2_Loops-and-Conditions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "372ea4b4-8849-4238-b9ef-e7d63cd1fbbe",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from IPython.core.display import HTML\n",
    "table_css = 'table {align:left;display:block} '\n",
    "HTML('<style>{}</style>'.format(table_css))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646e7440-6d39-49a1-bae3-8d6c150808ec",
   "metadata": {},
   "source": [
    "# **ASI WA Python Workshop**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df800c9e-fba8-4c16-aff7-d7765af96add",
   "metadata": {},
   "source": [
    "## **Jupyter Notebook Tips**\n",
    "\n",
    "Outside of a cell:\n",
    "\n",
    "| key combo | action |\n",
    "| :--: | ---- |\n",
    "| shift+enter |  run a cell |\n",
    "| b |  insert cell below | \n",
    "| a |  insert cell above |\n",
    "| dd | delete selected cell |\n",
    "\n",
    "<br>\n",
    "Within a cell:\n",
    "\n",
    "| key combo | action |\n",
    "| :--: | ---- |\n",
    "| Command + / |  comment out line(s) |\n",
    "| Alt + Cursor | vertical selection |\n",
    "\n",
    "**Control = Command\n",
    "<br><br><br>"
   ]
  },
  {
   "attachments": {
    "841a0c71-1dce-4f99-a0cb-c2f7690f06d5.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a4b5ddd0-7849-43bc-a02d-c9a915b2d1a5",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "## **Operators**\n",
    "\n",
    "### Comparison Operators\n",
    "In programming, we often need to compare values to make decisions or determine relationships between them. For this purpose, we use comparison operators that allow us to compare two values and evaluate conditions such as greater than, less than, or equal to. \n",
    "\n",
    "![comparison_operators.png](attachment:841a0c71-1dce-4f99-a0cb-c2f7690f06d5.png)\n",
    "\n",
    "All camparison operators return a bool, True or False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c6c45d7-9581-4690-aef5-77acf0730f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a > b False\n",
      "a >= b False\n",
      "a < b True\n",
      "a < b False\n",
      "a <= b False\n",
      "a == b False\n",
      "a != b True\n"
     ]
    }
   ],
   "source": [
    "a = 'A'\n",
    "b = 'C'\n",
    "\n",
    "print(\"a > b\", a > b)    \n",
    "print(\"a >= b\", a >= b)  \n",
    "print(\"a < b\", a < b)    \n",
    "print(\"a < b\", b < a)    \n",
    "print(\"a <= b\", b <= a)  \n",
    "print(\"a == b\", a == b)  \n",
    "print(\"a != b\", a != b) # Not equal is probably the only operator you have not seen before "
   ]
  },
  {
   "attachments": {
    "d1521cca-7edd-433d-ad13-c47cd6f59590.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "663b0b96-162c-4afc-811d-d1a87c57e9fd",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Logical Operators\n",
    "Unlike other programming languages python uses keywords and, or and not for logical operators which can be used to combine conditional statements:\n",
    "\n",
    "![logical_operators.png](attachment:d1521cca-7edd-433d-ad13-c47cd6f59590.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "256989c3-963a-49f8-be3b-44da17651339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True and True:  True\n"
     ]
    }
   ],
   "source": [
    "# and = True is both are True\n",
    "print('True and True: ', True and True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ce0f5d3-24dc-4e7b-bee9-53fe3a3b17dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True or False:  True\n",
      "False or False: True\n",
      "False or False: False\n"
     ]
    }
   ],
   "source": [
    "# or = True if at least one is True\n",
    "print('True or False: ', True or False)\n",
    "print('False or False:', False or True)\n",
    "print('False or False:', False or False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f78e78b-d656-46af-9f34-a4d92e8070ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not True  : False\n",
      "not False : True\n"
     ]
    }
   ],
   "source": [
    "# not changes bool\n",
    "print('not True  :', not True)      \n",
    "print('not False :', not False)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2687305-6d64-41d1-acc5-283a446577e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a > b and c > a: False\n",
      "a > b and c < a: False\n",
      "a < b and c < a: False\n",
      "a > b or c > a: True\n",
      "a > b or c < a: False\n",
      "a < b or c < a: True\n",
      "not a > b: True\n"
     ]
    }
   ],
   "source": [
    "# feel free to play around with value and type changes\n",
