Day_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Bootcamp Day 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is Python and why is it so popular ?\n",
    "\n",
    "Python is an interpreted, high-level, general-purpose programming language.\n",
    "\n",
    "Why is Python so Popular?\n",
    " - 1) Easy to Learn and Use\n",
    " - 2) Mature and Supportive Python Community\n",
    " - 3) Support from Renowned Corporate Sponsors - Facebook, AWS, Google\n",
    " - 4) Hundreds of Python Libraries and Frameworks\n",
    " - 5) Versatility, Efficiency, Reliability, and Speed\n",
    " - 6) Big data, Machine Learning and Cloud Computing\n",
    " - 7) The Flexibility of Python Language\n",
    " - 8) Use of python in academics\n",
    " - 9) Automation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 3 - We will be using Python3 throughout this course\n",
    "\n",
    "Py 2 vs Py3 - https://www.geeksforgeeks.org/important-differences-between-python-2-x-and-python-3-x-with-examples/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linting\n",
    "lint, or a linter, is a tool that analyzes source code to flag programming errors, bugs, stylistic errors, and \n",
    "suspicious constructs.\n",
    "\n",
    "https://www.pylint.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primitive data types & Data Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primitive data types\n",
    "\n",
    "Python has four primitive data types:\n",
    " - Integer\n",
    " - Float\n",
    " - String\n",
    " - Boolean\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numbers\n",
    "\n",
    "Python has various \"types\" of numbers (numeric literals). We'll mainly focus on integers and floating point numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 2\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 2.1\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Addition\n",
    "2+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subtraction\n",
    "2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiplication\n",
    "2*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Division\n",
    "3/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Floor Division\n",
    "7//4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mod Operator\n",
    "25%10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Powers\n",
    "2**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can use parentheses to specify orders\n",
    "(2+10) * (10+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assignment\n",
    "service_line = 'iLabs'\n",
    "type(service_line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Booleans\n",
    "\n",
    "Python  comes with Booleans (with predefined True and False displays that are basically just the integers 1 and 0). It also has a placeholder object called None. Let's walk through a few quick examples of Booleans (we will dive deeper into them later in this course)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set object to be a boolean\n",
    "a = True\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use comparison operators to create booleans. We will go over all the comparison operators later on in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output is boolean\n",
    "1 > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a) == bool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use None as a placeholder for an object that we don't want to reassign yet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# None placeholder\n",
    "b = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "# Show\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strings in Python are actually a *sequence*. For example, Python understands the string \"hello' to be a sequence of letters in a specific order. \n",
    "\n",
    "We'll learn about the following:\n",
    "\n",
    "    1.) Creating Strings\n",
    "    2.) String Indexing and Slicing\n",
    "    3.) String Properties\n",
    "    4.) String Methods\n",
    "    5.) Print Formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a String\n",
    "To create a string in Python you need to use either single quotes or double quotes. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "a = 'iLabs'\n",
    "b = \"iLabs rocks\"\n",
    "print(type(a))\n",
    "print(type(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason for the error above is because the single quote in <code>I'm</code> stopped the string. You can use combinations of double and single quotes to get the complete statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Now I'm ready to use the single quotes inside a string!\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# escaping quotes\n",
    "'Now I\\'m ready to use the single quotes inside a string!'\n",
    "\n",
    "# or use double quotes\n",
    "\"Now I'm ready to use the single quotes inside a string!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## String Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a function called len() to check the length of a string!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('Hello World')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python's built-in len() function counts all of the characters in the string, including spaces and punctuation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## String Indexing\n",
    "We know strings are a sequence, which means Python can use indexes to call parts of the sequence. Let's learn how this works.\n",
    "\n",
    "In Python, we use brackets <code>[]</code> after an object to call its index. We should also note that indexing starts at 0 for Python. Let's create a new object called <code>s</code> and then walk through a few examples of indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign name as a string\n",
    "dept = 'Innovation and platforms'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start indexing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show first element (in this case a letter)\n",
    "dept[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a <code>:</code> to perform *slicing* which grabs everything up to a designated point. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nnovation and platforms'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything past the first term all the way to the length of s which is len(s)\n",
    "dept[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Innovation and platforms'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that there is no change to the original s\n",
    "dept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Inn'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything UP TO the 3rd index\n",
    "dept[:3] # start from index 0 and go till index 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the above slicing. Here we're telling Python to grab everything from 0 up to 3. It doesn't include the 3rd index. You'll notice this a lot in Python, where statements and are usually in the context of \"up to, but not including\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Everything\n",
