n", "print('Sometimes I buy flour from King Arthur')\n", "print(\"Sometimes I don’t\")\n", "\n", "message()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "b1j5JwcQUCaj" }, "source": [ "##**You can define as many functions as needed in a program**\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "QQ6uq9G3UQnE" }, "source": [ "def main():\n", " print('I have a message for you.')\n", " message()\n", " print(\"good bye!\")\n", "\n", "def message():\n", " print(\"Sometimes I buy flour from King Arthur\")\n", " print(\"Sometimes I don’t\")\n", "\n", "main()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hmtOIazJUgke" }, "source": [ "##**Use Pseudo code to stay organized**\n", "\n", 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] }, { "cell_type": "markdown", "metadata": { "id": "UVSQEXf2t1kC" }, "source": [ "Below is the python code for the above pseudo code. If it wasn't chunked into functions, it would be harder to find all of the error messages and later debut (don't worry there are no errors with this code!)\n" ] }, { "cell_type": "code", "metadata": { "id": "PcyunWRrUquf" }, "source": [ "#generate random numbers\n", "import random\n", "\n", "#constants for menu choices\n", "ADDITION = 1\n", "SUBTRACTION = 2\n", "MULTIPLICATION = 3\n", "DIVISION = 4\n", "RANDOM_PROBLEM = 5\n", "QUIT = 6\n", "\n", "def main():\n", " #menu: set value of choice to zero\n", " selection = 0\n", "\n", " #pick easier or hard variables\n", " easy = 1\n", " hard = 2\n", " level = 0\n", "\n", " #display menu\n", " while selection != QUIT:\n", " display_menu()\n", "\n", " #get user choice\n", " selection = int(input(\"Select your problem set: \"))\n", "\n", " #perform choice\n", " while level == 0:\n", " if selection == ADDITION:\n", " print(\"Select '1' for easy problems or '2' for hard problems\")\n", " level = int(input(\"Would you like easy or hard problems?\"))\n", " if level == easy:\n", " easy_add()\n", " elif level == hard:\n", " hard_add()\n", " else:\n", " level = 0\n", " print('Error: Invalid selection.')\n", " elif selection == SUBTRACTION:\n", " print(\"Select '1' for easy problems or '2' for hard problems\")\n", " level = int(input(\"Would you like easy or hard problems?\"))\n", " if level == easy:\n", " easy_sub()\n", " elif level == hard:\n", " hard_sub()\n", " else:\n", " level = 0\n", " print('Error: Invalid selection.')\n", " elif selection == MULTIPLICATION:\n", " print(\"Select '1' for easy problems or '2' for hard problems\")\n", " difficulty = int(input(\"Would you like easy or hard problems?\"))\n", " if level == easy:\n", " easy_mult()\n", " elif level == hard:\n", " hard_mult()\n", " else:\n", " level = 0\n", " print('Error: Invalid selection.')\n", " elif selection == DIVISION:\n", " print(\"Select '1' for easy problems or '2' for hard problems\")\n", " level = int(input(\"Would you like easy or hard problems?\"))\n", " if level == easy:\n", " easy_div()\n", " elif level == hard:\n", " hard_div()\n", " else:\n", " level = 0\n", " print('Error: Invalid selection.')\n", " elif selection == RANDOM_PROBLEM:\n", " print(\"Select '1' for easy problems or '2' for hard problems\")\n", " level = int(input(\"Would you like easy or hard problems?\"))\n", " if level == easy:\n", " easy_random()\n", " elif level == hard:\n", " hard_random()\n", " else:\n", " level = 0\n", " print('Error: Invalid selection.')\n", " else:\n", " print('Error: Invalid selection.')\n", " selection = int(input(\"Select your problem set: \"))\n", "\n", " print()\n", " print('Thanks for playing!')