MY_PYTHON_BASIC_TUTORIAL_PART_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = green> Python Basics: Part 1. </font>\n",
    "\n",
    "### Prior Knowledge that are required to understand this??\n",
    "#### <font color = purple> * Basic idea about Jupyter Notebook is required,</font>\n",
    "\n",
    "<font color = purple> * Basic knowladge about Anaconda and package installation, </font>\n",
    "\n",
    ">Jpyter notebook tutorial (you can follow any other source such as YouTube)\n",
    "link: https://www.dataquest.io/blog/jupyter-notebook-tutorial/ <br>\n",
    ">Installation of Anaconda package manager for Windows:\n",
    "link: https://mas-dse.github.io/startup/anaconda-windows-install/ <br>\n",
    "link: https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp <br>\n",
    "\n",
    "\n",
    "##### <font color = red> Note: all the figues and data are taken from different sources and i dont hold any of the copy rights. all the copy rights goes to the respective Authors. This is an attempt to look at python language differently in a much easier and interactive fun way of learing it. </font>  \n",
    "\n"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables and datatype :\n",
    "### Variables: Containers that store value \n",
    "\n",
    " ![variable.jpg](attachment:variable.jpg)\n",
    "\n",
    "   ### Vectors holds some value assign to it\n",
    "   ### Vectors with numeric value is ambiguous ????\n",
    "   ### numers can be \"integers\" or \"decimals\" also knows as \"floats\" in python\n",
    "   ### integers are +ve or -ve, but a whole number without a decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets create a variable x1 containing the number 5 in it\n",
    "x1 = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check what the variable holds type the variable name :\n",
    "x1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here, x1 is a variable and 5 is the element inside the variable x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## lets create another variable x2 containing a floating value of 4.56 in it :\n",
    "x2 = 4.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.56"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type () , type function tells us about the variable type :\n",
    "type(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to change any decimal /float into integer we can use int() (int-function)\n",
    "int(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to convert any integer into decimal we can use float() (float-function)\n",
    "float(x1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### except the integer and float a variable can contain a  boolean value <br> denoted by \"bool\" or strings denoted by <br> str (basically words) <br> both are same thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppose you want to write two different types of elements together, <br> For example you want to write 100 rupees, where 100 is an integer and rupees is a string "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-8b98c82ecf56>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m100\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[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"rupees\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#it will give error as we have not mentioned the integer part i.e 100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "y = 100\n",
    "print(y + \"rupees\") #it will give error as we have not mentioned the integer part i.e 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100rupees\n"
     ]
    }
   ],
   "source": [
    "#To correct the error : we need to convert the integer value to stings\n",
    "y = 100\n",
    "print(str(y) + \"rupees\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \\  is often used as an escape funtion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"we'll catchup later\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of an escape function: suppose you want to print \"we'll catchup later\"\n",
    "\"we\\'ll catchup later\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple arithmatics in python:\n",
    "### Additions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 + 200 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Substractions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "250-240\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Divisions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "24/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.75"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#different one\n",
    "27/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to get the remainder\n",
    "27 % 4 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiplications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "624"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "24*26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4096"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how to calculate 2 to the power 12\n",
    "2 ** 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use of double equals \"==\" always states that something is exactly equals to something or not, <br> if it is the it gives \"True\" <br> if not then it gives \"False\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67108864"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 2 ** 26\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y == 67108864"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y == 67"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### line continuation using \" \\ \" , for larger codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y= 245+71+77+89+66+66+77+88+777777777777+6666+55555555555555+444444444444+3333333333333333+22222222222222+777 \\\n",
    "+123+456+78+66 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3412333333342276"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing elements: \n",
    "\n",
    "![index.jpg](attachment:index.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python always couts from 0 from right to left and from -1 from left to rigt/from the end\n",
    "#General way to index is :\n",
    "                            # \"name_of_the_Variable\"[index_element]\n",
    "#example:  \n",
    "\n",
    "\"GITHUB\"[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'U'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If we want to take out \"U\" :\n",
    "\"GITHUB\"[-2]"
   ]
  },
  {
   "attachments": {
    "indents_.jpg": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indentation:\n",
    "#### * Also knon as Spacing is very important while writing a code\n",
    "\n",
    "![indents_.jpg](attachment:indents_.jpg)\n",
    "\n",
    "#### * To get valid results we need to follow the indents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "def five(x):\n",
    "    x = 5\n",
    "    return x\n",
    "print(five(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison Operators :\n",
    " > == staes is left hand side is exactly equals to right hand side <br>\n",
    " > != means if left hand side is not equals to right hand side "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examples:\n",
    "80 == 20/2*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "80 == 20/2*8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# >= means greater than equals to:\n",