    "dept[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ovati'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept[3:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use negative indexing to go backwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'m'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last letter (one index behind 0 so it loops back around)\n",
    "dept[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Innovation and platform'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything but the last letter\n",
    "dept[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use index and slice notation to grab elements of a sequence by a specified step size (the default is 1). For instance we can use two colons in a row and then a number specifying the frequency to grab elements. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Invto n ltom'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything, but go in steps size of 1\n",
    "dept[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Iot'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything, but go in step sizes of 2\n",
    "dept[0:9:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'smroftalp dna noitavonnI'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use this to print a string backwards\n",
    "dept[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## String Properties\n",
    "It's important to note that strings have an important property known as *immutability*. This means that once a string is created, the elements within it can not be changed or replaced. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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-39-69f3c79a9bd7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Let's try to change the first letter to 'x'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdept\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'x'\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": [
    "# Let's try to change the first letter to 'x'\n",
    "dept[0] = 'x'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the error tells us directly what we can't do, change the item assignment!\n",
    "\n",
    "Something we *can* do is concatenate strings!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Innovation and platforms rocks!'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate strings!\n",
    "dept + ' rocks!'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the multiplication symbol to create repetition!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "letter = 'z'\n",
    "letter*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'IoT'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept = 'IoT'\n",
    "dept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Built-in String methods\n",
    "\n",
    "Objects in Python usually have built-in methods. These methods are functions inside the object (we will learn about these in much more depth later) that can perform actions or commands on the object itself.\n",
    "\n",
    "We call methods with a period and then the method name. Methods are in the form:\n",
    "object.method(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'INNOVATION AND PLATFORM'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upper Case a string\n",
    "dept = 'Innovation and platform'\n",
    "dept.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'innovation and platform'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lower case\n",
    "dept.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Innovation', 'and', 'platform']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split a string by blank space (this is the default)\n",
    "dept.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Innov', 'tion ', 'nd pl', 'tform']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split by a specific element (doesn't include the element that was split on)\n",
    "dept.split('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many more methods than the ones covered here. Visit the Advanced String section to find out more!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print Formatting\n",
    "\n",
    "We can use the .format() method to add formatted objects to printed string statements. \n",
    "\n",
    "The easiest way to show this is through an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Insert another string with curly brackets: second and The inserted string'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Insert another string with curly brackets: {1} and {0}'.format('The inserted string','second')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists\n",
    "\n",
    "Lists can be thought of the most general version of a *sequence* in Python. Unlike strings, they are mutable, meaning the elements inside a list can be changed!\n",
    "\n",
    "We will learn about:    \n",
    "    1.) Creating lists\n",
    "    2.) Indexing and Slicing Lists\n",
    "    3.) Basic List Methods\n",
    "    4.) Nesting Lists\n",
    "\n",
    "Lists are constructed with brackets [] and commas separating every element in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign a list to an variable named my_list\n",
    "my_list = []\n",
    "print(my_list)\n",
    "type(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just created a list of integers, but lists can actually hold different object types. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A string', 23, 100.232, 'o']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = ['A string',23,100.232,'o']\n",
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like strings, the len() function will tell you how many items are in the sequence of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing and Slicing\n",
    "Indexing and slicing work just like in strings. Let's make a new list to remind ourselves of how this works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = ['one','two','three',4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'one'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab element at index 0\n",
    "my_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['two', 'three', 4, 5]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab index 1 and everything past it\n",
    "my_list[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything UP TO index 3\n",
    "my_list[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use + to concatenate lists, just like we did for strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three', 4, 5, 'new item']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list + ['new item']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This doesn't actually change the original list!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three', 4, 5]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would have to reassign the list to make the change permanent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reassign\n",
    "my_list = my_list + ['add new item permanently']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three', 4, 5, 'add new item permanently']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the * for a duplication method similar to strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three', 4, 5, 'one', 'two', 'three', 4, 5]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make the list double\n",