\n", "#desplay menu function\n", "def display_menu():\n", " print('Menu Options:')\n", " print('1.) Addition problems')\n", " print('2.) Subtraction problems')\n", " print('3.) Multiplication problems')\n", " print('4.) Division problems')\n", " print('5.) Random problems')\n", " print('6.) Quit')\n", "\n", "#addition functions\n", "#easy addition problems\n", "def easy_add():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,10)\n", " num2 = random.randint(1,10)\n", " num3 = num1 + num2\n", " print(num1, \"+\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "#hard addition problems\n", "def hard_add():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,100)\n", " num2 = random.randint(1,100)\n", " num3 = num1 + num2\n", " print(num1, \"+\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "#subtraction functions\n", "\n", "#easy subtraction probelms\n", "def easy_sub():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,10)\n", " num2 = random.randint(1,10)\n", "\n", " #make sure no neg numbers\n", " if num1 > num2:\n", " num3 = num1 - num2\n", " print(num1, '-', num2, '=')\n", " else:\n", " num3 = num2 - num1\n", " print(num2, '-', num1, '=')\n", "\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", "\n", "#hard subtraction problems\n", "def hard_sub():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,100)\n", " num2 = random.randint(1,100)\n", "\n", " #make sure no neg numbers\n", " if num1 > num2:\n", " num3 = num1 - num2\n", " print(num1, '-', num2, '=')\n", " else:\n", " num3 = num2 - num1\n", " print(num2, '-', num1, '=')\n", "\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", "\n", "#multiplication functions\n", "\n", "#easy multiplcation problems\n", "def easy_mult():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,10)\n", " num2 = random.randint(1,10)\n", " num3 = num1 * num2\n", " print(num1, \"x\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "#hard multiplication problems\n", "def hard_mult():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,100)\n", " num2 = random.randint(1,100)\n", " num3 = num1 * num2\n", " print(num1, \"x\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "#division fuctions\n", "\n", "#easy division problems\n", "def easy_div():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,10)\n", " num2 = random.randint(1,10)\n", " num3 = (num1 * num2)/num2\n", " print()\n", " print((num1 * num2), \"/\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "def hard_div():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,100)\n", " num2 = random.randint(1,100)\n", " num3 = (num1 * num2)/num2\n", " print((num1 * num2), \"/\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "#random problem functions\n", "def easy_random():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,10)\n", " num2 = random.randint(1,10)\n", " prob = random.randint(1,4)\n", " #addition\n", " if prob == 1:\n", " num3 = num1 + num2\n", " print(num1, \"+\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " #subtraction\n", " elif prob == 2:\n", "\n", " if num1 > num2:\n", " num3 = num1 - num2\n", " print(num1, '-', num2, '=')\n", " else:\n", " num3 = num2 - num1\n", " print(num2, '-', num1, '=')\n", "\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " #multiplication\n", " elif prob == 3:\n", " num3 = num1 * num2\n", " print(num1, \"x\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " #division\n", " else:\n", " num3 = (num1 * num2)/num2\n", " print()\n", " print((num1 * num2), \"/\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "#hard random problems\n", "def hard_random():\n", " attempts = 10\n", " correct = 0\n", " for total in range(attempts):\n", " num1 = random.randint(1,100)\n", " num2 = random.randint(1,100)\n", " prob = random.randint(1,4)\n", " #addition\n", " if prob == 1:\n", " num3 = num1 + num2\n", " print(num1, \"+\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " #subtraction\n", " elif prob == 2:\n", "\n", " if num1 > num2:\n", " num3 = num1 - num2\n", " print(num1, '-', num2, '=')\n", " else:\n", " num3 = num2 - num1\n", " print(num2, '-', num1, '=')\n", "\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " #multiplication\n", " elif prob == 3:\n", " num3 = num1 * num2\n", " print(num1, \"x\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " #division\n", " else:\n", " num3 = (num1 * num2)/num2\n", " print()\n", " print((num1 * num2), \"/\", num2, \"=\")\n", " print()\n", " total = int(input(\"What is the answer?