Intro to Julia.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Julia_prog_language.svg.png](https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Julia_prog_language.svg/320px-Julia_prog_language.svg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Author: [Mohammed Yusuf Khan](https://www.linkedin.com/in/getmykhan)\n",
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "## Topics Covered in this Notebook:\n",
    "---\n",
    "\n",
    "\n",
    "1. Why Julia?\n",
    "2. How to Print?\n",
    "3. Assigning Variables\n",
    "4. Simple Operations\n",
    "5. Fun with Strings\n",
    "6. IF...ELSE...WHATELSE?\n",
    "7. Arrays\n",
    "8. Tuples\n",
    "9. Dictionaries\n",
    "10. Loops\n",
    "11. Functions\n",
    "12. The Guess Game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why Julia?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. __Super Fast__ - Can be compared to the performance of C and C++\n",
    "2. __Super Easy__ - Can be compared to the readibility of Python"
   ]
  },
  {
   "attachments": {
    "performance.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![performance.png](http://randomfantasies.com/wp-content/uploads/2015/05/juliabench-1024x633.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Bottom Line__:\n",
    "\n",
    "* Performance matters a lot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Print?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In Julia You would generally use `println()` to print a line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, World\n"
     ]
    }
   ],
   "source": [
    "println(\"Hello, World\")\n",
    "\n",
    "## Thats it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## - Commenting a line (Useful when writing code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=\n",
    "Multiline Commenting is Cool!\n",
    "=#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets pick up some pace now!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigning Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myvar1 = 5 # Assigning 5 to myvar1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I love Data Science\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myvar2 = \"I love Data Science\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myvar3 = 4.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(myvar1) #System Architecture based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "String"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(myvar2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(myvar3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Special built-in variables in Julia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "π = 3.1415926535897..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try pressing the TAB button here\n",
    "δ = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "α = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 1 #sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ^ 2 #power Note: Some other languages some times use **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 / 3 #div"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2//3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2//3 #fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.283185307179586"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: imported binding for pi overwritten in module Main\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warning when you change the value of pi\n",
    "# Stick to conventions\n",
    "pi = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2 + 1im"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 1im # Complex numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "div(6,2) # gives a truncated result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "div(5,2) # gives a truncated result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1622776601683795"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use `round()` to round the value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.16"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Rounding the number\n",
    "round(sqrt(10), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.17"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceil(sqrt(10),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.16"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floor(sqrt(10),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1623"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(sqrt(10), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fun with Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is a string.\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"This is a string.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" This is also a string \""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" This is also a string \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C': ASCII/Unicode U+0043 (category Lu: Letter, uppercase)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'C' # ' - denotes single character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Machine Learning\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mystr = \"Machine Learning\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use `length()` to find the length of an object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length(mystr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Remeber Index starts at 1 like R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Machine\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mystr[1:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Machine\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substr1 = mystr[1:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Learning\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substr2 = mystr[9:end]\n",
    "\n",
    "#or\n",
    "\n",
    "substr2 = mystr[9:length(mystr)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine\n",
      "Learning\n",
      "MachineLearning"
     ]
    }
   ],
   "source": [
    "println(substr1)\n",
    "println(substr2)\n",
    "\n",
    "print(substr1 , substr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String Conversion\n",
    "Use `parse()` to change the datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse(Float64, \"10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse(Int64, \"10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"100\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String Concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"MachineLearning\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string(substr1,substr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Machine Learning\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "substr1 * ' ' * substr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String Interpolation\n",
    "Use `$` followed by the variable name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine Learning\n"
     ]
    }
   ],