    "100 >= 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# <= means less than equals to:\n",
    "100 <= 50 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logical Operators in Python:\n",
    " > Not <br>\n",
    " > And <br>\n",
    " > Or <br>\n",
    "* gives result in \"True\" or \"False\" , Also known as Boolean Operators\n",
    "\n",
    "#### And : Checks whether the two statements around it are tue!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examples:\n",
    "True and True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False and False "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not : Leads to the opposite of a given statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examples :\n",
    "not True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for numeric values:\n",
    "\n",
    "10 > 3 and 20 > 10"
   ]
  },
  {
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3qqgUAeX896K7bWPBKFTJaHBA5WuLmhe1m2SAg5wQaAG0UUUAFFFFACZO5f8Aerzbxt/yGm+lek/xL/vV5t42/wCQ0fpQBg0UUUAX9E/4/lrrvwrkdE/4/lrrvwoAOfSjn0o59KOfSgA59KOfSjn0o59KADn0o59KOfSjn0oAOfSjn0o59KOfSgA59KOfSjn0o59KADn0o59KOfSjn0oAOfSjn0o59KOfSgA/Cj+JaOfSj+JaAO/+E3N5c16fXmHwl/4+7mvT6AMvxN/yAL7/AK5mvBR9K968Tf8AIAvv+uZrwUfSgA59KOfSjn0o59KAJrW7a1kBXp3Fb9vcLdKGVufSua59KmtL5rOQcfKaAOloplvMtxGGTmn5z2xQAUUUUAFFFFABRRRQAUUUUAFL6UlL/CfrQB6B4H/5Br/71dJXO+Cf+QY3+9XQUAPopmaM0APopmaM0APopmaM0APpCobqAabmjNADto9B1z0oChc4AGevFNzRmgBwAUAAYFLTM0ZoAfRTM0ZoAfRTM0ZoAfRTM0ZoAfRTM0ZoAfRTM0ZoAfRSDpS0AFFFFABVTU/+PGf/AHDVuqmp/wDHjP8A7hoA+fbn/j4l4/jb+dR8+lSXP/HxLx/G386j59KADn0o59KOfSjn0oAOfSjn0o59KOfSgA59KOfSjn0o59KAO4+G93p8Vy0dwqi5b7jtjFeh614gtNDtzJcSL0+VQeteB+YYXVxlGByGU1Zv9QudUaNrqYyKg45OKAOtvvixfXDkWkIRAf4quaF8UJZLpYtRQCNjjcvWvPRyvTI7ZoIByTwRzQB9F280dxCkiHKEZFcf440kR7bqNBnPzYFafgW4a68OwM5JPTk+9Hjx/J8PzODyDQB59SVBZ3qXUan+LHIqxndzQAlFFFACfxL/AL1ebeNv+Q0fpXpP8S/71ebeNv8AkNH6UAYNFFFAF/RP+P5a678K5HRP+P5a678KADn0o59KOfSjn0oAOfSjn0o59KOfSgA59KOfSjn0o59KADn0o59KOfSjn0oAOfSjn0o59KOfSgA59KOfSjn0o59KADn0o59KOfSjn0oAOfSj+JaOfSj+JaAO/wDhL/x93Nen15h8Jf8Aj7ua9PoAy/E3/IAvv+uZrwUfSvevE3/IAvv+uZrwUfSgA59KOfSjn0o59KADn0pjdafz6UfhQBY0++NoyjJ2mugjkWdQyHNcm33vSrthqDWrKMnafegDo/qMUlNhnSZdyndmnfXigAooooAKKKKACiiigAqe0jE06IelQVLby+RMr9aAOs0+9l0mLZF8y1bXxDc46LXneq+OH0+58rYvPvVT/hZB/wCeUdAHqH/CRXPotH/CRXPoteXf8LGP/PNPzoX4iOW4iRvYGgD1H/hIrn0Wj/hIrn0WvMv+FgS97bH4Uf8ACwJf+ff9KAPTf+EiufRaP+EiufRa8y/4WBL/AM+/6Uf8LAl/59/0oA9N/wCEiufRaP8AhIrn0WvMv+FgS/8APv8ApR/wsCX/AJ9/0oA9N/4SK59Fo/4SK59FrzL/AIWBL/z7/pR/wsCX/n3/AEoA9Th8QTtgELmp/wC15vRfyryUfECYciAD8K09N+ID3HySR7SKAPR/7Xm9B+VH9rzeg/KuL/4S6T/nkD+FH/CWyH/ln+lAHaf2vN6D8qP7Xm9B+VcX/wAJZJ/zzo/4SyT/AJ50Adp/a83oPyo/teb0H5Vxf/CWSf8APOj/AISyT/nnQB2n9rzeg/Kj+15vQflXF/8ACWSf886T/hLH/wCeZoA7X+15vQflU9jqD3FwFYVwY8WSZ4jyMY5ru9HtvMhjnJwzKDigDT/iNPplOHSgBaKKKACqmp/8eM/+4at1U1P/AI8Z/wDcNAHz7c/8fEvH8bfzqPn0qS5/4+JeP42/nUfPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lHPpQAmP9mj8KXn0o59KADn0ohhe4uBEi5dsYFN3YZs/d7/AP1q7r4d+FzdXH9oTxERx/cDjOaAPQfDNj/Zuj20BHOzP9a534n6okGli2B+eU8iuymkS1j8xiFjRSee1eIeLtcbXNWds/u0bC88UAZFvcNayBl5B610FndJcqCDz3Fc2M+lSW9y9rMCvQ9aAOpbrSVDa3SXKAg/N3qagBP4l/3q828bf8ho/SvSf4l/3q828bf8ho/SgDBooooAv6J/x/LXXfhXI6J/x/LXXfhQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6UfxLRz6UbhuXigDv8A4S/8fdzXp9eYfCb/AI+7mvT6AMvxN/yAL7/rma8FH0r3rxN/yAL7/rma8FH0oAOfSjn0o59KOfSgA59KOfSjn0o59KAD8KTHtS8+lHPpQBcsL77O4U8Ct5XW4UFWrlfwq5Y6g1rIFbJRvU0Ab/60UisjoCh3A0oz360AFFFFABRRRQAUv8QpKXsaAPMPiCzLrSYJHy/0rmRI2PvH866b4hf8hpP93+lcuvSgB/mP/eb86vaEzNqkYJJB6gms+r+g/wDIWioA7raP7tGPal/Cjn0oATHtRj2pefSjn0oATHtRj2pefSkZsEE9uwoAaynPGBSbG9RWxovhPUNdbdBCUTPLOOK6yL4RyNH+8utjegoA862469acpKMrKOfauv1j4b3+m25mgIuAvbHJrlGUxyFSpRx1BoA2tNvkuECScMKv7R6CuU3PE4YHkelbum6itwoWQ4YUAXto9BRtHoKWigBNo9BRtHoKWigBNo9BS4HpRRQAAfvBxXq+j/8AIPt/9wV5QP8AWD6f1r1fR/8AkHW/+6KAL1LRRQAUUUUAFVNT/wCPGf8A3DVuqmp/8eM/+4aAPn25/wCPiXj+Nv51Hz6VJc/8fEvH8bfzqPn0oAOfSjn0o59KOfSgA59KOfSjn0o59KADn0o59KOfSjn0oAOfSkH3sk4x2oYkRkgjPoa3vCvhSbxHdqCjR2ykFnbvQBL4R8Hya9cB5gUtlbLE9/pXstrbx2NukMYCRKvHGBUen6bb6barBAqqqjriuV8deM00u3+y2zq87jB29qAM74geMgFOnWjht3Ejg15sPp+dJJI8shdySxOc03cfU0ASc+lMYnce1OXO2l/CgB1rdtayAjJB610dtdR3UYIPzVzX4U+3u2tZgV6dxQB0w/h/3q838bf8ho/SvRrW6juI0YdSelec+N/+Q030oAwaKKKAL+if8fy1134VyOif8fy1134UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lbfhHR4tc1L7PMSFPpQBgsTuo/iWvW2+Fulk43yUn/AAqrTP8AnpJ+dAGN8Jf+Pq5r1CsLw94RtPDrs0DMS3XNbpoAy/E3/IAvv+uZrwIk5PNfRGoWYvraSB/9W4wcVyP/AAqrTecySZ69aAPJNx9aeudtesj4VaaAT5j4964fxr4fh8P3ixW7Ehl70Ac/z6Uc+lIudozyaXn0oAOfSjn0o59KOfSgA59KOvUZo59KOfSgC5Y6gbVlU8qe3pW8sgkUFTnPeuSb71aGl6k1u4R/mU+vagDe6UUb1YAxncD3oxt4zmgAooooAKX+E0lL/CaAPMPiF/yGk/3f6Vy69K6j4hf8hpP93+lcuvSgBav6D/yFoqoVf0H/AJC0VAHd/hRz6UfhRz6UAHPpRz6Uc+lHPpQAc+lIPlkDdwc4pefSj3xzQB6VpPxDs7HQgDEEnXgIowD9axJvipqTXBPlqI8HAFcdwWGQ2R0C96v2ug6leYEVm4Dn7zCgD1/wf4kHiTTzM67ZF4ZR0Nef/EjS49L1ZZIwAsoyVAruPBOgnwvpL/amAdznrXA/EPWItY1gJE2Y4xyQelAHMKNqjvSKzRyBl4+lNDEj0oyfWgDoNP1BbhQrnDCr9clHI0cgZetb2n6gLpQHO1qAL9FFFABRRRQAD/WD6f1r1fR/+Qdb/wC6K8oH+sH0/rXq+j/8g63/AN0UAX6KKKACiiigAqpqf/HjP/uGrdVNT/48Z/8AcNAHz7c/8fEvH8bfzqPn0qS5/wCPiXj+Nv51Hz6UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAb3hTwnJ4ivBu4gU5LV7NpumwabbrBAiqijHAAzXnvw51yz0yylS5lWMs3rXYf8ACb6OuR9qUUAaWpRTvautuR5rDAzXAzfC2a6uGnuLzdKxyTnNdZ/wmmkNz9rUUf8ACZaQOBeLigDlP+FSK3P2qj/hUa5/4+fbrXV/8Jto68falNKPGmjtg/al68UAeMaxY/2Xqk1pnPlnH6VT5rQ8TXKXniC6khbcjNkN68Cs5c7aAF59KPwo59KOfSgCW1umtpk/u7ulcv4uuUuNYJjZXO3kdxXRry65HevI/GWryaV4uYqTtZeRmgDfoqCxvUvLYTRuH45XvU9AF/RP+P5a678K5HRP+P5a678KADn0o59KOfSjn0oAOfSjn0o59KOfSgA59KOfSjn0o59KADn0o59KOfSjn0oAOfSjn0o59KOfSgA59KOfSjn0o59KADn0rq/huT/wkC1ynPpVrS9VuNJuTNbyBH7etAH0I3GPrSnrXiv/AAsbW148/wD8dzR/wsbW/wDnv/46KAPaaK8W/wCFja3/AM9//HRR/wALG1v/AJ7/APjooA9porxb/hY2t/8APf8A8dFH/Cxtb/57/wDjooA9q9K8m+KnGqQ44+Ws/wD4WPrf/Px/46KxtY1y71yTfduGYdG6GgCivQcUvPpSL90d6Xn0oAOfSjn0o59KOfSgA59KOfSjn0o59KAD8KPboaOfSj8KAL+m6gbdhG5O31rbVhtBU7ga5Jid1aWmakYWEchOxv4ielAG59OaKRGDKCORS0AFL/CaSl/hNAHmHxC/5DSf7v8ASuXXpXUfEL/kNJ/u/wBK5delAC1f0H/kLRVQq/oP/IWioA7v8KOfSj8KOfSgA59KOfSjn0o59KADn0o59KOfSkJwMn6YFAD4JNkysRgKwJr1jS/iFpAt44iDGwABJ6V5RcWk0EIaWN0V+VYioVG7k/MD60Ae66hDD4o0/Fpc43d1PIryDxB4dm0G8eOcHDNw3rXUfCuacalPDuYw7c88gVq/FaFDpcMxIMiNgHHWgDy4AY4HFGPahc45FLz6UAJj2p0chikDLwRSc+lH4UAb+n3y3QAc4arp4rlI5GhkDqTW/Y6gt2o3na1AFuiiigAH+sH0/rXq+j/8g63/AN0V5QP9YPp/WvV9H/5B1v8A7ooAv0UUUAFFFFABVTU/+PGf/cNW6qan/wAeM/8AuGgD59uf+PiXj+Nv51Hz6VJc/wDHxLx/G386gYndQA/n0o59Kj3H1o3H1oAk59KOfSo9x9aNx9aAJOfSjn0qPcfWjcfWgB/4UYx2pm4+tG4+tAD9v+zRtH90UzcfWjcfWgCT8KPwqPcfWjcfWgCTvnHPrR+FR7j60bj60ASc+lHPpUe4+tG4+tAEo+8n1rxP4l/8jM/0/oK9rTrH9a8U+JX/ACND/T+goAxtF1ufSZk2gNCThhXoNrfRXluJo23r3ry5eAcDr1rT0bWpdJnUnmBjyueKAPU9Dx/aEeOmO9db/C31rjPDd1HeXMU8Dhlx93uK7MdOlAC/hRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpR+FHPpRz6UAH4UmPal59KOfSgBMe1GPal59KOfSgBMe1GPal59KOfSgBMe1G0f3RS8+lHPpQAc+lHPpRz6Uc+lABz6Uc+lHPpRz6UAHPpRz6Uc+lHPpQAc+lHPpRz6Uc+lAB+FMbrjtT+fSj8KANDS9SMW2OQkr2JrbUhhlTkVynPHFaWm6kUIifp6mgDZpf4TSKQQCpyKX+E0AeYfEL/kNJ/u/0rl16V1HxC/5DSf7v9K5delAC1f0H/kLRVQq/of/ACFIaAO7/Cjn0oGaOfSgA59KOfSmMTupNx9aAHchie1T6fLBHdRPcAtHu5x/WoVYbeaaTyccD2oA9vgtNH8SaZFGojZAuFHGRWLN8LbGSYOsromfuhq8zsdUudNk328zRn2JFbUPjzWY1x9oB+pNAHq+kaRp/hm1Pl7UVvvSMeTXmvxA8UJrVwLe3+aGFvzrC1LxJqWp8TXZx3UE1nKwX69zQALnHPWl59KM57Uxid1AD+fSjn0qPcfWjcfWgCTn0ojmaKRSuRj0qPcfWjJ9aAOls75LlQrHBq1XKQyvHIGBOBXQ2V8l4oBO1hQBaH+sH0/rXq+j/wDIOt/90V5R3Ner6P8A8g63/wBwUAX6KKKACiiigAqrqRxY3Gemw1518WvjIPhe9qr2RuvP6EGvN7v9rJJreSL+zMblIOeaALFxIPtEuGGN7fzqI4PO4V5nL8VxNI7/AGTG5if1pv8AwtT/AKdTQB6dx/eFHH94V5j/AMLU/wCnU0f8LU/6dTQB6dx/eFHH94V5j/wtT/p1NH/C1P8Ap1NAHp3H94Ucf3hXmP8AwtT/AKdTR/wtT/p1NAHp3H94Ucf3hXmP/C1P+nU0f8LU/wCnU0Aencf3hRx/eFeY/wDC1P8Ap1NH/C1P+nU0Aencf3hRx/eFeY/8LU/6dTR/wtT/AKdTQB6dx/eFHH94V5j/AMLU/wCnU0f8LU/6dTQB6dx/eFHH94V5j/wtT/p1NH/C1P8Ap1NAHp6n94g3DrXi3xK/5Gh+/H9BWy3xWwo22p3bvyrjvEWsf29qjXQUpx9307UAZ4+lDZ2jPT0oGcDimHLP6YoA6/4d3ssWvQxKf3Tfw5r2c+3Irw74ftjxNC1e2GYbxQBLz6Uc+lV/tI5o+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc+lV/tQo+1CgCxz6Uc5yOMVX+1Cj7VQBuaXqWxgj8jtmthsblx0NcatwqgEda2NJ1hflSZu+BuNAHFfEL/kNJ/u/0rl16Ve+KXiEWXiFItm7K8H8K43/hMNvHkZ96AOlrQ0P/AJCcVcV/wmA/54frVmx8cfZJxKLfO33oA9hLc/exTg3H3q8xb4qc82pJo/4Wp/06mgD07j+8KOP7wrzH/han/TqaP+Fqf9OpoA9O4/vCjj+8K8x/4Wp/06mj/han/TqaAPTv+BCjj+8K8x/4Wp/06mj/AIWp/wBOpoA9O4/vCkwB3FeZf8LU/wCnU0f8LU/6dTQB6dn/AGqOP7wrzH/han/TqaP+Fqf9OpoA9O4/vCjj+8K8x/4Wp/06mj/han/TqaAPTuP7wo4/vCvMf+Fqf9Opo/4Wp/06mgD07P8AtU+Kc27hlfpXl3/C1P8Ap1NH/C1B/wA+hoA9vsdSW6Ucru9Pxr2TR/8AkGW3rsFfGFv8XDbyqyWhypyRmvRrP9rBLW1ihOl5KLjINAH0uOlLXknwr+OA+JGry2K2Rtyi792a9aoAWiiigD5g/a2A+2aUd20188seoD8V9zfEb4S6T8RpIH1ASB4vu7DXIf8ADKnhfvJN/wB9UAfI3/AhS5/2q+uP+GVPC/8Az0m/76o/4ZU8L/8APSb/AL6oA+R8/wC1Rn/ar64/4ZU8L/8APSb/AL6o/wCGVPC//PSb/vqgD5Hz/tUZ/wBqvrj/AIZU8L/89Jv++qP+GVPC/wDz0m/76oA+R8/7VGf9qvrj/hlTwv8A89Jv++qP+GVPC/8Az0m/76oA+R8/7VGf9qvrj/hlTwv/AM9Jv++qP+GVPC//AD0m/wC+qAPkfP8AtUZ/2q+uP+GVPC//AD0m/wC+qP8AhlTwv/z0m/76oA+R8/7VGf8Aar64/wCGVPC//PSb/vqj/hlTwv8A89Jv++qAPkfP+1Rn/ar64/4ZU8L/APPSb/vqj/hlTwv/AM9Jv++qAPkfP+1Rn/ar64/4ZU8L/wDPSb/vqj/hlTwv/wA9Jv8AvqgD5HH1H50YxkjAJ6819cf8Mp+Fv+ek3/fVH/DKfhb+/N/31QB8j/8AAsUBtrZ+U8V9cf8ADKfhf/npN/31UVx+yz4Yhhdg85IBYfNxwKAPkKx8R3Gj6gJ4OWWugX4vazj7i5rlfEVnHpmvanaR/chnZF+gNUdwoA7n/hbernkqmaT/AIW1q391K4bcPQUbh6CgDuf+Ftat/dSj/hbWrf3Urhtw9BRuHoKAO5/4W1q391KP+Ftat/dSuG3D0FG4egoA7n/hbWrf3Uo/4W1q391K4bcPQUbh6CgDuf8AhbWrf3Uo/wCFtat/dSuG3D0FG4egoA7n/hbWrf3Uo/4W1q391K4bcPQUbh6CgDuf+Ftat/dSj/hbWrf3Urhtw9BRuHoKAO5/4W1q391KP+Ftat/dSuG3D0FG4egoA7n/AIW1q391KP8AhbWrf3Urhtw9BRuHoKAO5/4W1q391KP+Ftat/dSuG3D0FG4egoA7n/hbWrf3Uo/4W1q391K4bcPQUbh6CgDuf+Ftat/dSj/hbWrf3Urhtw9BRuHoKAO5/wCFtat/dSj/AIW1q391K4bcPQUbh6CgDuf+Ftat/dSj/hbWrf3Urhtw9BRuHoKAO5/4W1q391KP+Ftat/dSuG3D0FG4egoA7n/hbWrf3Uo/4W1q391K4bcPQUbh6CgDuf8AhbWrf3Uo/wCFtat/dSuG3D0FG4egoA7dvi3rO7hExTl+L2sjHyrkdK4fcKNwoA3tc8Zah4jvFluSoZV+93p8eGRSepHrWHaxpcXUKMdoeREP4tX2TpH7Lvhm+0u1uJJJd8kascH1FAHykdvpn8aXtgHA9K+uP+GU/C3/AD0m/wC+qP8AhlTwv/z0m/76oA+R93+1Rn/ar64/4ZU8L/8APSb/AL6o/wCGVPC//PSb/vqgD5Hz/tUZ/wBqvrj/AIZU8L/89Jv++qP+GVPC/wDz0m/76oA+R8/7VGf9qvrj/hlTwv8A89Jv++qP+GVPC/8Az0m/76oA+R8/7VGf9qvrj/hlTwv/AM9Jv++qP+GVPC//AD0m/wC+qAPkfP8AtUZ/2q+uP+GVPC//AD0m/wC+qP8AhlTwv/z0m/76oA+R8/7VGf8Aar64/wCGVPC//PSb/vqj/hlTwv8A89Jv++qAPkfP+1Rn/ar64/4ZU8L/APPSb/vqj/hlTwv/AM9Jv++qAPkfP+1Rn/ar64/4ZU8L/wDPSb/vqj/hlTwv/wA9Jv8AvqgD5Hz/ALVH4g/jX1x/wyp4X/56Tf8AfVH/AAyn4W/56Tf99UAfJS8cjg+oNMb7x5Ar64/4ZT8Lf89Jv++qP+GVPC//AD0mx/vUAebfspsq+Mbwrhn8rmvravOvAHwW0b4d6lJeaeZWkkGDuavRaAFooooASjA9KWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKKWigBKhulLwyJjAYEfmKnph+9nvQB8x6v+yGNU1i8vf7T2C4laTaR0yar/8ADGX/AFFq+pOtFAHy3/wxmP8AoLUf8MZj/oLV9SUUAfLf/DGY/wCgtR/wxmP+gtX1JRQB8t/8MZj/AKC1H/DGY/6C1fUlFAHy3/wxmP8AoLUf8MZj/oLV9SUUAfLf/DGY/wCgtR/wxmP+gtX1JRQB8t/8MZj/AKC1H/DGY/6C1fU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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If a statement contain multiple opearators it will follow this sequence:\n",
    "\n",
    "![operator_.jpg](attachment:operator_.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examples:\n",
    "True and not True or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False or not False and True "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identity operators:\n",
    "> is <br>  is not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<>:3: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "<ipython-input-40-8451d94e5ac5>:3: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  'lion' is 'zebra'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of 'is':\n",
    "\n",
    "'lion' is 'zebra'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alternative way to write the same :\n",
    "'lion' == 'zebra'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
      "<>:3: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
      "<ipython-input-42-c682aac77932>:3: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
      "  8 is not 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of 'is not':\n",
    "\n",
    "8 is not 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Alternative way:\n",
    "\n",
    "8 != 10 #where, != means is not equals to "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditioanl Statements:\n",
    "#### If , Else, Elif\n",
    "#### * Remember, Indents are very important in this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If Statement: if statements print a particular result if the condition is true in it.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "well done!!!!!\n"
     ]
    }
   ],
   "source": [
    "#example of an 'if' statement:\n",
    "if 800 == 1600/2:\n",
    "    print(\"well done!!!!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Else statement: if the condition mention in the 'If' statement turn False then the Else function comes into picture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keep guessing!!!!!\n"
     ]
    }
   ],
   "source": [
    "if 800 == 1600/3:\n",
    "    print(\"well done!!!!!\")\n",
    "else:\n",
    "    print(\"keep guessing!!!!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elif Statement: Is a way to add two 'If' statements in any expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bank_balance(x): #def is used to define the name of a function you are about to create\n",
    "    if x > 1000:\n",
    "        return \"More than Thousand, Need Savings!!!\"\n",
    "    elif x < 1000:\n",
    "        return \"less than Thousand, Congrats!!!\"\n",
    "    else:\n",
    "        return \"Have exactly 1000, Work harder!!!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "less than Thousand, Congrats!!!\n"
     ]
    }
   ],
   "source": [
    "print(bank_balance(800))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "More than Thousand, Need Savings!!!\n"
     ]
    }
   ],
   "source": [
    "print(bank_balance(2000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Have exactly 1000, Work harder!!!\n"
     ]
    }
   ],
   "source": [
    "print(bank_balance(1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### where to use Print() and where to use return()\n",
    "#### Suppose for renting a car, You are paying rupees 100/hour and if its a holiday you pay rupee 50 extra on the basic rental scheme. Apply your computational thinking on the problem statement to write a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solution:\n",
    "def rental(rental_hours):\n",
    "    \n",
    "    return rental_hours * 100 \n",
    "\n",
    "def rental_on_weekends(rental_hours):\n",
    "    \n",
    "    return rental_hours * 100 + 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2400 2450\n"
     ]
    }
   ],
   "source": [
    "print(rental(24), rental_on_weekends(24))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### When to use conditional statements and functions hand in hand:\n",
