0. Python Basics Review.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Main Python 3.8 documentation located here:</b><br>\n",
    "https://docs.python.org/3/\n",
    "\n",
    "<b>Python Library (builtin functions and types in depth):</b><br>\n",
    "https://docs.python.org/3.8/library/index.html\n",
    "\n",
    "<b>Python Tutorials:</b><br>\n",
    "https://docs.python.org/3/tutorial/index.html <br>\n",
    "https://www.w3schools.com/python/\n",
    "\n",
    "<b>Python Coding Standards</b><br>\n",
    "https://docs.python.org/3.8/tutorial/controlflow.html#intermezzo-coding-style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coding Standards"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Documentation:</b><br>\n",
    "https://docs.python.org/3.8/library/string.html<br>\n",
    "https://www.w3schools.com/python/python_strings.asp\n",
    "\n",
    "<b>Notes:</b>\n",
    "1. Escape special characters with \\\n",
    "2. Strings can be concatenated with +\n",
    "3. Strings can be repeated with *\n",
    "4. Strings laterals can span over multiple lines with triple quotes '''___'''\n",
    "5. Strings are indexed and can be sliced and accessed similarly to a list\n",
    "6. Strings are immutable so they can't be changed like lists\n",
    "7. String slicing is of form [start:end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ab'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' + 'b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'aaa'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ababab'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'ab' * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hello I'm George\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Hello I\\'m George'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>End of line characters are added automatically with string literals, to avoid add \\ at the end of the line</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This \\nIs \\nBasic\\nStrings Business\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This \n",
    "Is \n",
    "Basic\n",
    "Strings Business\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This Is BasicStrings Business'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This \\\n",
    "Is \\\n",
    "Basic\\\n",
    "Strings Business\\\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tes'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'test'\n",
    "s[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Will Throw an error because strings are immutable</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-640fc9a3a8b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'a'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "s[0] = 'a'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Examples can be found here: https://www.w3schools.com/python/python_lists.asp </b>\n",
    "\n",
    "There are four collection data types in the Python programming language:\n",
    "\n",
    "1. List is a collection which is ordered and changeable. Allows duplicate members.\n",
    "2. Tuple is a collection which is ordered and unchangeable. Allows duplicate members.\n",
    "3. Set is a collection which is unordered and unindexed. No duplicate members.\n",
    "4. Dictionary is a collection which is unordered, changeable and indexed. No duplicate members.\n",
    "\n",
    "When choosing a collection type, it is useful to understand the properties of that type. Choosing the right type for a particular data set could mean retention of meaning, and, it could mean an increase in efficiency or security."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist = [\"apple\", \"banana\", \"cherry\"]\n",
    "print(thislist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Lists can be sliced</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Lists can be changed</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist[0] = 'new Value'\n",
    "thislist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Positional values can also be negative, -1 would correpond to the last element in the list</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Reversing order and changing step size</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist[-1::-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Freelancing with the code</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "len(thislist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thislist.append('Another Value')\n",
    "thislist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "list1 = [\"a\", \"b\" , \"c\"]\n",
    "list2 = ['d', 2, 3]\n",
    "\n",
    "list3 = list1 + list2\n",
    "print(list3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### List Comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "letters = []\n",
    "\n",
    "for l in 'George':\n",
    "    letters.append(l)\n",
    "\n",
    "print(letters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Python allows for shortcuts if the output is a list</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "letters=[l for l in 'George']\n",
    "letters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>List Comprehensions can get complex</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "num_list = [y for y in range(100) if y % 2 == 0 if y % 5 == 0]\n",
    "print(num_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "obj = [\"Even\" if i%2==0 else \"Odd\" for i in range(10)]\n",
    "print(obj)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>List Methods Summary:</b><br>\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>A tuple is a collection which is ordered and unchangeable. In Python tuples are written with round brackets.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "print(thistuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\")\n",
    "print(thistuple[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thistuple = (\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\")\n",
