activities/day3_functions_and_data_structures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "360c13df-200c-4a07-afd2-ca8cfb7749f2",
   "metadata": {},
   "source": [
    "# UQ SMP Programming Bootcamp (Python) V1\n",
    "\n",
    "## Day 3: functions and a bit more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b0b496-d35a-4bdb-9025-349434d6f990",
   "metadata": {},
   "source": [
    "We have already used in-built functions such as `print()` and `len()`.\n",
    "\n",
    "We also create our own functions without giving it too much thought.\n",
    "\n",
    "Functions are the basic building blocks of programs (classes are another building block - mostly beyond this bootcamp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0455b47-a916-4d54-8a4a-e43c19c29a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_function():\n",
    "    print(\"A function that gets no arguments\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47d464c8-83d6-48ca-bd3e-663dc792c99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A function that gets no arguments\n",
      "A function that gets no arguments\n",
      "A function that gets no arguments\n"
     ]
    }
   ],
   "source": [
    "for _ in range(3):\n",
    "    my_function() #invoking the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c6a756e-a17f-4ee7-840a-30234ce18934",
   "metadata": {},
   "outputs": [],
   "source": [
    "def square_and_increment(x): #A function with an argument\n",
    "    return x**2 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffd293e1-b98e-44db-a712-98b3f99bf56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square_and_increment(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63afa017-9442-47e6-a01d-f282404ade4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 5, 10, 17, 26, 37, 50, 65, 82, 101]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[square_and_increment(x) for x in range(0,11)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d99090d9-b727-47ce-b665-d1cf31ef171b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_age_string(name, age):\n",
    "    return f\"{name} is {age} years old\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09fd20cf-5884-48d0-91f4-822b554ca58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Blake is 24 years old'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_age_string(\"Blake\",24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ce1261-de02-4454-abc0-06004ee9ccd8",
   "metadata": {},
   "source": [
    "Of course functions can call other functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b8473c3-e18a-4635-b0e4-c939e54dd677",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_age_string_with_tricks(name, age):\n",
    "    age2 = square_and_increment(age)\n",
    "    return f\"{name} is {age} years old and their magic number is {age2}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86415c57-cfc6-4d65-97c8-479c82b2f25d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Blake's string is Blake is 24 years old and their magic number is 577\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string = make_age_string_with_tricks(\"Blake\", 24)\n",
    "\"Blake's string is \" + my_string"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9daa77-511b-4a20-a837-20bfdbc75cdf",
   "metadata": {},
   "source": [
    "#### Task 3.1: Write a function that take a list of numbers and returns their arithmatic mean (average). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e29360-9087-4596-8a7a-2aadc7e28b6f",
   "metadata": {},
   "source": [
    "A function can also call itself. This is called recursion. Here is a classic example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "714f55fd-3145-4cb4-adeb-6853b3e3dc0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_factorial(n):\n",
    "    if n == 0:\n",
    "        return 1\n",
    "    return n * my_factorial(n-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de5026f1-4633-42be-9681-edef909752d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "720"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_factorial(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7df834b-adaa-4777-b1ba-45786af22360",
   "metadata": {},
   "source": [
    "#### Task 3.2 Write a recursive version of the collatz sequence (until hitting 1), each time printing the value as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a287a453-50e2-4546-a62b-288a33ff01c0",
   "metadata": {},
   "source": [
    "A function can return multiple values by using a tuple. Tuples are interesting in their own right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebd01a8d-c521-40bd-b61d-6fd1c5c34967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (\"hello\", 28)\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9450cc8e-cc0a-4e46-a4d3-9e4dedeaa41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (\"hello\")\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea587a1a-db23-4fda-b0da-b15f315ca179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (\"hello\",)\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69f27ec6-eb89-486f-9999-5b9e6173915c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (\"hello\", 28, -1.3)\n",
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6928c5bb-3a6d-4a97-adb2-d5575d0e2c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12c7877e-56be-4742-b385-7ee1c92a37cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tuple() #empty tuple\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060c0960-b841-4b2f-9798-c508361603e7",
