Lesson 2 - Control structures.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 2 - Control structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Authors:**\n",
    "* Yilber Fabian Bautista\n",
    "* Keiwan Jamaly\n",
    "* Sean Tulin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Control structures are programming building blocks that are used to control the flow of the program. \n",
    "By default, code is evaluated sequentially, from one line of code to the next. \n",
    "However, control structures allow you to run different segments of the code under different circumstances, or the same segment of code multiple times. \n",
    "\n",
    "Here we consider three types of control structures:\n",
    "- **conditional statements**, or **if-else statements**\n",
    "- **for loops**\n",
    "- **while loops**\n",
    "\n",
    "Another useful type of control structure are **try-except statements**, but these are covered in a later lesson.\n",
    "\n",
    "**Objectives:** \n",
    "\n",
    "By the end of this lecture you will be able to:\n",
    "* Identify and use control structures in short programs in particular to:\n",
    "    * Understand and design **flow charts**\n",
    "    * Use **conditional statements** \n",
    "    * Differentiate between **definite**  and **indefinite** iterations\n",
    "    * Execute **definite** iterations using python **for** loops  \n",
    "    * Execute **indefinite** iterations using python **while** loops\n",
    "    * Translate **for** loops into **while** loops and vice-versa\n",
    "    * Use **print** statements to understand and debug your codes\n",
    "* Expand your **list comprehension** skills\n",
    "* Use **higher-order functions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional Statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A conditional statement is a piece of code that is executed in a program if a certain condition is met, otherwise that part of the code is skipped over and not executed. This can be illustrated in the following **Flow Chart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"Figures/scala_decision_making.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simples statement (as seen from the **flow chart**) is the **if statement**. It has the following basic structure in python: \n",
    "\n",
    "```py\n",
    "if <condition>:\n",
    "    <statement>\n",
    "```\n",
    "\n",
    "- <code>condition</code> is an expression that evaluates to a Boolean value (`True` or `False`).\n",
    "- <code>statement</code> is a valid Python statement, which must be indented. \n",
    "\n",
    "If <code>condition</code> is <code>True</code>, then <code>statement</code> is executed. If <code>condition</code> is false, then <code>statement</code> is skipped over and not executed.\n",
    "\n",
    "Note that the colon `:` following <code>condition</code> is required.\n",
    "\n",
    "Let us see an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is greater than 3\n"
     ]
    }
   ],
   "source": [
    "x = 5 # Change this to something else to see what happens\n",
    "\n",
    "if x > 3:\n",
    "    print(\"x is greater than 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can chain multiple conditions together as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is greater than 3\n",
      "x is even\n"
     ]
    }
   ],
   "source": [
    "x = 4 # Change this value and see what happens\n",
    "if x > 3:\n",
    "    print(\"x is greater than 3\")\n",
    "    if x % 2 == 0:\n",
    "        print(\"x is even\")\n",
    "    if x % 2 == 1:\n",
    "        print(\"x is odd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have learned how to execute a code if a condition is met, we ask what **else** can be done.\n",
    "Sometimes, we want to evaluate a condition and take one path if it is true but specify an alternative path if it is not. \n",
    "\n",
    "**else** is precisely another **conditional statement**. In a **flow chart** the path specification  it looks as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"Figures/if_else_statement.jpg\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In python this is accomplished with an if and an  else clause as follows:\n",
    "\n",
    "```py\n",
    "if <condition>:\n",
    "    <statement(s)>\n",
    "else:\n",
    "    <statement(s)>\n",
    "```\n",
    "\n",
    "In this code, if <code>condition</code> is true, the first suite is executed, and the second is skipped. If <code>condition</code> is false, the first suite is skipped and the second is executed. Either way, execution then resumes after the second suite.\n",
    "\n",
    "Let us see and specific example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is divisible by 3\n"
     ]
    }
   ],
   "source": [
    "x = 3 # change the this value and see what happens\n",
    "\n",
    "if x % 3 == 0:\n",
    "    print(\"x is divisible by 3\")\n",
    "else:\n",
    "    print(\"x is not divisible by 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case we have several alternatives in the code, we use  one or more **elif** (short for else if) clauses. Python evaluates each <code>condition</code> in turn and executes the suite corresponding to the first that is true. \n",