    "#### As per imagine bank if you deposit more than equals to rupees 1000 a month then the bank will give a profit of rupees 10 added to your bank account, and if you cannot then you get no profit. \n",
    "#### For getting a text in the result we use print (), it can be used multiple times,\n",
    "#### For getting the result of certain calculation where the input of x and y suppose, fives the result z, we use return(). <br> Can be used only once in an expression.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit_ten(x): \n",
    "    if x>=1000:\n",
    "        x=x+10\n",
    "    else:\n",
    "        return \"Better luck next month!!!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_ten(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit_of_ten(p):\n",
    "    if p > 1000:\n",
    "        p = p + 10\n",
    "        print(\"Rupees:\")\n",
    "        return p\n",
    "    else:\n",
    "        print(\"Save at least 1000 to get profit of Rupees 10!!!\")\n",
    "        print(\"Your Current Balance is: \")\n",
    "        return p \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rupees:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1510"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_of_ten(1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save at least 1000 to get profit of Rupees 10!!!\n",
      "Your Current Balance is: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "900"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_of_ten(900)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with more than one arguments at the same time:\n",
    "#### Problem statement: Suppose, The DC charecter Flash one day discovered that he has the ability to pick up Thors hammer and can throw it in the speed of light to disintegrate it into energy, As, according to the famous equation:\n",
    "> *E = mc2*\n",
    "#### Let us assume that mass of thors hammer is 10000 kg and speed of light is 2.9 * 10^-8 m/s\n",
    "#### Calculate the energy release???"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets define a function Release_Energy, containing the input values for mass(m)and speed of light(c)\n",
    "#fomulla is defined bu E, so to get the result we need to return E:\n",
    "\n",
    "def Released_Energy(m,c):\n",
    "    E = m * c ** 2\n",
    "    print(\"Mass =\", m, \"kg\")\n",
    "    print(\"speed of light =\", c, \"miles/sec\")\n",
    "    print(\"Released energy in Joules:\")\n",
    "    return E\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mass = 1000 kg\n",
      "speed of light = 2.8999999999999998e-08 miles/sec\n",
      "Released energy in Joules:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8.409999999999998e-13"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Released_Energy(1000, 2.9 * 10 ** -8) #we can replace any mass in the place of m to get the relevant energy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some common built in functions in Python:\n",
    "> sum() : To get the sum of numbers, <br> round(x,y) : To return float of x with y number of decimal places <br> pow(x,y) : To fint x to the power of y <br> len() : To find the total number of elements in an object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#examples for sum():\n",
    "weight = [10,20,30,40,60,100]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check the sum of all the numbers in weight :\n",
    "sum (weight) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4444444444444444"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of round() :\n",
    "a = 13/9\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.44"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(a, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9e+18"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of pow() :\n",
    "#example 1: \n",
    "speed_of_light = 2.9 * pow(10, 18)\n",
    "speed_of_light"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1073741824"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example 2: 2 to the power 30 is\n",
    "pow(2,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(): how many element in the previouly created object called weight?\n",
    "len(weight)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of objects :6\n"
     ]
    }
   ],
   "source": [
    "#to write the result in a particular format we can convert intiger part to string :\n",
    "y = len(weight)\n",
    "print(\"number of objects :\" + str(y))"
   ]
  },
  {
   "attachments": {
    "images.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Any sequence of data points can be saved in the following three formats, <br> Each of them has a particular role and use:\n",
    "> List denoted by [], elements with 'element'- single quotation mark <br>\n",
    "> Tuplet denoted by () <br>\n",
    "> Dictionary denoted by {} <br>\n",
    "\n",
    "#### List:\n",
    "\n",
    "##### \"Lets visit a zoo\"\n",
    "\n",
    "![images.jpg](attachment:images.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a list of all animals in a zoo:\n",
    "zoo_all = ['monkey', 'lion', 'elephant', 'birds', 'rabbits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey', 'lion', 'elephant', 'birds', 'rabbits']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check the animal names in the list called zoo:\n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lion'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting any element based on indexing starting from 0 to 1,2,3... from left to right \n",
    "#and -1,-2,-3 from right to left or from the end:\n",
    "zoo_all[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rabbits'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_all[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey', 'lion', 'rhino', 'birds', 'rabbits']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace elephant with rhino in the list called zoo: \n",
    "zoo_all[2] = 'rhino' \n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zoo_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey', 'rhino', 'birds', 'rabbits']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as lion got shifted to another bigger zoo delete the entry from the zoo: DELETION\n",
    "#DELETION changes the index of each element\n",
    "del zoo_all[1]\n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey', 'elephant', 'birds', 'rabbits']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace rhino with elephant:\n",
    "zoo_all[1] = 'elephant'\n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey', 'elephant', 'birds', 'rabbits', 'lion']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add lion again to the list:\n",
    "zoo_all.append('lion')\n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check the index for lion:\n",
    "zoo_all.index('lion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combining two zoo together:\n",
    "another_zoo = ['snakes','polar bear', 'chimps']\n",
    "zoo_all.extend(another_zoo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey',\n",
       " 'elephant',\n",
       " 'birds',\n",
       " 'rabbits',\n",
       " 'lion',\n",
       " 'snakes',\n",
       " 'polar bear',\n",