    "print(thistuple[2:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>A set is a collection which is unordered and unindexed. In Python sets are written with curly brackets.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "for x in thisset:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Once a set is created, you cannot change its items, but you can add new items.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
    "\n",
    "thisset.add(\"orange\")\n",
    "\n",
    "print(thisset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>A dictionary is a collection which is unordered, changeable and indexed. In Python dictionaries are written with curly brackets, and they have keys and values.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>You can access the items of a dictionary by referring to its key name, inside square brackets:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print(thisdict[\"model\"])\n",
    "#or use get method\n",
    "print(thisdict.get(\"model\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Values can be changed as following</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict[\"year\"] = 2018\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Looping through a dictionary</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for x in thisdict:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for x in thisdict:\n",
    "    print(thisdict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for x in thisdict.values():\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Loop through both keys and values, by using the items() method:\n",
    "for x, y in thisdict.items():\n",
    "    print(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#adding items\n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "thisdict[\"color\"] = \"red\"\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#The del keyword removes the item with the specified key name:\n",
    "thisdict = {\n",
    "  \"brand\": \"Ford\",\n",
    "  \"model\": \"Mustang\",\n",
    "  \"year\": 1964\n",
    "}\n",
    "del thisdict[\"model\"]\n",
    "print(thisdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Dictionaries can be composed of dictionaries, they are called nested dictionaries</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "myfamily = {\n",
    "  \"child1\" : {\n",
    "    \"name\" : \"Emil\",\n",
    "    \"year\" : 2004\n",
    "  },\n",
    "  \"child2\" : {\n",
    "    \"name\" : \"Tobias\",\n",
    "    \"year\" : 2007\n",
    "  },\n",
    "  \"child3\" : {\n",
    "    \"name\" : \"Linus\",\n",
    "    \"year\" : 2011\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "child1 = {\n",
    "  \"name\" : \"Emil\",\n",
    "  \"year\" : 2004\n",
    "}\n",
    "child2 = {\n",
    "  \"name\" : \"Tobias\",\n",
    "  \"year\" : 2007\n",
    "}\n",
    "child3 = {\n",
    "  \"name\" : \"Linus\",\n",
    "  \"year\" : 2011\n",
    "}\n",
    "\n",
    "myfamily = {\n",
    "  \"child1\" : child1,\n",
    "  \"child2\" : child2,\n",
    "  \"child3\" : child3\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "myfamily['child1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "myfamily['child1']['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Control Flow Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.python.org/3/tutorial/controlflow.html<br>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## if Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = 42\n",
    "\n",
    "if x < 0:\n",
    "    x = 0\n",
    "    print('Negative')\n",
    "elif x==0:\n",
    "    print('Zero')\n",
    "else:\n",
    "    print('Positive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>If statement can be written in 1 line</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "price = 10\n",
    "x='expensive' if price > 20 else 'cheap'\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>More complex one line if statement</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print('expensive' if price > 20 else 'manageable' if price > 10 else 'cheap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## for Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Python will identify the code that's related to the loop with indents</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "words = ['cat', 'window', 'defenestrate']\n",
    "\n",
    "for w in words:\n",
    "    print(w, len(w))\n",
    "\n",
    "#code below isn't indented so it's not relevant to for loop\n",
    "print('hello')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Combining if and for statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'words' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-34cbdb29db7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mw\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'window'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Found a window'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'words' is not defined"
     ]
    }
   ],
   "source": [
    "for w in words:\n",
    "    if w == 'window':\n",
    "        a = 'Found a window'\n",
    "        print(a)\n",
    "    \n",
    "    #this code isn't under if statement but under for loop\n",
    "    print(w)\n",
    "    #expected to print 'cat', 'Found a window' and the rest of the words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## range()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Range will generate numbers from start (inclusive) if provided to stop number (exclusive), step can also be provided</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for i in range(0, 10, 2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## while Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Unlike for loop where we know how many times we will execute the code while loop will continue executing until condition is met</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "num = 5\n",