    "my_list * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two', 'three', 4, 5]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Again doubling not permanent\n",
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic List Methods\n",
    "\n",
    "If you are familiar with another programming language, you might start to draw parallels between arrays in another language and lists in Python. Lists in Python however, tend to be more flexible than arrays in other languages for a two good reasons: they have no fixed size (meaning we don't have to specify how big a list will be), and they have no fixed type constraint (like we've seen above).\n",
    "\n",
    "Let's go ahead and explore some more special methods for lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new list\n",
    "list1 = [1,2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the **append** method to permanently add an item to the end of a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 'append me!']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append\n",
    "list1.append('append me!')\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "list1.append([3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 'append me!', [3, 4, 5]]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 'append me!', [3, 4, 5], 7, 8, 9]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extend\n",
    "list1.extend([7,8,9])\n",
    "list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use **pop** to \"pop off\" an item from the list. By default pop takes off the last index, but you can also specify which index to pop off. Let's see an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pop off the 0 indexed item\n",
    "list1.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 'append me!', [3, 4, 5], 7, 8, 9]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign the popped element, remember default popped index is -1\n",
    "popped_item = list1.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popped_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 'append me!', [3, 4, 5], 7, 8]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show remaining list\n",
    "list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It should also be noted that lists indexing will return an error if there is no element at that index. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-77-af6d2015fa1f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlist1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "list1[100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the **sort** method and the **reverse** methods to also effect your lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = ['a','e','x','b','c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'e', 'x', 'b', 'c']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show\n",
    "new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use reverse to reverse order (this is permanent!)\n",
    "new_list.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c', 'b', 'x', 'e', 'a']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use sort to sort the list (in this case alphabetical order, but for numbers it will go ascending)\n",
    "new_list.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'e', 'x']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nesting Lists\n",
    "A great feature of of Python data structures is that they support *nesting*. This means we can have data structures within data structures. For example: A list inside a list.\n",
    "\n",
    "Let's see how this works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make three lists\n",
    "lst_1=[1,2,3]\n",
    "lst_2=[4,5,6]\n",
    "lst_3=[7,8,9]\n",
    "\n",
    "# Make a list of lists to form a matrix\n",
    "matrix = [lst_1,lst_2,lst_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again use indexing to grab elements, but now there are two levels for the index. The items in the matrix object, and then the items inside that list!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab first item in matrix object\n",
    "matrix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab first item of the first item in the matrix object\n",
    "matrix[2][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dictionaries\n",
    "\n",
    "Mappings are a collection of objects that are stored by a *key*, unlike a sequence that stored objects by their relative position. This is an important distinction, since mappings won't retain order since they have objects defined by a key.\n",
    "\n",
    "A Python dictionary consists of a key and then an associated value. That value can be almost any Python object.\n",
    "\n",
    "Topics:\n",
    "     - 1.) Constructing a Dictionary\n",
    "     - 2.) Accessing objects from a dictionary\n",
    "     - 3.) Nesting Dictionaries\n",
    "     - 4.) Basic Dictionary Methods\n",
    "\n",
    "## Constructing a Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 'value1', 'key2': 'value2'}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a dictionary with {} and : to signify a key and a value\n",
    "my_dict = {'key1':'value1','key2':'value2'}\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-2e042f7087f7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "my_dict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value2'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call values by their key\n",
    "my_dict['key2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its important to note that dictionaries are very flexible in the data types they can hold. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = {'key1':123,'key2':[12,23,33],'key3':['item0','item1','item2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-2e042f7087f7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "my_dict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['item0', 'item1', 'item2']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's call items from the dictionary\n",
    "my_dict['key3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'item0'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can call an index on that value\n",
    "my_dict['key3'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ITEM0'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can then even call methods on that value\n",
    "my_dict['key3'][0].upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can affect the values of a key as well. For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['key1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': 123, 'key2': [12, 23, 33], 'key3': ['item0', 'item1', 'item2']}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subtract 123 from the value\n",
    "my_dict['key1'] = my_dict['key1'] - 123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check\n",
    "my_dict['key1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick note, Python has a built-in method of doing a self subtraction or addition (or multiplication or division). We could have also used += or -= for the above statement. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-123"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the object equal to itself minus 123 \n",