       " 'chimps']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zoo_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing : slice can be used to capture a smaller sub-set of a lagrger data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['monkey', 'elephant', 'birds', 'rabbits', 'lion', 'snakes']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#slice the list zoo fron first before the second last elements:\n",
    "zoo_all[:-2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['birds', 'rabbits', 'lion', 'snakes', 'polar bear', 'chimps']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#slice the list zoo after second element till the end:\n",
    "zoo_all[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['birds',\n",
       " 'chimps',\n",
       " 'elephant',\n",
       " 'lion',\n",
       " 'monkey',\n",
       " 'polar bear',\n",
       " 'rabbits',\n",
       " 'snakes']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort in alphabetical order:\n",
    "zoo_all.sort()\n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['snakes',\n",
       " 'rabbits',\n",
       " 'polar bear',\n",
       " 'monkey',\n",
       " 'lion',\n",
       " 'elephant',\n",
       " 'chimps',\n",
       " 'birds']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reverse alphabetic:\n",
    "zoo_all.sort(reverse = True)\n",
    "zoo_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list of lists: To create a bigget list from two smaller ones, <br> \n",
    "> #### Suppose you have three old friends in school <br> After 2 years you have 4 more new friends. <br> If we consider old_friends and new_friends as two seperate lists <br> How can you add list of lists with a bigget list name all_friends????\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['sudip', 'saurav', 'sapto'], ['john', 'kamakhya', 'kaushik']]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_friends = ['sudip', 'saurav', 'sapto']\n",
    "new_friends = ['john' , 'kamakhya', 'kaushik']\n",
    "\n",
    "all_friends = [old_friends , new_friends]\n",
    "all_friends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['john', 'kamakhya', 'kaushik'], ['sudip', 'saurav', 'sapto']]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_friends.sort()\n",
    "all_friends\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All about Tuplets:\n",
    "> * Tuplets are another kind of data sequence, these are unmutable in nature (can't be changed), <br>\n",
    "> * Elements are placed with a parenthesis ( )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, 5)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets create a Tuplet:\n",
    "x = (1,2,3,4,5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 7, 8, 9, 10)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#f we dont mention the data type it becomes tuplet by default:\n",
    "y= 6,7,8,9,10\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tuplets are the reason that the variable assignments never change :\n",
    "p,q,r,s,t = 10,11,12,13,14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 2, 3, 4, 5), (6, 7, 8, 9, 10))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combilne x and y:\n",
    "z = (x,y)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#comma-separated-values in tuplets:\n",
    "(Gender,age) = (\"male\",26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'male'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age = 26\n",
      "Gender = male\n"
     ]
    }
   ],
   "source": [
    "print(\"Age = \" + str(age) )\n",
    "print(\"Gender = \" + Gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fuction can return the result in Tuple:\n",
    "#### lets work with a circle and findout:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.14"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define the value of pi: \n",
    "pi = round(3.141592653589793238, 2)\n",
    "pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def circle(r):\n",
    "    Area = pi * r ** 2\n",
    "    perimeter = 2 * pi * r\n",
    "    print(\"Area & perimeter :\")\n",
    "    return Area, perimeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area & perimeter :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3.14, 6.28)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#area and perimeter of a circle of radius (r) 1 cm\n",
    "circle(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area & perimeter :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(12.56, 12.56)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#radius (r) 2 cm:\n",
    "circle(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area & perimeter :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(30.959144000000002, 19.7192)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circle(3.14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary:\n",
    "#### Represent another way of storing a data.\n",
    "#### Denoted by { }, curley braces\n",
    "#### Each value is associated with a certain \"key\" with contains a respective \"value\", <br> Together forms a 'key-value-pair'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example of a dictionary:\n",
    "\n",
    "dict = {'key1' : '1', 'key2' : '2', 'key3' : '3', 'key4' : '4'} #Here, key1,key2,key3,key4 are keys and 1,2,3,4 are values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict['key1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding a new value to the dictionary dict:\n",
    "\n",
    "dict['key5'] = '5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key1': '1', 'key2': '2', 'key3': '3', 'key4': '4', 'key5': '5'}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Simpler and more logicaL way to create a dictionary\n",
    "\n",
    "Amazon_team = {}\n",
    "Amazon_team['manager'] = 'Abhishek'\n",
    "Amazon_team['customer_service'] = 'Jishnu'\n",
    "Amazon_team['trms'] = 'Anisha'\n",
    "Amazon_team['sales'] = 'Niranjan'\n",
    "Amazon_team['advisor'] = 'Melody'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'manager': 'Abhishek', 'customer_service': 'Jishnu', 'trms': 'Anisha', 'sales': 'Niranjan', 'advisor': 'Melody'}\n"
     ]
    }
   ],
   "source": [
    "print(Amazon_team)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melody\n"
     ]
    }
   ],
   "source": [
    "print(Amazon_team['advisor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jishnu\n"
     ]
    }
   ],
   "source": [
    "print(Amazon_team['customer_service'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'noone'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-110-fe132b0e0432>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#what if something i ask for which is not there in the dict????? it will give error!!!!\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[0mAmazon_team\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'noone'\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;31mKeyError\u001b[0m: 'noone'"
     ]
    }
   ],
   "source": [
    "#what if something i ask for which is not there in the dict????? it will give error!!!!\n",
    "print(Amazon_team['noone'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iteration : in list and dictionary\n",
    "#### Is the ability to execute a certain code repeatedly on a loop...\n",
    "> for loop <br>\n",
    "> while loop\n",
    "#### Example of a for loop in a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets make a list first \n",
    "even = [0,2,4,6,8,10,12,14,16,18,20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "#for loop function:\n",