    "If none of the expressions are true, and an else clause is specified, then its suite is executed. In a **flow chart** it looks as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"Figures/Else-if.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In python this is implemented in the following way:\n",
    "\n",
    "```py\n",
    "if <condition>:\n",
    "    <statement(s)>\n",
    "elif <condition>:\n",
    "    <statement(s)>\n",
    "elif <condition>:\n",
    "    <statement(s)>\n",
    "    ...\n",
    "else:\n",
    "    <statement(s)>\n",
    "```\n",
    "Let us see an example implemented [here](https://realpython.com/python-conditional-statements/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I don't know who you are!\n"
     ]
    }
   ],
   "source": [
    "name = 'Sam' #change this imput and see what happens\n",
    "if name == 'Fred':\n",
    "     print('Hello Fred')\n",
    "elif name == 'Xander':\n",
    "    print('Hello Xander')\n",
    "elif name == 'Joe':\n",
    "     print('Hello Joe')\n",
    "elif name == 'Arnold':\n",
    "    print('Hello Arnold')\n",
    "else:\n",
    "    print(\"I don't know who you are!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To end this section let us mention that **conditional statements** can be implemented in one line. \n",
    "(This is analogous to **list comprehensions** as we will see below.)  Let us see an example of how this works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.23606797749979\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = -5\n",
    "\n",
    "x = np.sqrt(x) if x >= 0 else np.sqrt(-x)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The one liner if statement can be the easies understood if we read it from left to right and translate it to English.\n",
    "\n",
    "1. `np.sqrt(x)`: square root of x\n",
    "2. `if x >= 0`: if the condition is true\n",
    "3. `else -np.sqrt(-x)`: else (if the condition is false) minus square root of -x\n",
    "\n",
    "It is a little tricky to get used to the syntax but if it is used at the right places, program code will be very readable. In most cases, though, it is more readable (and preferred) to write conditional statements in multiple lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1\n",
    "\n",
    "Define a piecewise function `f(x)` that takes as input a number `x` and returns\n",
    "- $-e$ if $ x < 1$\n",
    "- $\\pi$ if $x = 1$\n",
    "- $\\sin(x)$ if $x > 1$\n",
    "\n",
    "Make a plot of $x$ on the $x$-axis and $f(x)$ on the $y$-axis, taking a logarithmic spacing in $x$ from $10^{-5}$ to $10^5$. Use a `log` scale for the $x$-axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part of the tutorial will learn how to perform definite iteration with a Python `for` loop.\n",
    "\n",
    "An **iteration** is a repetitive execution of the same block of code over and over. There are two types of iteration: \n",
    "* **Definite iteration**, in which the number of repetitions is specified explicitly in advance\n",
    "* **Indefinite iteration**, in which the code block executes until some condition is met\n",
    "In Python, definite iterations are achieved using **for** loops, whereas **indefinite iterations** are performed using **while** loops.\n",
    "\n",
    "A for loop has the following structure:\n",
    "        \n",
    "```py\n",
    "for <var> in <iterable>:\n",
    "    <statement(s)>\n",
    "```\n",
    "\n",
    "The loop variable <code>var</code> is an iterator which takes on the value of the next element in <code>iterable</code>. The latter corresponds to a collection of objects, for example, a list or tuple, an interval, an array, etc. \n",
    "The <code>statement(s)</code> in the loop body are denoted by indentation, and are executed once for each item in <code>iterable</code>. \n",
    "\n",
    "Let us see a simple example of a for loop in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's break this down having in mind the previous general structure \n",
    "\n",
    "* The for loop always begins with the statement `for`\n",
    "* Then you name a variable wich will change its value in the for loop. This variable is the iterator. \n",
    "* The keyword `in`\n",
    "* An iterable object (`range(10)` in the upper example, at which we will look closer later) \n",
    "\n",
    "Let us see another example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "67\n",
      "2\n",
      "5\n",
      "7\n",
      "324\n",
      "56\n",
      "34\n"
     ]
    }
   ],
   "source": [
    "x = [2,67,2,5,7,324,56,34]\n",
    "\n",
    "for i in x:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see a more complex example of a for loop, for which the iterator can take more complicated  objects in the iterator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number is 1 and word is hello\n",
      "Number is 25 and word is pencil\n",
      "Number is 58 and word is metronome\n",
      "Number is 5 and word is bike\n"
     ]
    }
   ],
   "source": [
    "x = [(1, \"hello\"), (25, \"pencil\"), (58, \"metronome\"), (5, \"bike\")]\n",
    "\n",
    "for (number, word) in x:\n",
    "    print(\"Number is\", number, \"and word is\", word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, you individually select the elements of the tuple in a list. This is pretty neat if you want to write clean code. If you are interested, you can read something about [enumerate](https://docs.python.org/3/library/functions.html#enumerate) and [destructuring](https://blog.teclado.com/destructuring-in-python/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we learned from Exercise 1, conditional statements can be use anywhere in a code, and in particular, they are useful in loops. Let us illustrate this with the following example:\n",