    "my_dict['key1'] -= 123\n",
    "my_dict['key1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create keys by assignment. For instance if we started off with an empty dictionary, we could continually add to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dictionary\n",
    "d = {}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'animal': 'Dog'}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new key through assignment\n",
    "d['animal'] = 'Dog'\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can do this with any object\n",
    "d['answer'] = 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'animal': 42, 'answer': 'Yes'}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nesting with Dictionaries\n",
    "\n",
    "Hopefully you're starting to see how powerful Python is with its flexibility of nesting objects and calling methods on them. Let's see a dictionary nested inside a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary nested inside a dictionary nested inside a dictionary\n",
    "d = {'key1':{'nestkey':{'subnestkey':'value'}}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! That's a quite the inception of dictionaries! Let's see how we can grab that value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep calling the keys\n",
    "d['key1']['nestkey']['subnestkey']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few Dictionary Methods\n",
    "\n",
    "There are a few methods we can call on a dictionary. Let's get a quick introduction to a few of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a typical dictionary\n",
    "d = {'key1':1,'key2':2,'key3':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['key1', 'key2', 'key3'])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method to return a list of all keys \n",
    "d.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([1, 2, 3])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method to grab all values\n",
    "d.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('key1', 1), ('key2', 2), ('key3', 3)])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method to return tuples of all items  (we'll learn about tuples soon)\n",
    "d.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuples\n",
    "\n",
    "In Python tuples are very similar to lists, however, unlike lists they are *immutable* meaning they can not be changed. You would use tuples to present things that shouldn't be changed, such as days of the week, or dates on a calendar. \n",
    "\n",
    "Topics:\n",
    "    1.) Constructing Tuples\n",
    "    2.) Basic Tuple Methods\n",
    "    3.) Immutability\n",
    "    4.) When to Use Tuples\n",
    "\n",
    "\n",
    "## Constructing Tuples\n",
    "The construction of a tuples use () with elements separated by commas. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a tuple\n",
    "t = (1,2,3)\n",
    "t\n",
    "list(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check len just like a list\n",
    "len(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('one', 2)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also mix object types\n",
    "t = ('one',2)\n",
    "\n",
    "# Show\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use indexing just like we did in lists\n",
    "t[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing just like a list\n",
    "t[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Tuple Methods\n",
    "\n",
    "Tuples have built-in methods, but not as many as lists do. Let's look at two of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('one', 2)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .index to enter a value and return the index\n",
    "t.index('one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .count to count the number of times a value appears\n",
    "t.count('one')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Immutability\n",
    "\n",
    "It can't be stressed enough that tuples are immutable. To drive that point home:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' 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-94-1257c0aa9edd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\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'change'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "t[0]= 'change'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of this immutability, tuples can't grow. Once a tuple is made we can not add to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'append'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-95-b75f5b09ac19>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'nope'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
     ]
    }
   ],
   "source": [
    "t.append('nope')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sets\n",
    "\n",
    "Sets are an unordered collection of *unique* elements. We can construct them by using the set() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = set()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We add to sets with the add() method\n",
    "x.add(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the curly brackets. This does not indicate a dictionary! Although you can draw analogies as a set being a dictionary with only keys.\n",
    "\n",
    "We know that a set has only unique entries. So what happens when we try to add something that is already in a set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a different element\n",
    "x.add(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to add the same element\n",
    "x.add(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how it won't place another 1 there. That's because a set is only concerned with unique elements! We can cast a list with multiple repeat elements to a set to get the unique elements. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 2, 3, 4, 5, 6, 1, 1]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list with repeats\n",
    "list1 = [1,1,2,2,3,4,5,6,1,1]\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 6}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cast as set to get unique values\n",
    "set(list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How are these implemented in Python -\n",
    "\n",
    "https://docs.python.org/2/faq/design.html#design-and-history-faq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Files\n",
    "\n",
    "Python uses file objects to interact with external files on your computer. These file objects can be any sort of file you have on your computer, whether it be an audio file, a text file, emails, Excel documents, etc. Note: You will probably need to install certain libraries or modules to interact with those various file types, but they are easily available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Opening a file\n",
    "\n",
    "Let's being by opening the file test.txt that is located in the same directory as this notebook. For now we will work with files located in the same directory as the notebook or .py script you are using.\n",
    "\n",