    "a = 1\n",
    "b = 3\n",
    "c = 5\n",
    "\n",
    "print('a > b and c > a:', a > b and c > a) \n",
    "print('a > b and c < a:', a > b and c < a) \n",
    "print('a < b and c < a:', a < b and c < a) \n",
    "\n",
    "print('a > b or c > a:', a > b or c > a) \n",
    "print('a > b or c < a:', a > b or c < a) \n",
    "print('a < b or c < a:', a < b or c < a) \n",
    "\n",
    "print('not a > b:', not a > b)     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13f09d3-89fa-44fb-afb0-1ee2eebef09b",
   "metadata": {},
   "source": [
    "### Python Membership Operators\n",
    "\n",
    "*in* and *not in* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "351536cc-dbd7-4f6b-93ae-374babbe1583",
   "metadata": {},
   "outputs": [],
   "source": [
    "list1 = ['a','b','c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8739fbd-34de-462f-81b5-a969895ec50d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print( 'a' in list1)\n",
    "print( 'a' not in list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "377467ba-1a89-47d9-87af-d96891bf66cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print( 'd' in list1)\n",
    "print( 'd' not in list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614a2fef-fb99-47e6-bb28-a15de3dc2fe9",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "## **Python Condition Statements**\n",
    "In python and other programming languages the key word if is used to check if a condition is true and to execute the block code. **Remember the indentation after the colon.**\n",
    "\n",
    "<br>\n",
    "\n",
    "**Python Indentation**\n",
    "\n",
    "Indentation refers to the spaces at the beginning of a code line. Where in other programming languages the indentation in code is for readability only, the indentation in Python is very important.\n",
    "\n",
    "Python uses indentation to indicate a block of code.\n",
    "### *if* statement\n",
    "```# syntax\n",
    "if condition:\n",
    "    this part of code runs for truthy conditions\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03fe3d69-899f-466b-8567-99443eb558bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A is a positive number\n"
     ]
    }
   ],
   "source": [
    "# one condition example\n",
    "a = 3\n",
    "if a > 0:\n",
    "    print('A is a positive number')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d556d515-87ab-4ab4-9c46-9e13855ffea9",
   "metadata": {},
   "source": [
    "As you can see in the example above, 3 is greater than 0. The condition was true and the block code was executed. However, if the condition is false, we do not see the result. In order to see the result of the falsy condition, we should have another block, which is going to be else.\n",
    "\n",
    "### *if else* statement\n",
    "\n",
    "If condition is true the first block will be executed, if not the else condition will run.\n",
    "\n",
    "```\n",
    "# syntax\n",
    "if condition:\n",
    "    this part of code runs for truthy conditions\n",
    "else:\n",
    "     this part of code runs for false conditions\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f4ea2c3-f71e-4f39-8621-1040a7897230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A is a positive number\n"
     ]
    }
   ],
   "source": [
    "# two condition example\n",
    "a = 3\n",
    "if a < 0:\n",
    "    print('A is a negative number')\n",
    "else:\n",
    "    print('A is a positive number')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf04736-8979-4c0f-9f21-04d810c229cf",
   "metadata": {},
   "source": [
    "### *if elif else* statement\n",
    "\n",
    "Often in programming we need to test more than two conditions. We can use elif when we have multiple conditions.\n",
    "\n",
    "```\n",
    "# syntax\n",
    "if condition:\n",
    "    code\n",
    "elif condition:\n",
    "    code\n",
    "else:\n",
    "    code\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a822951b-8ac8-4472-868b-3782f9f968b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A is zero\n"
     ]
    }
   ],
   "source": [
    "# multi-conditional example\n",
    "a = 0\n",
    "if a > 0:\n",
    "    print('A is a positive number')\n",
    "elif a < 0:\n",
    "    print('A is a negative number')\n",
    "else:\n",
    "    print('A is zero')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35031123-2817-453d-8077-b060e1e94d39",
   "metadata": {},
   "source": [
    "### If Condition *and* Logical Operators\n",
    "\n",
    "```\n",
    "# syntax\n",
    "if condition and condition:\n",