   "metadata": {},
   "source": [
    "Tuples are immutable (unlike lists)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f95364c-7cb8-4fff-a99a-ce3185577b2f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2.8\u001b[39m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "x[2] = 2.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ba3d1ef-9d50-49ee-b827-68ec0e5a1e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 28, 2.8]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [\"hello\", 28, -1.3]\n",
    "x[2] = 2.8\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "349f897f-ea4d-4da5-9935-d84ff8d1e1da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8dab9ecd-d2cd-4f92-90ac-dee8de1c29cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_roots_of_quadratic(a, b, c):\n",
    "    disc = b**2 - 4*a*c\n",
    "    if disc < 0:\n",
    "        return tuple() #empty tuple - no solutions.\n",
    "    elif disc == 0:\n",
    "        return (-b / 2 / a, ) #tuple of length 1 \n",
    "    else: #disc >0\n",
    "        return ((-b + sqrt(disc)) / 2 / a, (-b - sqrt(disc)) / 2 / a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0be9395-8aa4-4fce-a4c8-76567e70d378",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_qudratic(a, b, c, roots):\n",
    "    f = lambda x: a*x**2 + b*x + c*x #This is a lambda function! \n",
    "    for r in roots:\n",
    "        print(f(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e80fcd1f-6bf8-439e-adca-ee7f533791ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roots = real_roots_of_quadratic(1,0,0)\n",
    "roots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9be4e5e3-3c40-4aff-9474-09713a91bf50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "check_qudratic(1, 0, 0, roots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a550ece8-e4ec-43b5-a0b8-1ecd6f566fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, -1.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_roots_of_quadratic(1,0,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "796107af-846e-4a44-83b1-bad147e6aead",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "check_qudratic(1, 0, -1, roots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9bb90e4-4f0a-48ff-8f1e-d3ca2af54f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "roots = real_roots_of_quadratic(-1,0,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e3e3bced-255b-490e-95e8-4d4a93232c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "check_qudratic(-1, 0, -1, roots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27dd3643-4157-401c-ba94-381e43a0da38",
   "metadata": {},
   "source": [
    "#### Task 3.3 Write function that gets a list of numbers and returns a tuple with the sample mean and sample standard deviation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48f2f24-cdc7-4a87-bb9d-a90f0bcf5d3b",
   "metadata": {},
   "source": [
    "We can (should) document our functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "047d149c-cc10-4036-afc9-1c134bad4b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_all_lower_case(str):\n",
    "    #This function checks if the input string is all_lower case\n",
    "    for c in str:\n",
    "        if ord(c) >= ord('A') and ord(c) <= ord('Z'):\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37414e00-2595-408c-a764-4668230f2c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m  \u001b[0mcheck_all_lower_case\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
       "\u001b[0;31mFile:\u001b[0m      /var/folders/1r/48qlcl0n01q21sqtbnvn6m9c0000gp/T/ipykernel_28081/2099116852.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "? check_all_lower_case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ccc504dc-bd21-47c5-b829-9e38be469dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_all_lower_case(\"Miriam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "836fd135-4522-4676-b38b-fd57625f0b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_all_lower_case(\"miriam\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad3ad2a-f1ee-47c2-a42a-03fd8d431028",
   "metadata": {},
   "source": [
    "#### Task 3.4 Write a function checks if all characters are letters (upper case or lower case)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eaad3e4-5dc1-4e2e-8ce6-4945f3176e2f",
   "metadata": {},
   "source": [
    "Variables have scope. For example a variable defined in one running program is not visible in another program. The same goes with functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "267cbcde-7438-4d43-bb94-67900e74c352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did nothing\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "a = 2\n",
    "def my_func():\n",
    "    a = 3 #This will not change a which is in global scope\n",
    "    print(\"did nothing\")\n",
    "\n",
    "my_func()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e1b5e07a-0da1-4b8c-92fc-617cbad1dac6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did something\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "a = 2\n",
    "def my_func():\n",
    "    global a\n",
    "    a = 3 #This WILL change a which is in global scope\n",
    "    print(\"did something\")\n",
    "\n",
    "my_func()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4345039d-3486-4986-ad53-b609191afed6",
   "metadata": {},
   "source": [