    "It is very easy to get an error on this step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Users\\\\abhranjan\\\\Desktop\\\\Python_Training'"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_file = open('D:\\\\Users\\\\abhranjan\\\\Documents\\\\sample_text.txt',encoding='UTF-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alternatively, to grab files from any location on your computer, simply pass in the entire file path. **\n",
    "\n",
    "For Windows you need to use double \\ so python doesn't treat the second \\ as an escape character, a file path is in the form:\n",
    "\n",
    "    myfile = open(\"C:\\\\Users\\\\YourUserName\\\\Home\\\\Folder\\\\myfile.txt\")\n",
    "\n",
    "For MacOS and Linux you use slashes in the opposite direction:\n",
    "\n",
    "    myfile = open(\"/Users/YouUserName/Folder/myfile.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Why is Python so Popular?\\n1) Easy to Learn and Use\\nPython language is incredibly easy to use and learn for new beginners and newcomers. The python language is one of the most accessible programming languages available because it has simplified syntax and not complicated, which gives more emphasis on natural language. Due to its ease of learning and usage, python codes can be easily written and executed much faster than other programming languages.\\n\\nWhen Guido van Rossum was creating python in the 1980s, he made sure to design it to be a general-purpose language. One of the main reasons for the popularity of python would be its simplicity in syntax so that it could be easily read and understood even by amateur developers also.\\n\\nOne can also quickly experiment by changing the code base of python because it is an interpreted language which makes it even more popular among all kinds of developers.\\n\\n2) Mature and Supportive Python Community\\nPython was created more than 30 years ago, which is a lot of time for any community of programming language to grow and mature adequately to support developers ranging from beginner to expert levels. There are plenty of documentation, guides and Video Tutorials for Python language are available that learner and developer of any skill level or ages can use and receive the support required to enhance their knowledge in python programming language.\\n\\nMany students get introduced to computer science only through Python language, which is the same language used for in-depth research projects.\\n\\nIf any programming language lacks developer support or documentation, then they don’t grow much. But python has no such kind of problems because it has been here for a very long time. The python developer community is one of the most incredibly active programming language communities.\\n\\nThis means that if somebody has an issue with python language, they can get instant support from developers of all levels ranging from beginner to expert in the community. Getting help on time plays a vital role in the development of the project, which otherwise might cause delays.\\n\\n3) Support from Renowned Corporate Sponsors\\nProgramming languages grows faster when a corporate sponsor backs it. For example, PHP is backed by Facebook, Java by Oracle and Sun, Visual Basic & C# by Microsoft. Python Programming language is heavily backed by Facebook, Amazon Web Services, and especially Google.\\n\\nGoogle adopted python language way back in 2006 and have used it for many applications and platforms since then. Lots of Institutional effort and money have been devoted to the training and success of the python language by Google. They have even created a dedicated portal only for python. The list of support tools and documentation keeps on growing for python language in the developers’ world.\\n\\n4) Hundreds of Python Libraries and Frameworks\\nDue to its corporate sponsorship and big supportive community of python, python has excellent libraries that you can use to select and save your time and effort on the initial cycle of development. There are also lots of cloud media services that offer cross-platform support through library-like tools, which can be extremely beneficial.\\n\\nLibraries with specific focus are also available like nltk for natural language processing or scikit-learn for machine learning applications.\\n\\nThere are many frameworks and libraries are available for python language, such as:\\n\\nmatplotib for plotting charts and graphs\\nSciPy for engineering applications, science, and mathematics\\nBeautifulSoup for HTML parsing and XML\\nNumPy for scientific computing\\nDjango for server-side web development\\n5) Versatility, Efficiency, Reliability, and Speed\\nAsk any python developer, and they will wholeheartedly agree that the python language is efficient, reliable, and much faster than most modern languages. Python can be used in nearly any kind of environment, and one will not face any kind of performance loss issue irrespective of the platform one is working.\\n\\nOne more best thing about versatility of python language is that it can be used in many varieties of environments such as mobile applications, desktop applications, web development, hardware programming, and many more. The versatility of python makes it more attractive to use due to its high number of applications.\\n\\n6) Big data, Machine Learning and Cloud Computing\\nCloud Computing, Machine Learning, and Big Data are some of the hottest trends in the computer science world right now, which helps lots of organizations to transform and improve their processes and workflows.\\n\\nPython language is the second most popular used tool after R language for data science and analytics. Lots of many data processing workloads in the organization are powered by python language only. Most of the research and development takes place in python language due to its many applications, including ease of analyzing and organizing the usable data.\\n\\nNot only this, but hundreds of python libraries are being used in thousands of machine learning projects every day, such as TensorFlow for neural networks and OpenCV for computer vision, etc.\\n\\n7) First-choice Language\\nPython language is the first choice for many programmers and students due to the main reason for python being in high demand in the development market. Students and developers always look forward to learning a language that is in high demand. Python is undoubtedly the hottest cake in the market now.\\n\\nMany programmers and data science students are using python language for their development projects. Learning python is one of the important section in data science certification courses.  In this way, the python language can provide plenty of fantastic career opportunities for students. Due to the variety of applications of python, one can pursue different career options and will not remain stuck to one.\\n\\n8) The Flexibility of Python Language\\nThe python language is so flexible that it gives the developer the chance to try something new. The person who is an expert in python language is not just limited to build similar kinds of things but can also go on to try to make something different than before.\\n\\nPython doesn’t restrict developers from developing any sort of application. This kind of freedom and flexibility by just learning one language is not available in other programming languages.