    "    code\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b940ad1b-12e9-4c4b-ba93-186bd5c1657a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are eligible for a discount!\n"
     ]
    }
   ],
   "source": [
    "age = 25\n",
    "is_student = True\n",
    "\n",
    "if age <= 30 and is_student:\n",
    "    print(\"You are eligible for a discount!\")\n",
    "else:\n",
    "    print(\"Sorry, you are not eligible for a discount.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72173922-b864-459a-a018-8cbe8b59cbf2",
   "metadata": {},
   "source": [
    "### If Condition *or* Logical Operators\n",
    "\n",
    "```\n",
    "# syntax\n",
    "if condition or condition:\n",
    "    code\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1049aaaa-18cb-4391-b435-f8de3d9ef76d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Access denied!\n"
     ]
    }
   ],
   "source": [
    "user = 'James'\n",
    "access_level = 3\n",
    "\n",
    "if user == 'admin' or access_level >= 4:\n",
    "    print('Access granted!')\n",
    "else:\n",
    "    print('Access denied!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20440ad-f583-4c1f-8df1-5b508e5425fd",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 2.1: Grade Assignment*\n",
    "1. Ask the user to input their score with input() function.\n",
    "\n",
    "2. Write a code that assigns a grade based on the score according to the following criteria:\n",
    "- If the score is between 90 and 100, assign grade \"A\".\n",
    "- If the score is between 80 and 89, assign grade \"B\".\n",
    "- If the score is between 70 and 79, assign grade \"C\".\n",
    "- If the score is below 70, assign grade \"Fail\".\n",
    "\n",
    "3. Print the assigned grade.\n",
    "\n",
    "*Bonus:* Using type() print an error if something other than a numerical is passed.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "837b79c6-c829-43c4-84d3-587a67048b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter your score 70\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You entered: 70\n",
      "Your letter grade is C\n"
     ]
    }
   ],
   "source": [
    "# Task 2.1\n",
    "score = int(input(\"Enter your score\")) # press enter after entering you score\n",
    "print(f\"You entered: {score}\") # printing with help of an f-string\n",
    "\n",
    "# you can do this explicitly with if statements\n",
    "if score>=90:\n",
    "    grade = 'A'\n",
    "elif score>=80 and score<90:\n",
    "    grade = 'B'\n",
    "elif score>=70 and score<80:\n",
    "    grade = 'C'\n",
    "else:\n",
    "    grade = \"Fail\"\n",
    "    \n",
    "print(f'Your letter grade is {grade}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a9ab97-2317-498f-b0ba-cfec705b112a",
   "metadata": {},
   "source": [
    "The above solution would work if you replaced each elif with if, but we can make use of the iterative nature of elif statements to make this more streamline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "54a75b3a-eef8-44d7-9f56-ba04f5445e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter your score 88\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You entered: 88\n",
      "Your letter grade is B\n"
     ]
    }
   ],
   "source": [
    "score = int(input(\"Enter your score\")) # press enter after entering you score\n",
    "print(f\"You entered: {score}\") # printing with help of an f-string\n",
    "\n",
    "# you can do this explicitly with if statements\n",
    "if score>=90:\n",
    "    grade = 'A'\n",
    "elif score>=80:\n",
    "    grade = 'B'\n",
    "elif score>=70:\n",
    "    grade = 'C'\n",
    "else:\n",
    "    grade = \"Fail\"\n",
    "    \n",
    "print(f'Your letter grade is {grade}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e35416-c1af-4c07-a6e0-d18a911fec47",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## **Loops**\n",
    "Life is full of routines. In programming we also do lots of repetitive tasks. In order to handle repetitive task programming languages use loops. \n",
    "\n",
    "### *while* loops\n",
    "We use the reserved word while to make a while loop. It is used to execute a block of statements repeatedly until a given condition is satisfied. When the condition becomes false.\n",
    "\n",
    "```\n",
    "# syntax\n",
    "while condition:\n",
    "    code goes here\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c712aa88-67a1-4952-bbe8-724a1faacb5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "while condition failed at 4\n"
     ]
    }
   ],