    "Python can have named arguments to functions with default values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a9bf84d4-32aa-4354-afdd-fbe893bb4f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fe458440-c722-44ec-99be-bfd4ec461d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normal_density(x, mu = 0, sigma = 1):\n",
    "    return 1/sqrt(2*math.pi) / sigma * exp(-((x-mu)/(2*sigma))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e90654e9-1ff2-40ec-8bd8-56ebac0d2335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x_range = np.arange(-6.0,6.0,0.01)\n",
    "density_standard = [normal_density(x) for x in x_range]\n",
    "density_wide = [normal_density(x, mu = 0, sigma = 1.5) for x in x_range]\n",
    "\n",
    "plt.plot(x_range, density_standard)\n",
    "plt.plot(x_range, density_wide)\n",
    "\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"Density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73e7853-67de-494b-87db-1fbaea3e241f",
   "metadata": {},
   "source": [
    "#### Task 3.5 Write function that gets another pure function (e.g. a normal density) and integrates it with specified limits using an approximation like,\n",
    "\n",
    "$$\n",
    "\\int_{a}^b f(x) dx \\approx \\sum f(x) \\delta\n",
    "$$\n",
    "\n",
    "The function will have arguments for the number of elements in the summation.\n",
    "\n",
    "Test this on the normal densities above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040f43e0-de29-456f-8051-561708e4bfba",
   "metadata": {},
   "source": [
    "### Let's now focus on lists..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "20dee781-aa02-4de9-9746-a4f7746a32df",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data = [\"My name\", 87, 23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bce4214f-405f-44c4-bd5d-8018174a56f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "your_data = my_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8356c542-5e1d-427a-a140-35031d83addc",
   "metadata": {},
   "outputs": [],
   "source": [
    "your_data[-1] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "938349aa-3680-4f39-a02f-0221c964237a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['My name', 87, 100]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data #Wow my_data is also changed. This is because both my_data and your_data point to the same list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4263ff65-aab0-4e73-aadd-c358b0f8f525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['My name', 87, 23], ['My name', 87, 100])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = [\"My name\", 87, 23]\n",
    "your_data = my_data.copy() #Copy actually creates a copy of the list.\n",
    "your_data[-1] = 100\n",
    "my_data, your_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782d5943-9a95-4bd6-964c-6fff2596ad88",
   "metadata": {},
   "source": [
    "#### Task 3.6 Write function gets a list and returns a sorted version of the list.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81824b5b-9689-4114-acfb-165858ee4241",
   "metadata": {},
   "source": [
    "There are a few functions for lists. Some of these are methods of the list class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "90f3072a-88d6-417f-9caa-e8b85c206ee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jack', 'jill', 'emma', 'lili']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_friends = [\"jack\", \"jill\"]\n",
    "your_friends = [\"emma\", \"lili\"]\n",
    "my_friends + your_friends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0aae4476-4298-4d22-a871-b80d1078731a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = [\"A\", \"B\" , \"C\"]\n",
    "list2 = [\"D\", \"E\"]\n",
    "\n",
    "for x in list2:\n",
    "  list1.append(x)\n",
    "\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "15cee919-8f0a-4d4d-9251-13b1c7f69943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'F' in list1, 'C' in list1 #you can use `in`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac8343e-c30e-4fb6-8ee2-bcbfa40c1bbf",
   "metadata": {},
   "source": [
    "#### Task 3.7: Write a function that loops, asking us for friend's names and their phone numbers until we say \"stop\". It then returns a tuple with one list of names, and one list of phone numbers...."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944e4f6c-b0a8-4936-9bf9-f044d215a0e9",
   "metadata": {},
   "source": [
    "E.g. The return value can be like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "acb8462c-870d-49f2-b41c-06319c1bf793",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ([\"Kayley\", \"Emily\", \"Miriam\"],[\"042424535\", \"042424125\", \"04235235\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd033447-3e31-49a7-af30-b26ba137df2e",
   "metadata": {},
   "source": [
    "#### Task 3.8: Now write a function that gets a result and a name, it should return the phone number of the name, or otherwise return \"nothing\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b00c73e-bfca-44e0-b5e0-3a88ef551029",
   "metadata": {},
   "source": [