\\n\\n9) Use of python in academics\\nNow python language is being treated as the core programming language in schools and colleges due to its countless uses in Artificial Intelligence, Deep Learning, Data Science, etc. It has now become a fundamental part of the development world that schools and colleges cannot afford not to teach python language.\\n\\nIn this way, it is increasing more python Developers and Programmers and thus further expanding its growth and popularity. \\n\\n10) Automation \\nPython language can help a lot in automation of tasks as there are lots of tools and modules available, which makes things much more comfortable. It is incredible to know that one can reach an advanced level of automation easily by just using necessary python codes.\\n\\nPython is the best performance booster in the automation of software testing also. One will be amazed at how much less time and few numbers of lines are required to write codes for automation tools.\\n\\nConclusion\\nSome of the reasons why Python is growing at a supersonic speed. We hope this article has shed some good light on python language and its importance. So if anyone asks you “why python programming?” you have an essay answer ready.'"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can now read the file\n",
    "my_file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# But what happens if we try to read it again?\n",
    "my_file.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This happens because you can imagine the reading \"cursor\" is at the end of the file after having read it. So there is nothing left to read. We can reset the \"cursor\" like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seek to the start of file (index 0)\n",
    "my_file.seek(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Why is Python so Popular?\\n1) Easy to Learn and Use\\nPython language is incredibly easy to use and learn for new beginners and newcomers. The python language is one of the most accessible programming languages available because it has simplified syntax and not complicated, which gives more emphasis on natural language. Due to its ease of learning and usage, python codes can be easily written and executed much faster than other programming languages.\\n\\nWhen Guido van Rossum was creating python in the 1980s, he made sure to design it to be a general-purpose language. One of the main reasons for the popularity of python would be its simplicity in syntax so that it could be easily read and understood even by amateur developers also.\\n\\nOne can also quickly experiment by changing the code base of python because it is an interpreted language which makes it even more popular among all kinds of developers.\\n\\n2) Mature and Supportive Python Community\\nPython was created more than 30 years ago, which is a lot of time for any community of programming language to grow and mature adequately to support developers ranging from beginner to expert levels. There are plenty of documentation, guides and Video Tutorials for Python language are available that learner and developer of any skill level or ages can use and receive the support required to enhance their knowledge in python programming language.\\n\\nMany students get introduced to computer science only through Python language, which is the same language used for in-depth research projects.\\n\\nIf any programming language lacks developer support or documentation, then they don’t grow much. But python has no such kind of problems because it has been here for a very long time. The python developer community is one of the most incredibly active programming language communities.\\n\\nThis means that if somebody has an issue with python language, they can get instant support from developers of all levels ranging from beginner to expert in the community. Getting help on time plays a vital role in the development of the project, which otherwise might cause delays.\\n\\n3) Support from Renowned Corporate Sponsors\\nProgramming languages grows faster when a corporate sponsor backs it. For example, PHP is backed by Facebook, Java by Oracle and Sun, Visual Basic & C# by Microsoft. Python Programming language is heavily backed by Facebook, Amazon Web Services, and especially Google.\\n\\nGoogle adopted python language way back in 2006 and have used it for many applications and platforms since then. Lots of Institutional effort and money have been devoted to the training and success of the python language by Google. They have even created a dedicated portal only for python. The list of support tools and documentation keeps on growing for python language in the developers’ world.\\n\\n4) Hundreds of Python Libraries and Frameworks\\nDue to its corporate sponsorship and big supportive community of python, python has excellent libraries that you can use to select and save your time and effort on the initial cycle of development. There are also lots of cloud media services that offer cross-platform support through library-like tools, which can be extremely beneficial.\\n\\nLibraries with specific focus are also available like nltk for natural language processing or scikit-learn for machine learning applications.\\n\\nThere are many frameworks and libraries are available for python language, such as:\\n\\nmatplotib for plotting charts and graphs\\nSciPy for engineering applications, science, and mathematics\\nBeautifulSoup for HTML parsing and XML\\nNumPy for scientific computing\\nDjango for server-side web development\\n5) Versatility, Efficiency, Reliability, and Speed\\nAsk any python developer, and they will wholeheartedly agree that the python language is efficient, reliable, and much faster than most modern languages. Python can be used in nearly any kind of environment, and one will not face any kind of performance loss issue irrespective of the platform one is working.\\n\\nOne more best thing about versatility of python language is that it can be used in many varieties of environments such as mobile applications, desktop applications, web development, hardware programming, and many more. The versatility of python makes it more attractive to use due to its high number of applications.\\n\\n6) Big data, Machine Learning and Cloud Computing\\nCloud Computing, Machine Learning, and Big Data are some of the hottest trends in the computer science world right now, which helps lots of organizations to transform and improve their processes and workflows.\\n\\nPython language is the second most popular used tool after R language for data science and analytics. Lots of many data processing workloads in the organization are powered by python language only. Most of the research and development takes place in python language due to its many applications, including ease of analyzing and organizing the usable data.\\n\\nNot only this, but hundreds of python libraries are being used in thousands of machine learning projects every day, such as TensorFlow for neural networks and OpenCV for computer vision, etc.\\n\\n7) First-choice Language\\nPython language is the first choice for many programmers and students due to the main reason for python being in high demand in the development market. Students and developers always look forward to learning a language that is in high demand. Python is undoubtedly the hottest cake in the market now.