   "source": [
    "# conditional loop example\n",
    "count = 0\n",
    "while count < 4:\n",
    "    print(count)\n",
    "    count = count + 1\n",
    "\n",
    "# code after loop will excute after condition becomes False\n",
    "print( f\"while condition failed at {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec4f785-cefe-4270-9faf-aa0fddf3e186",
   "metadata": {},
   "source": [
    "### *break* within a loop\n",
    "We use *break* when we like to get out of or stop the loop.\n",
    "\n",
    "\n",
    "```\n",
    "# syntax\n",
    "while condition:\n",
    "    code goes here\n",
    "    if another_condition:\n",
    "        break\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f74c8601-9d6a-4dc4-a684-188128393c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# break example\n",
    "count = 0\n",
    "while count < 5:\n",
    "    print(count)\n",
    "    count = count + 1\n",
    "    if count == 3:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18e9ef8-2ada-45f4-9655-baf4fbd86a79",
   "metadata": {},
   "source": [
    "### *for* loops\n",
    "A *for* loop is used for iterating over a sequence (that is either a list, a tuple, a dictionary, a set, or a string). \n",
    "\n",
    "```\n",
    "# syntax\n",
    "for iterator in lst:\n",
    "    code goes here\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543ace1b-39fa-44ca-b5a5-df94aeb4f7ed",
   "metadata": {},
   "source": [
    "*for* loop with a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "705839f1-57a9-484f-9361-42b2151d8de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# for loop with list example\n",
    "numbers = [0, 1, 2, 3, 4, 5]\n",
    "for num in numbers: # num is temporary name to refer to the list's items, valid only inside this loop\n",
    "    print(num)       # the numbers will be printed line by line, from 0 to 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438507bf-5046-49db-811a-10bdfbd76f6a",
   "metadata": {},
   "source": [
    "*for* loop with a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1b72c90-d47d-4c55-b6e8-139ae7caa98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P\n",
      "y\n",
      "t\n",
      "h\n",
      "o\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "# for loop with string example\n",
    "language = 'Python'\n",
    "for letter in language:\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3161b7de-bbbd-4a90-9480-a8cba7dfc06f",
   "metadata": {},
   "source": [
    "*for* loop with a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39ed02a6-1573-4a1f-8c93-dc7cd4e86a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "person = { 'first_name':'Asabeneh',\n",
    "           'last_name':'Yetayeh',\n",
    "           'age':250,\n",
    "           'country':'Finland'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c09faca7-f1fa-4552-aab5-3b90624ac3be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_name\n",
      "last_name\n",
      "age\n",
      "country\n"
     ]
    }
   ],
   "source": [
    "# with labels or keys\n",
    "for key in person.keys():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db678919-b20d-4fdf-8495-ba70a52a224f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asabeneh\n",
      "Yetayeh\n",
      "250\n",
      "Finland\n"
     ]
    }
   ],
   "source": [
    "# with values\n",
    "for key in person.values():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9dffd10c-ca92-46fd-9160-e1ddf07c12d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_name Asabeneh\n",
      "last_name Yetayeh\n",
      "age 250\n",
      "country Finland\n"
     ]
    }
   ],
   "source": [
    "# with both keys and values\n",
    "for key, value in person.items():\n",
    "    print(key, value) # this way we get both keys and values printed out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765e9c80-a9ff-43b1-8382-e5956556af03",
   "metadata": {},
   "source": [
    "### *continue* within a loop\n",
    "We use continue when we like to skip to the next iterator in the loop. *break* ends the loop while *continue* just skips to the next item. \n",
    "\n",
    "```\n",
    "# syntax\n",
    "for iterator in sequence:\n",
    "    code goes here\n",
    "    if condition:\n",
    "        continue\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "efb9fe72-ddc9-4de0-812a-48e85b68af2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Next number should be  1\n",
      "1\n",
      "Next number should be  2\n",
      "2\n",
      "Next number should be  3\n",
      "3\n",
      "4\n",