    "\n",
    "### Example\n",
    "\n",
    "We are given the array of integer numbers from 0 to 100, and we want to separate the even and the odd numbers into two different lists. We can do that with a for loop as follows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The list of even numbers is: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100] \n",
      "\n",
      "The list of odd numbers is: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99]\n"
     ]
    }
   ],
   "source": [
    "given_array = np.arange(101)\n",
    "even  = []\n",
    "odd = []\n",
    "\n",
    "for k in given_array:\n",
    "    if k % 2 == 1:\n",
    "        odd.append(k)\n",
    "    else:\n",
    "        even.append(k)\n",
    "        \n",
    "print(\"The list of even numbers is:\", even,\"\\n\")\n",
    "\n",
    "print(\"The list of odd numbers is:\", odd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple example illustrates how we can combine elements learned in the different lessons, and make our codes more dynamic. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List Comprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous example we used a for loop to create the lists of even and odd numbers.  Here we will learn a different method to create list. Let us see it in a simple example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "\n",
    "for i in range(10):\n",
    "    x.append(i**2)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analog to the one-line **conditional statements** of previous section, this can be implemented in one line as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    }
   ],
   "source": [
    "x = [i**2 for i in range(10)]\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List comprehensions can also be chained together. \n",
    "Let's say you want to store all positions of a two dimensional grid $[0, 5] \\times [0,5]$ into one list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5)]\n"
     ]
    }
   ],
   "source": [
    "grid_2_d = [(x, y) for x in range(6) for y in range(6)]\n",
    "\n",
    "print(grid_2_d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "\n",
    "Using for loops and conditional statements, find all prime numbers between 0 and 100, and store them in one list. In a separate list, store the non-prime numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write your solution in this cell\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "Assume `s` is a string of lower case characters.\n",
    "\n",
    "Write a program that counts up the number of vowels contained in the string s. Valid vowels are: <code>'a' , 'e', 'i', 'o',</code>  and <code>'u'</code>.\n",
    "\n",
    "For example, if <code>s = 'azcbobobegghakl'</code>, your program should print:\n",
    "\n",
    "<code>Number of vowels: 5 </code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write your solutions here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can learn more about for loops [here](https://realpython.com/python-for-loop/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## While loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have learnt how to perform **definite** iterations using **for** loops. In this part of the tutorial we will learn how to do do **indefinite** iterations using **while** loops. \n",
    "\n",
    "A **while** loop has the following structure:\n",
    "\n",
    "\n",
    "```py\n",
    "while <condition>:\n",
    "    <statement(s)>\n",
    "```\n",
    "Like for loops, the <code>statement(s)</code> represents the block to be repeatedly executed. \n",
    "\n",
    "The controlling <code>condition</code> typically involves one or more variables that are initialized prior to starting the loop and then modified somewhere in the loop body.\n",
    "\n",
    "When a while loop is encountered, <code>condition</code> is first evaluated in Boolean context. \n",
    "If it is true, the loop body is executed. Then <code>condition</code> is checked again, and if still true, the body is executed again. This continues until <code>condition</code>becomes false, at which point program execution proceeds to the first statement beyond the loop body.\n",
    "     \n",
    "Let us see an example of a while loop:    \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "\n",
    "while x > 3:\n",
    "    print(x)\n",
    "    x -= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the condition is checked before the loop body is executed. \n",
    "It starts with the keyword `while`. \n",
    "After the keyword, you write the condition (i.e. `x > 3`), which can be any expression that evaluates to a boolean value, followed by a colon and the code that is executed if the condition is true (i.e., `print(\"x is greater than 3\")`).\n",
    "\n",
    "You may have noticed, that this could easily end up in an infinite loop, if you make mistakes during programming, this is very common when first learning how to code while loops, for instance if you forget to update the <code>condition</code> in the loop body. \n",
    "\n",
    "For instance, if you wrote the following code \n",
    "    \n",