\"))\n", " if total == num3:\n", " correct+=1\n", " print()\n", " correct_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", " else:\n", " print()\n", " wrong_random()\n", " print()\n", " print(\"******************\")\n", " print()\n", "\n", " print('Grade:', (correct/attempts)*100)\n", " print()\n", " print(\"******************\")\n", "\n", "#random message generators\n", "def correct_random():\n", " correct_answer = [\"That's right!\", \"Awesome!\", \"Great work!\", \"Right on!\", \"Keep it up!\"]\n", " print(random.choice(correct_answer))\n", "\n", "def wrong_random():\n", " wrong_answer = [\"Rats!\", \"WOMP!\", \"Try again!\", \"Wrong!\", \"You'll get it next time!\"]\n", " print(random.choice(wrong_answer))\n", "\n", "#call the main function\n", "main()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6rpcdN2NYDWq" }, "source": [ "##**Local variables vs Global Variables**\n", "Local variable: used inside a function and cannot be accessed by statements that are outside the function\n", "\n", "Global variable: live outside functions and are accessible to all functions in a program file, this is not best practice\n" ] }, { "cell_type": "code", "metadata": { "id": "MMTHgGkgYHYt" }, "source": [ "#Local variable example, results in error message\n", "\n", "def main():\n", " get_name()\n", " print('Hello', name)\n", "\n", "def get_name():\n", " name = input('Enter your name: ')\n", "\n", "main()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "CMXipiNwc85t" }, "source": [ "#Global variable example, results in working code\n", "\n", "number = 0\n", "\n", "def main():\n", " global number\n", " number = int(input('Enter a number: '))\n", " show_number()\n", "\n", "def show_number():\n", " print('the number you entered is', number)\n", "\n", "main()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "dGXgzKmxYe7M" }, "source": [ "##**Use “return” to use a local variable globally**\n", "\n", "Instead of using global variables, it’s better to use a local variable globally.\n" ] }, { "cell_type": "code", "metadata": { "id": "shet1Bfkf5ka" }, "source": [ "def main():\n", " first_age = int(input('enter your age:'))\n", " second_age = int(input('enter your age:'))\n", " total = sum(first_age, second_age)\n", " print('together you are', total, 'years old.')\n", "\n", "def sum(num1, num2):\n", " result = num1 + num2\n", " return result\n", "\n", "main()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "u9iP0HCQcqPM" }, "source": [ "##**Built in Functions**\n", "\n", "* print() - prints a statement\n", "* max() - finds the largest value of one or more values\n", "* min() - finds the smallest value of one or more values\n", "* round() - returns a floating point number that is a rounded version of the specified number\n", "\n", "More found here: https://docs.python.org/3/library/functions.html" ] }, { "cell_type": "code", "metadata": { "id": "IKXHZj58c0IL" }, "source": [ "print(max(1, 2, 3))\n", "print(min('a', 'A', '0'))\n", "print(max(1, 'a'))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bsNfP9AYeYf7" }, "source": [ "##**Use \"help\" to get help for a function**" ] }, { "cell_type": "code", "metadata": { "id": "_tvVkCedefP1" }, "source": [ "help(max)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "5uEJmOC2ehAK" }, "source": [ "help(print)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Sbojgd0Lfwc8" }, "source": [ "##**Try it yourself!**\n", "\n", "Take a look at the try it yourselves from the previous weeks and convert a couple of them into functions." ] }, { "cell_type": "code", "metadata": { "id": "vrQaiI-df2rK" }, "source": [], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "YPm0R4_xeioc" }, "source": [ "##**Looking for more practice?**\n", "\n", "W3Schools functions: https://www.w3schools.com/python/python_functions.asp\n", "\n", "Software Carpentry Built in functions: http://swcarpentry.github.io/python-novice-gapminder/04-built-in/index.html\n", "\n", "Software Carpentry Functions: http://swcarpentry.github.io/python-novice-gapminder/16-writing-functions/index.html\n", "\n", "Try programming a game! https://inventwithpython.com/invent4thed/" ] }, { "cell_type": "markdown", "metadata": { "id": "yRVlFpi2ep7J" }, "source": [ "##**Sources**\n", "1. Gaddis T. Starting out with Python. Third edition. Pearson; 2014.\n" ] } ] }