      "Next number should be  5\n",
      "outside the loop\n"
     ]
    }
   ],
   "source": [
    "numbers = (0,1,2,3,4)\n",
    "for number in numbers:\n",
    "    print(number)\n",
    "    if number == 3:\n",
    "        continue\n",
    "    print('Next number should be ', number + 1)\n",
    "print('outside the loop')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d019cb-b6f4-4277-b1cc-e85ff30d8e31",
   "metadata": {},
   "source": [
    "### *range()* function\n",
    "\n",
    "To loop through a set of code a specified number of times, we can use the *range()* function,\n",
    "The *range()* function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and ends at a specified number.\n",
    "\n",
    "```\n",
    "# syntax\n",
    "for iterator in range(start, end, step):\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbde9ac7-e778-45e8-9287-1ed2f0588566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# loop through 6 iterations\n",
    "for x in range(6):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee9e0ee-f42b-4f11-8df3-107384f2c426",
   "metadata": {},
   "source": [
    "### Nested loops\n",
    "We can write loops inside a loop.\n",
    "\n",
    "```\n",
    "# syntax\n",
    "for x in y:\n",
    "    for t in x:\n",
    "        print(t)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8988cf09-1149-4c05-b11c-f5235289cdb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "red apple\n",
      "red cherry\n",
      "red banana\n",
      "tasty apple\n",
      "tasty cherry\n",
      "tasty banana\n",
      "yellow apple\n",
      "yellow cherry\n",
      "yellow banana\n"
     ]
    }
   ],
   "source": [
    "adj = [\"red\", \"tasty\", \"yellow\"]\n",
    "fruits = [\"apple\", \"cherry\", \"banana\"]\n",
    "\n",
    "for x in adj:\n",
    "    for y in fruits:\n",
    "        print(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a31db0-05a4-421a-be11-8392cf8d685b",
   "metadata": {},
   "source": [
    "### *zip()* with *for* loops\n",
    "*zip()* is builtin function that is used to combine two or more iterables into a single iterable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e9407054-b24b-4149-aa2f-2cad66c324ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "red apple\n",
      "tasty cherry\n",
      "yellow banana\n"
     ]
    }
   ],
   "source": [
    "adj = [\"red\", \"tasty\", \"yellow\"]\n",
    "fruits = [\"apple\", \"cherry\", \"banana\"]\n",
    "\n",
    "for x, y in zip(adj,fruits):\n",
    "    print(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddaf3561-87f4-4515-8000-9ff8394a50aa",
   "metadata": {},
   "source": [
    "### *enumerate()* with *for* loops\n",
    "enumerate() lets you write Pythonic for loops when you need a count and the value from an iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4f607e36-4669-4268-9d8e-633e2677727f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index: 0, Fruit: apple\n",
      "Index: 1, Fruit: banana\n",
      "Index: 2, Fruit: orange\n",
      "Index: 3, Fruit: grape\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"apple\", \"banana\", \"orange\", \"grape\"]\n",
    "\n",
    "for index, fruit in enumerate(fruits):\n",
    "    print(f\"Index: {index}, Fruit: {fruit}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370d9a76-8bc4-40a8-931a-db9b06a55984",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 2.2: Multiplication Table Generator*\n",
    "From an input number generate a multiplication table. Use a loop to generate and display the multiplication table for that number from 1 to 10. For example:\n",
    "\n",
    "```\n",
    "Enter a number: 7\n",
    "Multiplication Table for 7\n",
    "---------------------------\n",
    "7 x 1 = 7\n",
    "7 x 2 = 14\n",
    "7 x 3 = 21\n",
    "7 x 4 = 28\n",
    "7 x 5 = 35\n",
    "7 x 6 = 42\n",
    "7 x 7 = 49\n",
    "7 x 8 = 56\n",
    "7 x 9 = 63\n",
    "7 x 10 = 70\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "86db6106-801a-4520-85b9-03a45d5bf68b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter a number 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiplication Table for 5\n",
      "---------------------------\n",
      "5 x 1 = 5\n",
      "5 x 2 = 10\n",
      "5 x 3 = 15\n",
      "5 x 4 = 20\n",
      "5 x 5 = 25\n",
      "5 x 6 = 30\n",
      "5 x 7 = 35\n",
      "5 x 8 = 40\n",
      "5 x 9 = 45\n",
      "5 x 10 = 50\n"
     ]
    }
   ],
   "source": [
    "# Task 2.2\n",
    "number = int(input(\"Enter a number\"))\n",
    "print(\"Multiplication Table for\", number)\n",