    "# Let's say we want to keep squaring the number untill the number is above 1,000,000.\n",
    "# We don't know how many iterations it might take therefore it's advised to use a while loop\n",
    "\n",
    "while num < 1000000:\n",
    "    print(num)\n",
    "    num = num ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Keep in mind since the loop is controled by a condition, if the condition is never met it will result in the infinite loop.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Functions are defined as following:</b>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def func_name(v):\n",
    "    code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.\n",
    "\n",
    "The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings.) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.\n",
    "\n",
    "The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.\n",
    "\n",
    "The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). 1 When a function calls another function, a new local symbol table is created for that call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fib(n):    # write Fibonacci series up to n\n",
    "     \"\"\"Print a Fibonacci series up to n.\"\"\"\n",
    "     a, b = 0, 1\n",
    "     while a < n:\n",
    "         print(a, end=' ')\n",
    "         a, b = b, a+b\n",
    "     print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 1 2 3 5 8 13 \n"
     ]
    }
   ],
   "source": [
    "fib(19)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function Arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ask_ok(prompt, retries=4, reminder='Please try again!'):\n",
    "    while True:\n",
    "        ok = input(prompt)\n",
    "        if ok in ('y', 'ye', 'yes'):\n",
    "            return True\n",
    "        if ok in ('n', 'no', 'nop', 'nope'):\n",
    "            return False\n",
    "        retries = retries - 1\n",
    "        if retries < 0:\n",
    "            raise ValueError('invalid user response')\n",
    "        print(reminder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword.\n",
    "\n",
    "A function definition may look like:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):\n",
    "      -----------    ----------     ----------\n",
    "        |             |                  |\n",
    "        |        Positional or keyword   |\n",
    "        |                                - Keyword only\n",
    "         -- Positional only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword.\n",
    "\n",
    "<b> Summary</b>\n",
    "1. arguments before / are positional only\n",
    "2. arguments after * are keyword only\n",
    "3. arguments without specifications can be called by keyword or position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):\n",
    "    print(\"-- This parrot wouldn't\", action, end=' ')\n",
    "    print(\"if you put\", voltage, \"volts through it.\")\n",
    "    print(\"-- Lovely plumage, the\", type)\n",
    "    print(\"-- It's\", state, \"!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- This parrot wouldn't voom if you put 1000 volts through it.\n",
      "-- Lovely plumage, the Norwegian Blue\n",
      "-- It's a stiff !\n",
      "-- This parrot wouldn't voom if you put 1000 volts through it.\n",
      "-- Lovely plumage, the Norwegian Blue\n",
      "-- It's a stiff !\n",
      "-- This parrot wouldn't VOOOOOM if you put 1000000 volts through it.\n",
      "-- Lovely plumage, the Norwegian Blue\n",
      "-- It's a stiff !\n",
      "-- This parrot wouldn't VOOOOOM if you put 1000000 volts through it.\n",
      "-- Lovely plumage, the Norwegian Blue\n",
      "-- It's a stiff !\n",
      "-- This parrot wouldn't jump if you put a million volts through it.\n",
      "-- Lovely plumage, the Norwegian Blue\n",
      "-- It's bereft of life !\n",
      "-- This parrot wouldn't voom if you put a thousand volts through it.\n",
      "-- Lovely plumage, the Norwegian Blue\n",
      "-- It's pushing up the daisies !\n"
     ]
    }
   ],
   "source": [
    "parrot(1000)                                          # 1 positional argument\n",
    "parrot(voltage=1000)                                  # 1 keyword argument\n",
    "parrot(voltage=1000000, action='VOOOOOM')             # 2 keyword arguments\n",
    "parrot(action='VOOOOOM', voltage=1000000)             # 2 keyword arguments\n",
    "parrot('a million', 'bereft of life', 'jump')         # 3 positional arguments\n",
    "parrot('a thousand', state='pushing up the daisies')  # 1 positional, 1 keyword"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Unpacking Argument Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>The built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the *-operator to unpack the arguments out of a list or tuple:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args = [3, 6]\n",
    "list(range(*args))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 6\n",
      "[3, 6]\n"
     ]
    }
   ],
   "source": [
    "print(*args)\n",
    "print(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>In the same fashion, dictionaries can deliver keyword arguments with the **-operator</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !\n"
     ]
    }
   ],
   "source": [
    "def parrot(voltage, state='a stiff', action='voom'):\n",
    "    print(\"-- This parrot wouldn't\", action, end=' ')\n",
    "    print(\"if you put\", voltage, \"volts through it.\", end=' ')\n",
    "    print(\"E's\", state, \"!\")\n",
    "    \n",
    "d = {\"voltage\": \"four million\", \"state\": \"bleedin' demised\", \"action\": \"VOOM\"}\n",
    "parrot(**d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Lambda Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b. Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_incrementor(n):\n",
    "    return lambda x: x + n\n",
    "\n",
    "f = make_incrementor(42)\n",
    "f(1)"
   ]
  }
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