   "source": [
    "println(\"$substr1 $substr2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IF...ELSE...WHATELSE?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The answer is 2\n"
     ]
    }
   ],
   "source": [
    "var1 = 10\n",
    "var2 = 5\n",
    "\n",
    "if div(var1, var2) == 2\n",
    "    println(\"The answer is 2\")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `readline()` to prompt user for an input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is 1 + 1?\n",
      "STDIN> 2\n",
      "Correct Answer\n"
     ]
    }
   ],
   "source": [
    "println(\"What is 1 + 1?\")\n",
    "\n",
    "answer = readline() # Readline reads a string\n",
    "\n",
    "if parse(Int64 , answer) == 2\n",
    "    println(\"Correct Answer\")\n",
    "else\n",
    "    println(\"Wrong Answer\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a Number:\n",
      "STDIN> 2\n",
      "The Number is less than 10\n"
     ]
    }
   ],
   "source": [
    "println(\"Enter a Number:\")\n",
    "\n",
    "answer = readline() # Readline reads a string\n",
    "\n",
    "if parse(Int64 , answer) > 10\n",
    "    println(\"The Number is greater than 10\")\n",
    "elseif parse(Int64, answer) < 10\n",
    "    println(\"The Number is less than 10\")\n",
    "else\n",
    "    println(\"The Number entered is 10\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-element Array{Any,1}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Empty Array\n",
    "arr = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = [1,2,3,4,5,6,7,8,9]; # Semi-colon at the end wont print anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5\n",
       " 6\n",
       " 7\n",
       " 8\n",
       " 9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "\u001b[91mBoundsError: attempt to access 9-element Array{Int64,1} at index [0]\u001b[39m",
     "output_type": "error",
     "traceback": [
      "\u001b[91mBoundsError: attempt to access 9-element Array{Int64,1} at index [0]\u001b[39m",
      "",
      "Stacktrace:",
      " [1] \u001b[1mgetindex\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Int64,1}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./array.jl:554\u001b[22m\u001b[22m",
      " [2] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:522\u001b[22m\u001b[22m"
     ]
    }
   ],
   "source": [
    "arr[0] #index start at 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1:4] # slicing just like strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "\u001b[91msyntax: missing last argument in \"1:\" range expression \u001b[39m",
     "output_type": "error",
     "traceback": [
      "\u001b[91msyntax: missing last argument in \"1:\" range expression \u001b[39m",
      "",
      "Stacktrace:",
      " [1] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:522\u001b[22m\u001b[22m"
     ]
    }
   ],
   "source": [
    "arr[1:] # throws error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5\n",
       " 6\n",
       " 7\n",
       " 8\n",
       " 9"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "push!(arr,1)\n",
    "push!(arr,2)\n",
    "push!(arr,4)\n",
    "push!(arr,3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 4\n",
       " 5\n",
       " 6\n",
       " 7\n",
       " 8\n",
       " 9\n",
       " 1\n",
       " 2\n",
       " 4\n",
       " 3"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deleteat!(arr, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop!(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 5\n",
       " 4\n",
       " 2"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [5,4,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 2\n",
       " 4\n",
       " 5"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort!(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 2\n",
       " 4\n",
       " 5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 5\n",
       " 4\n",
       " 2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort!(arr, rev= true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Int64,1}:\n",
       " 5\n",
       " 4\n",
       " 2"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Any,1}:\n",
       "  'A'\n",
       "  'C'\n",
       " 1   \n",
       "  'D'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = ['A', 'C', 1 , 'D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{String,1}:\n",
       " \"A\"\n",
       " \"C\"\n",
       " \"D\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [\"A\", \"C\", \"D\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{String,1}:\n",
       " \"D\"\n",
       " \"C\"\n",
       " \"A\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort(arr, rev = true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Int64,1}:\n",
       " 4\n",
       " 5\n",
       " 6\n",
       " 7"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = [4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5\n",
       " 6\n",
       " 7"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = append!(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[1] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Array{Int64,1}:\n",
       " 2\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5\n",
       " 6\n",
       " 7"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10-element Array{Int64,1}:\n",
       "  1\n",
       "  2\n",
       "  3\n",
       "  4\n",
       "  5\n",
       "  6\n",
       "  7\n",
       "  8\n",
       "  9\n",
       " 10"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#range broadcasting\n",
    "ran_ = [1:10;]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{Int64,1}:\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran_[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Int64,1}:\n",
       "  7\n",
       "  8\n",
       "  9\n",
       " 10"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran_[7:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in ran_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tuples are immutable. Faster than lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tups = ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tups = tuple()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tuple{}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(tups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, 5, 6, 7, 8)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tups = (1,2,3,4,5,6,7,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tups[2:3] # Similar to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "\u001b[91mMethodError: no method matching setindex!(::NTuple{8,Int64}, ::Int64, ::Int64)\u001b[39m",
     "output_type": "error",
     "traceback": [
      "\u001b[91mMethodError: no method matching setindex!(::NTuple{8,Int64}, ::Int64, ::Int64)\u001b[39m",
      "",
      "Stacktrace:",
      " [1] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:522\u001b[22m\u001b[22m"
     ]
    }
   ],
   "source": [