    "print(\"---------------------------\")\n",
    "\n",
    "for itr in range(1,11):\n",
    "    print(f\"{number} x {itr} = {number*itr}\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "a214b2b6-2f91-4730-a10b-9a38880a55b6",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 2.3: Element-wise Addition*\n",
    "\n",
    "1. Create two lists of equal length containing integers.\n",
    "\n",
    "2. Write a code that performs element-wise addition (1+10,2+20,...) of the corresponding elements from both lists using a loop and the zip() function.\n",
    "\n",
    "3. Store the results in a new list. (hint: create empty list prior to loop and `append()` to it)\n",
    "\n",
    "4. Print the original lists and the resulting list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "56cdbbd8-654b-4405-aa75-d2c195a2b078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The element wise addition of [1, 2, 3, 4, 5] and [10, 20, 30, 40, 50] is [11, 22, 33, 44, 55]\n"
     ]
    }
   ],
   "source": [
    "# Task 2.2\n",
    "list1 = [1, 2, 3, 4, 5]\n",
    "list2 = [10, 20, 30, 40, 50]\n",
    "\n",
    "result = []\n",
    "for itr1, itr2 in zip(list1,list2):\n",
    "    result.append(itr1+itr2)\n",
    "    \n",
    "print(f\"The element wise addition of {list1} and {list2} is {result}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69be1d69-0247-486f-a8ec-46b928ec79b3",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 2.4: Task Vowel Count*\n",
    "\n",
    "1. Ask the user to input a sentence or a word.\n",
    "\n",
    "2. Write a code that counts the number of vowels (a, e, i, o, u) in the input text using a loop and conditional statements.\n",
    "\n",
    "3. Print the count of vowels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "031e44a6-27a4-43a7-bb93-b1d90c2b4de1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter a sentence or word:  This is one\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 vowels in This is one\n"
     ]
    }
   ],
   "source": [
    "# Task 2.4\n",
    "text = input(\"Enter a sentence or word: \")\n",
    "\n",
    "vowels = ('a','e','i','o','u') \n",
    "vowel_count = 0\n",
    "\n",
    "for text_itr in text:\n",
    "    if text_itr in vowels:\n",
    "        vowel_count += 1 # this is shorthand for vowel_count = vowel_count + 1\n",
    "        \n",
    "print( f\"There are {vowel_count} vowels in {text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ef2c44-f25c-48fd-a0e2-90f7cae034c4",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 2.5: Dictionary Iteration with Logical Operator*\n",
    "1. Create a dictionary called students with multiple student names as keys and their corresponding ages as values.\n",
    "\n",
    "2. Implement a loop to iterate over the students dictionary with `items()`.\n",
    "\n",
    "3. Within the loop, use a logical operator (and, or, or not) to check if each student's age is greater than or equal to 18 and less than or equal to 25.\n",
    "\n",
    "4. Display appropriate messages based on the condition check:\n",
    "If a student's age is greater than or equal to 18 and less than or equal to 25, display a message like \"[student_name] is eligible for enrollment.\"\n",
    "For all other cases, display a message like \"[student_name] is not eligible for enrollment.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7e7ee72b-6f57-4f39-802e-31750f8d8718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adam is not eligible for enrollment\n",
      "Cathy is eligible for enrollment.\n",
      "Jon is not eligible for enrollment\n"
     ]
    }
   ],
   "source": [
    "# Task 2.5\n",
    "student_dict = {\n",
    "                'Adam':179,\n",
    "                'Cathy':20,\n",
    "                'Jon':26\n",
    "               }\n",
    "\n",
    "for stud_nm, stud_age in student_dict.items():\n",
    "    if stud_age>=18 and stud_age<=25:\n",
    "        print(f\"{stud_nm} is eligible for enrollment.\")\n",
    "    else:\n",
    "        print(f\"{stud_nm} is not eligible for enrollment\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7014109c-87f9-4b14-bdea-63e5236d5a39",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "<br><br>\n",
    "References:\n",
    "- https://www.w3schools.com/python/python_operators.asp\n",
    "- https://github.com/Asabeneh/30-Days-Of-Python/blob/master/09_Day_Conditionals/09_conditionals.md\n",
    "- https://www.w3schools.com/python/python_conditions.asp"
   ]
  }
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