    "for n in even:\n",
    "    print(n)                                   #where n is any number in the list even"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2 4 6 8 10 12 14 16 18 20 "
     ]
    }
   ],
   "source": [
    "#to print the same result horizontally:\n",
    "even = [0,2,4,6,8,10,12,14,16,18,20]\n",
    "for n in even:\n",
    "    print(n, end=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2 4 6 8 10 12 14 16 18 20 "
     ]
    }
   ],
   "source": [
    "#while loop:\n",
    "x = 0\n",
    "while x <= 20:\n",
    "    print(x, end = \" \")\n",
    "    x=x+2 #also known as increment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 10)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create list with range() :\n",
    "range(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10)) #end=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(3,7,1)) #range(start,end,step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All the even numbers from 0-30:\n",
    "list(range(0,30,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All the odd numbers from 0-30:\n",
    "list(range(1,30,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 4 8 16 32 64 128 256 512 "
     ]
    }
   ],
   "source": [
    "#Use for loop to create a list from 1-10 range with the values starting from 2^1 to 2^9:\n",
    "#wrong way:\n",
    "for n in range(1,10,1):\n",
    "    print(2 ** n, end = \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 4 8 16 32 64 128 256 512 "
     ]
    }
   ],
   "source": [
    "#correct way:\n",
    "for n in range(10):\n",
    "    print(2 ** n, end = \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 20)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print all the even numbers between 0 to 19 and print the odd places with the string \"odd-number\":\n",
    "#create a range:\n",
    "range(20)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the range in list format:\n",
    "list(range(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Odd-number 2 Odd-number 4 Odd-number 6 Odd-number 8 Odd-number 10 Odd-number 12 Odd-number 14 Odd-number 16 Odd-number 18 Odd-number "
     ]
    }
   ],
   "source": [
    "#create the for function:\n",
    "for n in range(20):\n",
    "    if n % 2 == 0:                  # n % 2, signifies where the remainder for n when divided by 2 is exactly equals to 0 \n",
    "        print (n,end=\" \")\n",
    "    else:\n",
    "        print(\"Odd-number\",end=\" \")\n",
    "    \n"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple linear regression analysis :\n",
    "#### - It is also known as Ordinary Least Square (OLS) method. <br> - Least Square is also known as the minimum squared error or minSSE. <br> (lower the error meands better explanatory potwntial of the data in the regression model) <br> -  with the help of Calculus and linear algebra, <br> The ideal slope and intercepts are determine computationally closest to all the data points. \n",
    "\n",
    "![linear%20regression%20model.JPG](attachment:linear%20regression%20model.JPG)\n",
    "\n",
    "#### Three majoy terms falls under the ANOVA framework are:\n",
    "#### - sum of squares total (SST) <br>(squsred differences between observed and its mean) <br>\n",
    "![sst.JPG](attachment:sst.JPG)\n",
    "#### - sum of squares regression (SSR) <br> (sum of the differences between the predicted value and the mean of the dependent variables) <br> \n",
    "![ssr.JPG](attachment:ssr.JPG)\n",
    "#### - sum of sruares error (SSE) <br> (sum of the squares of the error - lower the value better) <br>\n",
    "![sse.JPG](attachment:sse.JPG)\n",
    "### Relation between all three:\n",
    "#### $SST = SSR + SSE$\n",
    "\n",
    "\n",
    "### Rsquared method:\n",
    "\n",
    "* SSR:\n",
    "#### ![rsquared_1.JPG](attachment:rsquared_1.JPG)\n",
    "\n",
    "#### How do we know whats a good r-squared value????\n",
    "\n",
    "![r_squared_2.JPG](attachment:r_squared_2.JPG)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing all essential libraries requied to handel the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('1.01.+Simple+linear+regression.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1714</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1664</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1760</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1685</td>\n",
       "      <td>2.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1693</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1936</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1810</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1987</td>\n",
       "      <td>3.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1962</td>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2050</td>\n",
       "      <td>3.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SAT   GPA\n",
       "0   1714  2.40\n",
       "1   1664  2.52\n",
       "2   1760  2.54\n",
       "3   1685  2.74\n",
       "4   1693  2.83\n",
       "..   ...   ...\n",
       "79  1936  3.71\n",
       "80  1810  3.71\n",
       "81  1987  3.73\n",
       "82  1962  3.76\n",
       "83  2050  3.81\n",
       "\n",
       "[84 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use pandas to descrive the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1845.273810</td>\n",
       "      <td>3.330238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.530661</td>\n",
       "      <td>0.271617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1634.000000</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1772.000000</td>\n",
       "      <td>3.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1846.000000</td>\n",
       "      <td>3.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1934.000000</td>\n",
       "      <td>3.502500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2050.000000</td>\n",
       "      <td>3.810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT        GPA\n",
       "count    84.000000  84.000000\n",
       "mean   1845.273810   3.330238\n",
       "std     104.530661   0.271617\n",
       "min    1634.000000   2.400000\n",
       "25%    1772.000000   3.190000\n",
       "50%    1846.000000   3.380000\n",
       "75%    1934.000000   3.502500\n",
       "max    2050.000000   3.810000"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Count : number of observations, <br> mean, <br> std : standard deviation, <br> min : minimum value, <br> max : maximum value\n",
    "* SAT = Criticalreading+mathematics+writing\n",
    "* GPA = Grade Point Average (at graduation from university)\n",
    "#### Problem satement:\n",
    "- Regression model : will estimate the GPA from SAT scores "
   ]
  },
  {
   "attachments": {
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aooorI0CiiigAooop9ACiiikAUUUU0AUyb/Vn8P50UU2Mfgbicc0n8VFFSLoxaRulFFNErccOtN2gsARmiimOQDoKcAD2ooqHuV0GlQ3UZo6MAOmKKKvoZx3FoooqSz//2Q=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the dependent and indipendent variables:\n",
    "> The equation is : ![equation_.jpg](attachment:equation_.jpg)\n",
    "\n",
    "#### Here, our dependent variable is GPA  <br> and indipendent variable is SAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['GPA']\n",