    "```py\n",
    "x = 10\n",
    "bigger_3 = True\n",
    "while bigger_3:\n",
    "    print(x)\n",
    "    x -= 1\n",
    "```\n",
    "this ends in an infinite loop, so please do not try to run it in a cell. \n",
    "We can however truncate infinite loops, using **conditional statements**. Here is where they become very useful:\n",
    "```py\n",
    "x = 10\n",
    "bigger_3 = True\n",
    "while bigger_3:\n",
    "    print(x)\n",
    "    x -= 1\n",
    "    if x < 3:\n",
    "        break       \n",
    "``` \n",
    "Try and run this last piece of code in a cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us finally mention that every **for** loop can be written in as a **while** loop: Let us see this in one of our examples above which separates the even an odd numbers contained in the interval 0-100:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The list of even numbers is: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100]\n",
      "\n",
      "The list of odd numbers is: [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99]\n"
     ]
    }
   ],
   "source": [
    "x = 100\n",
    "even  = []\n",
    "odd = []\n",
    "\n",
    "while x >= 0:\n",
    "    if x % 2 == 1:\n",
    "        odd.append(x)\n",
    "    else:\n",
    "        even.append(x)\n",
    "    x -= 1\n",
    "even.sort() # sort() function just orders the numbers from the smallest to the biggest\n",
    "odd.sort()\n",
    "print(\"The list of even numbers is:\", even)\n",
    "print()\n",
    "print(\"The list of odd numbers is:\", odd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4\n",
    "Write your code for finding the prime numbers in the interval 0-100 using a **while** loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write your solutions here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 5\n",
    "Here is some code sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0; count is: 12\n",
      "Iteration 1; count is: 24\n",
      "Iteration 2; count is: 36\n",
      "Iteration 3; count is: 48\n",
      "Iteration 4; count is: 60\n"
     ]
    }
   ],
   "source": [
    "iteration = 0\n",
    "count = 0\n",
    "while iteration < 5:\n",
    "    for letter in \"hello, world\":\n",
    "        count += 1\n",
    "    print(\"Iteration \" + str(iteration) + \"; count is: \" + str(count))\n",
    "    iteration += 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wish to re-write the above code, but instead of nesting a for loop inside a while loop, we want to nest a while loop inside a for loop. Write a solution for this here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Incert your solution here and test it "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 6 (optional)\n",
    "\n",
    "Use the  [Newton-Raphson](https://en.wikipedia.org/wiki/Newton%27s_method) method and a **while** loop, to find the root of the function $f(x) = x^3 - x^5 - 1$.\n",
    "\n",
    "After an initial guess $x_0$, the root can be approximated by the following formula iteratively:\n",
    "\n",
    "$x_{n+1} = x_n - f(x_n) / f'(x_n)$\n",
    "\n",
    "where $f(x)$ is the function and $f'(x)$ is the derivative of the function.\n",
    "\n",
    "*Hint: think of a good condition for the while loop.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write your solution here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A warning about lists and arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to copy a single variable, it is trivial to do so. Note that `x` and `y` are independent. Although `y` was initially set to the same value of `x`, you are then free to modify `y` without affecting `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x= 4 y= 4\n",
      "x= 4 y= 5\n"
     ]
    }
   ],
   "source": [
    "x = 4\n",
    "y = x\n",
    "print(\"x=\", x, \"y=\", y)\n",
    "\n",
    "# and if you change y\n",
    "\n",
    "y = 5\n",
    "print(\"x=\", x, \"y=\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this does not work for lists or numpy arrays! The following example illustrates the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [1, 2, 3, 100, 5]\n",
      "y = [1, 2, 3, 100, 5]\n"
     ]
    }
   ],
   "source": [
    "x = [1,2,3,4,5]\n",
    "y = x\n",
    "\n",
    "y[3] = 100\n",
    "print(\"x =\", x)\n",
    "print(\"y =\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `x` and `y` are not independent objects. Setting `y = x` does not create `y` as a new list that is the same as `x`. It just creates a **view** of `x`, which means that `y` and `x` are actually the *same object*. Hence, modifying one modifies the other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists and numpy arrays can be sliced, with the same notation that we learned about for slicing strings in **Lesson 1**. List and array slicing can also help with a quick fix to the above problem, as we will see.\n",
    "\n",
    "The general syntax of array slicing is\n",
    "```py\n",
    "sliced_array = original_array[start:end:step]\n",
    "```\n",
    "where `start` and `end` are the start and end index of the slice, and `step` is the step size. If you omit `start`, it defaults to 0. If you omit `end`, it defaults to the length of the array. If you omit `step`, it defaults to 1. \n",
    "If you omit both start and end, it defaults to the entire array.\n",
    "\n",