    "Lucikly, Python comes with a built in data structure that is very suitable for such lookup and other many cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ea4cfcd5-d1a2-4165-97dc-e30149f20354",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"Kayley\": \"042424535\", \"Emily\": \"042424125\", \"Miriam\": \"04235235\" }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "52f31d0a-4598-4613-b3aa-82c27015fce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'042424535'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Kayley\"] #we can now index based on the keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6dd19fde-a669-43b2-89c4-eefb21943c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Kayley', 'Emily', 'Miriam'])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a770931b-a14d-4080-a7f4-8b18635d823a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['042424535', '042424125', '04235235'])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e9f1c9a9-3c26-4de7-87d9-58523db932fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"New friend\"]=\"5124125\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c8356d9c-c928-4e50-b9a5-c24bcb9fd8b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kayley': '042424535',\n",
       " 'Emily': '042424125',\n",
       " 'Miriam': '04235235',\n",
       " 'New friend': '5124125'}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3a9cb866-d45d-434a-914a-fae347037331",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Imaginary friend'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[58], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mImaginary friend\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Imaginary friend'"
     ]
    }
   ],
   "source": [
    "data[\"Imaginary friend\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e823ca1b-11d1-4c32-8b0b-18e4183369ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kayley': '042424535',\n",
       " 'Emily': '042424125',\n",
       " 'Miriam': '04235235',\n",
       " 'New friend': '5124125'}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c962513a-7474-48a8-b13b-36800fbc27f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['New friend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba83e394-594d-43fe-882e-e4d20b1f8457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Kayley': '042424535', 'Emily': '042424125', 'Miriam': '04235235'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6774fe0-8917-4cc7-88ab-bb1fafb08000",
   "metadata": {},
   "source": [
    "See more on the [Python docs](https://docs.python.org/3/tutorial/datastructures.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83606efa-c014-4d76-a6e3-4df56a92fa28",
   "metadata": {},
   "source": [
    "Python also has sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "65e2a8f5-d943-4ff8-8a97-02b1bc25c0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_set = {\"car\", 245, \"dog\", (2,3)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c95b37-ed77-440a-a895-b4890795fcd5",
   "metadata": {},
   "source": [
    "#### Task 3.9: Try to create a set with a list as an element. Discuss and investigate the error message that you get."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c71048-b940-4a09-84e1-b1ef7d224d61",
   "metadata": {},
   "source": [
    "#### Task 3.10: In this task you will manipulate a dictionary based data structure for contacts..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b9eb00-f214-4c7f-8e93-37c1486f9c8b",
   "metadata": {},
   "source": [
    "Assume you have a contacts... where a contact is identified by a name.  These are the keys in the dictionary. Every contact has a key which itself a dictionary with the following keys: `phone_number` (string), `email` (string), `age` (int), and `friends` (set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8182edc6-caba-47a0-b244-4e60e784e50f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'phone_number': '3562252',\n",
       " 'email': 'jake5@gmail.com',\n",
       " 'age': 22,\n",
       " 'friends': {'Ben', 'Jacob'}}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for example\n",
    "data = {\"Ben\": \n",
    "            {\"phone_number\": \"35123532\", \n",
    "             \"email\": \"ben@onehop.com\", \n",
    "             \"age\": 34, \n",
    "             \"friends\": {\"Jacob\", \"Beth\"}\n",
    "            },\n",
    "        \"Lea\":\n",
    "            {\"phone_number\": \"35345125652\", \n",
    "             \"email\": \"lea3523@gmail.com\", \n",
    "             \"age\": 82, \n",
    "             \"friends\": {\"Ben\"}\n",
    "            },\n",
    "        \"Jacob\":\n",
    "            {\"phone_number\": \"3515252\", \n",
    "             \"email\": \"jake5@gmail.com\", \n",
    "             \"age\": 22, \n",
    "             \"friends\": {}\n",
    "            },\n",
    "        \"Beth\":\n",
    "            {\"phone_number\": \"3562252\", \n",
    "             \"email\": \"jake5@gmail.com\", \n",
    "             \"age\": 22, \n",
    "             \"friends\": {\"Jacob\", \"Ben\"}\n",
    "            }\n",
    "       }\n",
    "data[\"Beth\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32efa208-0f4d-4ca7-a4b1-6f4ac74a19d9",
   "metadata": {},
   "source": [
    "3.10 (a): Write a function that checks consistency between friends. For example, see that every friend of Ben exists and Ben is a friend of him as well. \n",
    "\n",
    "3.10 (b): Write a function that adds a new entry.\n",
    "\n",
    "3.10 (c): Write a function that deletes an entry (for this it would need to potentially delete entries from the friends set of everyone)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1563d1a-11dc-46c5-84d7-fcb6266ce04d",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <h1>\n",
    "        END OF DAY 3\n",
    "    </h1>\n",
    "</center>"
   ]
  }
 ],
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