    "x1 = data['SAT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a scatter plot of the data to know it better :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, y)\n",
    "plt.xlabel('SAT', fontsize = 20)\n",
    "plt.ylabel('GPA', fontsize = 20)\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression itself using Statsmodels:\n",
    "#### OLS method: ordinary least squares is a type of linear least squares method for estimating the unknown parameters in a linear regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>GPA</td>       <th>  R-squared:         </th> <td>   0.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   56.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 18 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>7.20e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:01:06</td>     <th>  Log-Likelihood:    </th> <td>  12.672</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>  -21.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    82</td>      <th>  BIC:               </th> <td>  -16.48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.2750</td> <td>    0.409</td> <td>    0.673</td> <td> 0.503</td> <td>   -0.538</td> <td>    1.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SAT</th>   <td>    0.0017</td> <td>    0.000</td> <td>    7.487</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.839</td> <th>  Durbin-Watson:     </th> <td>   0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  16.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.722</td> <th>  Prob(JB):          </th> <td>0.000310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.590</td> <th>  Cond. No.          </th> <td>3.29e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.29e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    GPA   R-squared:                       0.406\n",
       "Model:                            OLS   Adj. R-squared:                  0.399\n",
       "Method:                 Least Squares   F-statistic:                     56.05\n",
       "Date:                Fri, 18 Dec 2020   Prob (F-statistic):           7.20e-11\n",
       "Time:                        00:01:06   Log-Likelihood:                 12.672\n",
       "No. Observations:                  84   AIC:                            -21.34\n",
       "Df Residuals:                      82   BIC:                            -16.48\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.2750      0.409      0.673      0.503      -0.538       1.088\n",
       "SAT            0.0017      0.000      7.487      0.000       0.001       0.002\n",
       "==============================================================================\n",
       "Omnibus:                       12.839   Durbin-Watson:                   0.950\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               16.155\n",
       "Skew:                          -0.722   Prob(JB):                     0.000310\n",
       "Kurtosis:                       4.590   Cond. No.                     3.29e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.29e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= sm.add_constant(x1)\n",
    "results = sm.OLS(y,x).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot regression line in the same scatter plot : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, y)\n",
    "yhat=0.0017*x1 + +0.275\n",
    "fig = plt.plot(x1, yhat, lw=4, c='orange', label='Regression line')\n",
    "plt.xlabel('SAT', fontsize = 20)\n",
    "plt.ylabel('GPA', fontsize = 20)\n",
    "plt.show"
   ]
  },
  {
   "attachments": {
    "equation_.jpg": {
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"
    },
    "result_summary_.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Understanding the data involved and the relation with the summary:\n",
    "> ![result_summary_.jpg](attachment:result_summary_.jpg)\n",
    "The equation is :\n",
    "![equation_.jpg](attachment:equation_.jpg)\n",
    "### so the equation here transforms to:\n",
    "#### yhat=0.0017*x1  +0.275\n",
    "or \n",
    "##### GPA = 0.0017*SAT +0.275\n",
    "\n",
    "#### As the P value defines the significance of the variable, <br> it P value is below 0.05 = significant \n",
    "\n",
    "![r_squared.jpg](attachment:r_squared.jpg)"
   ]
  },
  {
   "attachments": {
    "mlra_.jpg": {
     "image/jpeg": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple regression ( MR) models :\n",
    "#### Population model for MR looks like :\n",
    "![mra.JPG](attachment:mra.JPG)\n",
    "\n",
    "![mlra_.jpg](attachment:mlra_.jpg)\n",
    "\n",
    "#### - Its not about the best fitting line anymore, <br>\n",
    "\n",
    "#### - It stops being 2 dimensional and its very complicated to visualize the data. <br>\n",
    "\n",
    "#### - MR is about finding the best fitting model.\n",
    "\n",
    "#### - Previously we have seen the rsquared value for the given data from the summary was 0.406 means the provided data have around 40% of variability. \n",
    "   > * Which signifies that we are loosing some important variables except GPA and SAT values. \n",
    "   >* If some other variable such as gender and age and others support our framed question of Research we can add those data.\n",
    "\n",
    "#### - Then the OLR will turn into MR\n",
    "\n",
    "#### - As the data becomes multidimensional its very essential to optimise data if two or thee of the different variables somehow are similar we should select the most significant one and ignore the rest. \n",
    "\n",
    "#### -  Adjusted R-squared,  used in MR models. Its value will always be smaller than the R-squared in OLR \n",
    "\n",
    "#### - This is the reason MLR is always better than the OLR, as it increases the dimesionality of the data and can help us asnswer better questions out of the colledted data.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple exercise on Multiple linear regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries:\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the data containing three variables:\n",
    "data = pd.read_csv('1.02.+Multiple+linear+regression.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Rand 1,2,3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1714</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1664</td>\n",
       "      <td>2.52</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1760</td>\n",
       "      <td>2.54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1685</td>\n",
       "      <td>2.74</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1693</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1670</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1764</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1764</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1792</td>\n",
       "      <td>3.01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1850</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAT   GPA  Rand 1,2,3\n",
       "0  1714  2.40           1\n",
       "1  1664  2.52           3\n",
       "2  1760  2.54           3\n",
       "3  1685  2.74           3\n",
       "4  1693  2.83           2\n",
       "5  1670  2.91           1\n",
       "6  1764  3.00           2\n",
       "7  1764  3.00           1\n",
       "8  1792  3.01           2\n",