    "Note that end index is not included in the slice. So if you want to select the first three elements of an array, you have to write\n",
    "```py\n",
    "silced_array = original_array[0:3]\n",
    "```\n",
    "Keep in mind, that this works also for lists as well as numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get back to our previous problem, we can simply copy an array using the following syntax:\n",
    "```py\n",
    "x = y[:]\n",
    "```\n",
    "Setting `y` equal to a *slice* of `x` (even if that slice is the entire list or array) creates a separate *copy* of `x` that can modified separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [1, 2, 3, 4, 5]\n",
      "y = [1, 2, 3, 100, 5]\n"
     ]
    }
   ],
   "source": [
    "x = [1,2,3,4,5]\n",
    "y = x[:]\n",
    "\n",
    "y[3] = 100\n",
    "print(\"x =\", x)\n",
    "print(\"y =\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## range() - function\n",
    "\n",
    "remember the `range()` function we have used before? It is a built-in function that returns a sequence of numbers. The sequence of numbers starts at 0 and ends at the number you specify. But you can also specify the start and step size of the sequence. The syntax is very similar to the one of array slicing, but the start and step size are optional and instead of an double colon, you use a comma. Let's generate a more advance sequence, that starts at 10 and ends at 20 with a step size of 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(10, 20, 2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10, 20, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the sequence is not really a list, but a range object. You can use the list methods on this object. Let's convert the sequence to a list, to see our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 12, 14, 16, 18]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10, 20, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also generate reversed sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[20, 18, 16, 14, 12]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(20, 10, -2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Higher order functions (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher order functions are functions that take other functions as arguments or return functions. \n",
    "We will look at two in particular: **map** and **filter**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## map()\n",
    "\n",
    "It takes a function and a sequence as arguments and applies the function to each element of the sequence.\n",
    "An example of this would be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<map at 0x1e4fc1b4d30>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(x):\n",
    "    return x**2\n",
    "\n",
    "squared_sequence = map(square, range(10))\n",
    "squared_sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`map()` will not return a list, but a a generator object. This means, that elements of the sequence are not computed immediately, but only when you call them within a loop for example. This reduces the memory usage of your program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "4\n",
      "9\n",
      "16\n",
      "25\n",
      "36\n",
      "49\n",
      "64\n",
      "81\n"
     ]
    }
   ],
   "source": [
    "for element in squared_sequence:\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can always convert a generator object to a list using the `list()` function.\n",
    "\n",
    "We already know the numpy equivalent of `map()`, we just insert the array into the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  4  9 16 25 36 49 64 81]\n"
     ]
    }
   ],
   "source": [
    "numpy_array = np.array(list(range(10)))\n",
    "\n",
    "numpy_array_squared = square(numpy_array)\n",
    "\n",
    "print(numpy_array_squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filter()\n",
    "\n",
    "As the name suggests, `filter()` takes a function and a sequence as arguments and returns the part of the sequence for which the function returns true. let's find all even values of a sequence/list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = range(10)\n",
    "\n",
    "def even_check(x):\n",
    "    return x % 2 == 0\n",
    "\n",
    "filtered_sequence = filter(even_check, x)\n",
    "list(filtered_sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The numpy equivalent of `filter()` is more complicated. It's best explained, by making an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True False  True False  True False  True False]\n",
      "[0 2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(10) # define our numpy array\n",
    "\n",
    "filter_array = x % 2 == 0 # define our filter\n",
    "\n",
    "print(filter_array)\n",
    "\n",
    "filtered_array = x[filter_array] # apply the filter\n",
    "\n",
    "print(filtered_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, from our `x` array, only the elements are selected, where the `filter_array` is `True`. you can compact this code to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "\n",
    "x[x % 2 == 0] # apply the filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to write more complicated logical expressions, you might want to use [this](https://numpy.org/doc/stable/reference/routines.logic.html#logical-operations) reference. "
   ]
  }
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