\\n\\nMany programmers and data science students are using python language for their development projects. Learning python is one of the important section in data science certification courses.  In this way, the python language can provide plenty of fantastic career opportunities for students. Due to the variety of applications of python, one can pursue different career options and will not remain stuck to one.\\n\\n8) The Flexibility of Python Language\\nThe python language is so flexible that it gives the developer the chance to try something new. The person who is an expert in python language is not just limited to build similar kinds of things but can also go on to try to make something different than before.\\n\\nPython doesn’t restrict developers from developing any sort of application. This kind of freedom and flexibility by just learning one language is not available in other programming languages.\\n\\n9) Use of python in academics\\nNow python language is being treated as the core programming language in schools and colleges due to its countless uses in Artificial Intelligence, Deep Learning, Data Science, etc. It has now become a fundamental part of the development world that schools and colleges cannot afford not to teach python language.\\n\\nIn this way, it is increasing more python Developers and Programmers and thus further expanding its growth and popularity. \\n\\n10) Automation \\nPython language can help a lot in automation of tasks as there are lots of tools and modules available, which makes things much more comfortable. It is incredible to know that one can reach an advanced level of automation easily by just using necessary python codes.\\n\\nPython is the best performance booster in the automation of software testing also. One will be amazed at how much less time and few numbers of lines are required to write codes for automation tools.\\n\\nConclusion\\nSome of the reasons why Python is growing at a supersonic speed. We hope this article has shed some good light on python language and its importance. So if anyone asks you “why python programming?” you have an essay answer ready.'"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now read again\n",
    "my_file.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read a file line by line using the readlines method. Use caution with large files, since everything will be held in memory. We will learn how to iterate over large files later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Why is Python so Popular?\\n',\n",
       " '1) Easy to Learn and Use\\n',\n",
       " 'Python language is incredibly easy to use and learn for new beginners and newcomers. The python language is one of the most accessible programming languages available because it has simplified syntax and not complicated, which gives more emphasis on natural language. Due to its ease of learning and usage, python codes can be easily written and executed much faster than other programming languages.\\n',\n",
       " '\\n',\n",
       " 'When Guido van Rossum was creating python in the 1980s, he made sure to design it to be a general-purpose language. One of the main reasons for the popularity of python would be its simplicity in syntax so that it could be easily read and understood even by amateur developers also.\\n',\n",
       " '\\n',\n",
       " 'One can also quickly experiment by changing the code base of python because it is an interpreted language which makes it even more popular among all kinds of developers.\\n',\n",
       " '\\n',\n",
       " '2) Mature and Supportive Python Community\\n',\n",
       " 'Python was created more than 30 years ago, which is a lot of time for any community of programming language to grow and mature adequately to support developers ranging from beginner to expert levels. There are plenty of documentation, guides and Video Tutorials for Python language are available that learner and developer of any skill level or ages can use and receive the support required to enhance their knowledge in python programming language.\\n',\n",
       " '\\n',\n",
       " 'Many students get introduced to computer science only through Python language, which is the same language used for in-depth research projects.\\n',\n",
       " '\\n',\n",
       " 'If any programming language lacks developer support or documentation, then they don’t grow much. But python has no such kind of problems because it has been here for a very long time. The python developer community is one of the most incredibly active programming language communities.\\n',\n",
       " '\\n',\n",
       " 'This means that if somebody has an issue with python language, they can get instant support from developers of all levels ranging from beginner to expert in the community. Getting help on time plays a vital role in the development of the project, which otherwise might cause delays.\\n',\n",
       " '\\n',\n",
       " '3) Support from Renowned Corporate Sponsors\\n',\n",
       " 'Programming languages grows faster when a corporate sponsor backs it. For example, PHP is backed by Facebook, Java by Oracle and Sun, Visual Basic & C# by Microsoft. Python Programming language is heavily backed by Facebook, Amazon Web Services, and especially Google.\\n',\n",
       " '\\n',\n",
       " 'Google adopted python language way back in 2006 and have used it for many applications and platforms since then. Lots of Institutional effort and money have been devoted to the training and success of the python language by Google. They have even created a dedicated portal only for python. The list of support tools and documentation keeps on growing for python language in the developers’ world.\\n',\n",
       " '\\n',\n",
       " '4) Hundreds of Python Libraries and Frameworks\\n',\n",
       " 'Due to its corporate sponsorship and big supportive community of python, python has excellent libraries that you can use to select and save your time and effort on the initial cycle of development. There are also lots of cloud media services that offer cross-platform support through library-like tools, which can be extremely beneficial.\\n',\n",
       " '\\n',\n",
       " 'Libraries with specific focus are also available like nltk for natural language processing or scikit-learn for machine learning applications.\\n',\n",
       " '\\n',\n",
       " 'There are many frameworks and libraries are available for python language, such as:\\n',\n",
       " '\\n',\n",
       " 'matplotib for plotting charts and graphs\\n',\n",
       " 'SciPy for engineering applications, science, and mathematics\\n',\n",
       " 'BeautifulSoup for HTML parsing and XML\\n',\n",
       " 'NumPy for scientific computing\\n',\n",
       " 'Django for server-side web development\\n',\n",
       " '5) Versatility, Efficiency, Reliability, and Speed\\n',\n",
       " 'Ask any python developer, and they will wholeheartedly agree that the python language is efficient, reliable, and much faster than most modern languages. Python can be used in nearly any kind of environment, and one will not face any kind of performance loss issue irrespective of the platform one is working.\\n',\n",