    "tups[1] = 10 #tuples are immutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "\u001b[91mMethodError: no method matching push!(::NTuple{8,Int64}, ::Int64)\u001b[0m\nClosest candidates are:\n  push!(::Any, ::Any, \u001b[91m::Any\u001b[39m) at abstractarray.jl:1940\n  push!(::Any, ::Any, \u001b[91m::Any\u001b[39m, \u001b[91m::Any...\u001b[39m) at abstractarray.jl:1941\n  push!(\u001b[91m::Array{Any,1}\u001b[39m, ::ANY) at array.jl:658\n  ...\u001b[39m",
     "output_type": "error",
     "traceback": [
      "\u001b[91mMethodError: no method matching push!(::NTuple{8,Int64}, ::Int64)\u001b[0m\nClosest candidates are:\n  push!(::Any, ::Any, \u001b[91m::Any\u001b[39m) at abstractarray.jl:1940\n  push!(::Any, ::Any, \u001b[91m::Any\u001b[39m, \u001b[91m::Any...\u001b[39m) at abstractarray.jl:1941\n  push!(\u001b[91m::Array{Any,1}\u001b[39m, ::ANY) at array.jl:658\n  ...\u001b[39m",
      "",
      "Stacktrace:",
      " [1] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:522\u001b[22m\u001b[22m"
     ]
    }
   ],
   "source": [
    "push!(tups, 20) ## immutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Unpacking\n",
    "tup_up = (1,2,3)\n",
    "a , b, c = tup_up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Any,Any} with 0 entries"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Key , Value pair\n",
    "mydictionary = Dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{Any,Any}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(mydictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String,String} with 2 entries:\n",
       "  \"EmailID\" => \"mohammedykhan7@gmail.com\"\n",
       "  \"Name\"    => \"Mohammed Yusuf\""
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydictionary = Dict(\"Name\" => \"Mohammed Yusuf\", \"EmailID\" => \"mohammedykhan7@gmail.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Mohammed Yusuf\""
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydictionary[\"Name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"mohammedykhan7@gmail.com\""
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydictionary[\"EmailID\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Base.KeyIterator for a Dict{String,String} with 2 entries. Keys:\n",
       "  \"EmailID\"\n",
       "  \"Name\""
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys(mydictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Base.ValueIterator for a Dict{String,String} with 2 entries. Values:\n",
       "  \"mohammedykhan7@gmail.com\"\n",
       "  \"Mohammed Yusuf\""
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values(mydictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Python\""
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydictionary[\"Language\"] = \"Python\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String,String} with 3 entries:\n",
       "  \"EmailID\"  => \"mohammedykhan7@gmail.com\"\n",
       "  \"Name\"     => \"Mohammed Yusuf\"\n",
       "  \"Language\" => \"Python\""
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Use sets to find unique pair and faster computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Set([4, 2, 3, 5, 1])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myset = Set([1,1,1,1,2,2,3,1,5,4,3,2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{Int64,1}:\n",
       " 4\n",
       " 2\n",
       " 3\n",
       " 5\n",
       " 1"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to list\n",
    "mysetlist = collect(myset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{Int64,1}:\n",
       " 4\n",
       " 2\n",
       " 3\n",
       " 5\n",
       " 1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysetlist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "for i in 1:10\n",
    "    println(i)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A\n",
      "C\n",
      "D\n"
     ]
    }
   ],
   "source": [
    "for i in arr\n",
    "    println(i)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EmailID: mohammedykhan7@gmail.com\n",
      "Name: Mohammed Yusuf\n",
      "Language: Python\n"
     ]
    }
   ],
   "source": [
    "for (k,v) in mydictionary\n",
    "    println(\"$k: $v\")\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## While Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 "
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "\n",
    "while count < 10\n",
    "    print(count, \" \")\n",
    "    count += 1\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name (generic function with 1 method)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function name(Lname)\n",
    "    return Lname\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Khan\""
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name(\"Khan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Khan\""
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LNAME = name(\"Khan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Khan\""
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LNAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factorial of a number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fact (generic function with 1 method)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function fact(number)\n",
    "    if number == 1\n",
    "        return 1\n",
    "    else\n",
    "        return (number * fact(number - 1))\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "6\n",
      "24\n",
      "120\n",
      "720\n",
      "5040\n",
      "40320\n",
      "362880\n",
      "3628800\n"
     ]
    }
   ],
   "source": [
    "for i in 1:10\n",
    "    println(fact(i))\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guess Game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MYK\n",
      "Enter a number between 1 and 10\n",
      "STDIN> 3\n",
      "Try a smaller number\n",
      "Enter a number between 1 and 10\n",
      "STDIN> 8\n",
      "Try a smaller number\n",
      "Enter a number between 1 and 10\n",
      "STDIN> 1\n",
      "You have made the right guess\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"The Number selected was: 1\""
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function game(name)\n",
    "    println(name)\n",
    "    rand_number = rand(1:10)\n",
    "    \n",
    "    while true\n",
    "        println(\"Enter a number between 1 and 10\")\n",
    "        player_answer = readline()\n",
    "    if parse(Int64, player_answer) == rand_number\n",
    "            println(\"You have made the right guess\")\n",
    "            break\n",
    "    \n",
    "    elseif parse(Int64, player_answer) < rand_number\n",
    "            println(\"Try a greater number\")\n",
    "    \n",
    "    elseif parse(Int64, player_answer) > rand_number\n",
    "            println(\"Try a smaller number\")\n",
    "        end\n",
    "        end\n",
    "    \n",
    "    return (\"The Number selected was: $rand_number\")\n",
    "\n",
    "end\n",
    "\n",
    "\n",
    "game(\"MYK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questions?\n",
    "#### Ends here!"
   ]
  }
 ],
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