       " '\\n',\n",
       " 'One more best thing about versatility of python language is that it can be used in many varieties of environments such as mobile applications, desktop applications, web development, hardware programming, and many more. The versatility of python makes it more attractive to use due to its high number of applications.\\n',\n",
       " '\\n',\n",
       " '6) Big data, Machine Learning and Cloud Computing\\n',\n",
       " 'Cloud Computing, Machine Learning, and Big Data are some of the hottest trends in the computer science world right now, which helps lots of organizations to transform and improve their processes and workflows.\\n',\n",
       " '\\n',\n",
       " 'Python language is the second most popular used tool after R language for data science and analytics. Lots of many data processing workloads in the organization are powered by python language only. Most of the research and development takes place in python language due to its many applications, including ease of analyzing and organizing the usable data.\\n',\n",
       " '\\n',\n",
       " 'Not only this, but hundreds of python libraries are being used in thousands of machine learning projects every day, such as TensorFlow for neural networks and OpenCV for computer vision, etc.\\n',\n",
       " '\\n',\n",
       " '7) First-choice Language\\n',\n",
       " 'Python language is the first choice for many programmers and students due to the main reason for python being in high demand in the development market. Students and developers always look forward to learning a language that is in high demand. Python is undoubtedly the hottest cake in the market now.\\n',\n",
       " '\\n',\n",
       " 'Many programmers and data science students are using python language for their development projects. Learning python is one of the important section in data science certification courses.  In this way, the python language can provide plenty of fantastic career opportunities for students. Due to the variety of applications of python, one can pursue different career options and will not remain stuck to one.\\n',\n",
       " '\\n',\n",
       " '8) The Flexibility of Python Language\\n',\n",
       " 'The python language is so flexible that it gives the developer the chance to try something new. The person who is an expert in python language is not just limited to build similar kinds of things but can also go on to try to make something different than before.\\n',\n",
       " '\\n',\n",
       " 'Python doesn’t restrict developers from developing any sort of application. This kind of freedom and flexibility by just learning one language is not available in other programming languages.\\n',\n",
       " '\\n',\n",
       " '9) Use of python in academics\\n',\n",
       " 'Now python language is being treated as the core programming language in schools and colleges due to its countless uses in Artificial Intelligence, Deep Learning, Data Science, etc. It has now become a fundamental part of the development world that schools and colleges cannot afford not to teach python language.\\n',\n",
       " '\\n',\n",
       " 'In this way, it is increasing more python Developers and Programmers and thus further expanding its growth and popularity. \\n',\n",
       " '\\n',\n",
       " '10) Automation \\n',\n",
       " 'Python language can help a lot in automation of tasks as there are lots of tools and modules available, which makes things much more comfortable. It is incredible to know that one can reach an advanced level of automation easily by just using necessary python codes.\\n',\n",
       " '\\n',\n",
       " 'Python is the best performance booster in the automation of software testing also. One will be amazed at how much less time and few numbers of lines are required to write codes for automation tools.\\n',\n",
       " '\\n',\n",
       " 'Conclusion\\n',\n",
       " 'Some of the reasons why Python is growing at a supersonic speed. We hope this article has shed some good light on python language and its importance. So if anyone asks you “why python programming?” you have an essay answer ready.']"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Readlines returns a list of the lines in the file\n",
    "my_file.seek(0)\n",
    "my_file.readlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you have finished using a file, it is always good practice to close it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing to a File\n",
    "\n",
    "By default, the `open()` function will only allow us to read the file. We need to pass the argument `'w'` to write over the file. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Users\\\\abhranjan\\\\Desktop\\\\Python_Training'"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a second argument to the function, 'w' which stands for write.\n",
    "# Passing 'w+' lets us read and write to the file\n",
    "\n",
    "my_file = open('test.txt','w+')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <strong><font color='red'>Use caution!</font></strong> \n",
    "Opening a file with `'w'` or `'w+'` truncates the original, meaning that anything that was in the original file **is deleted**!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write to the file\n",
    "my_file.write('This is a new line 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is a new line 2'"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the file\n",
    "my_file.seek(0)\n",
    "my_file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_file.close()  # always do this when you're done with a file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appending to a File\n",
    "Passing the argument `'a'` opens the file and puts the pointer at the end, so anything written is appended. Like `'w+'`, `'a+'` lets us read and write to a file. If the file does not exist, one will be created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_file = open('test.txt','a')\n",
    "my_file.write('\\nThis is new text 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnsupportedOperation",
     "evalue": "not readable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnsupportedOperation\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-161-e7aee0e14615>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmy_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mUnsupportedOperation\u001b[0m: not readable"
     ]
    }
   ],
   "source": [
    "my_file.seek(0)\n",
    "print(my_file.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterating through a File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a new line 2\n",
      "\n",
      "More\n",
      "This is text being appended to test.txt\n",
      "\n",
      "More\n",
      "And another line here.\n",
      "\n",
      "More\n",
      "This is new text 3\n",
      "More\n"
     ]
    }
   ],
   "source": [
    "for line in open('test.txt'):\n",
    "    print(line)\n",
    "    print(\"More\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment\n",
    "Given an apache log file, find out all the unique IPs tht have tried to access the machine."
   ]
  }
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