       "9  1850  3.01           3"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheak the from the top of the csv file starting from 0 to 9:\n",
    "data.head(10)"
   ]
  },
  {
   "attachments": {
    "newmodel.JPG": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New model:\n",
    "![newmodel.JPG](attachment:newmodel.JPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Rand 1,2,3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1845.273810</td>\n",
       "      <td>3.330238</td>\n",
       "      <td>2.059524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.530661</td>\n",
       "      <td>0.271617</td>\n",
       "      <td>0.855192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1634.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1772.000000</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1846.000000</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1934.000000</td>\n",
       "      <td>3.502500</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2050.000000</td>\n",
       "      <td>3.810000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT        GPA  Rand 1,2,3\n",
       "count    84.000000  84.000000   84.000000\n",
       "mean   1845.273810   3.330238    2.059524\n",
       "std     104.530661   0.271617    0.855192\n",
       "min    1634.000000   2.400000    1.000000\n",
       "25%    1772.000000   3.190000    1.000000\n",
       "50%    1846.000000   3.380000    2.000000\n",
       "75%    1934.000000   3.502500    3.000000\n",
       "max    2050.000000   3.810000    3.000000"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets perform multiple regression:\n",
    "y = data['GPA']\n",
    "x2 = data[['SAT','Rand 1,2,3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>GPA</td>       <th>  R-squared:         </th> <td>   0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   27.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 18 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>6.58e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:52:59</td>     <th>  Log-Likelihood:    </th> <td>  12.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>  -19.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>  -12.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>      <td>    0.2960</td> <td>    0.417</td> <td>    0.710</td> <td> 0.480</td> <td>   -0.533</td> <td>    1.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SAT</th>        <td>    0.0017</td> <td>    0.000</td> <td>    7.432</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rand 1,2,3</th> <td>   -0.0083</td> <td>    0.027</td> <td>   -0.304</td> <td> 0.762</td> <td>   -0.062</td> <td>    0.046</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.992</td> <th>  Durbin-Watson:     </th> <td>   0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  16.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.731</td> <th>  Prob(JB):          </th> <td>0.000280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.594</td> <th>  Cond. No.          </th> <td>3.33e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.33e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    GPA   R-squared:                       0.407\n",
       "Model:                            OLS   Adj. R-squared:                  0.392\n",
       "Method:                 Least Squares   F-statistic:                     27.76\n",
       "Date:                Fri, 18 Dec 2020   Prob (F-statistic):           6.58e-10\n",
       "Time:                        02:52:59   Log-Likelihood:                 12.720\n",
       "No. Observations:                  84   AIC:                            -19.44\n",
       "Df Residuals:                      81   BIC:                            -12.15\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.2960      0.417      0.710      0.480      -0.533       1.125\n",
       "SAT            0.0017      0.000      7.432      0.000       0.001       0.002\n",
       "Rand 1,2,3    -0.0083      0.027     -0.304      0.762      -0.062       0.046\n",
       "==============================================================================\n",
       "Omnibus:                       12.992   Durbin-Watson:                   0.948\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               16.364\n",
       "Skew:                          -0.731   Prob(JB):                     0.000280\n",
       "Kurtosis:                       4.594   Cond. No.                     3.33e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.33e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= sm.add_constant(x2)\n",
    "results = sm.OLS(y,x).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>GPA</td>       <th>  R-squared:         </th> <td>   0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   27.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 18 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>6.58e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:53:08</td>     <th>  Log-Likelihood:    </th> <td>  12.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>  -19.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>  -12.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>      <td>    0.2960</td> <td>    0.417</td> <td>    0.710</td> <td> 0.480</td> <td>   -0.533</td> <td>    1.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SAT</th>        <td>    0.0017</td> <td>    0.000</td> <td>    7.432</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rand 1,2,3</th> <td>   -0.0083</td> <td>    0.027</td> <td>   -0.304</td> <td> 0.762</td> <td>   -0.062</td> <td>    0.046</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.992</td> <th>  Durbin-Watson:     </th> <td>   0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  16.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.731</td> <th>  Prob(JB):          </th> <td>0.000280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.594</td> <th>  Cond. No.          </th> <td>3.33e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.33e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    GPA   R-squared:                       0.407\n",
       "Model:                            OLS   Adj. R-squared:                  0.392\n",
       "Method:                 Least Squares   F-statistic:                     27.76\n",
       "Date:                Fri, 18 Dec 2020   Prob (F-statistic):           6.58e-10\n",
       "Time:                        02:53:08   Log-Likelihood:                 12.720\n",
       "No. Observations:                  84   AIC:                            -19.44\n",
       "Df Residuals:                      81   BIC:                            -12.15\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.2960      0.417      0.710      0.480      -0.533       1.125\n",
       "SAT            0.0017      0.000      7.432      0.000       0.001       0.002\n",
       "Rand 1,2,3    -0.0083      0.027     -0.304      0.762      -0.062       0.046\n",
       "==============================================================================\n",
       "Omnibus:                       12.992   Durbin-Watson:                   0.948\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               16.364\n",
       "Skew:                          -0.731   Prob(JB):                     0.000280\n",
       "Kurtosis:                       4.594   Cond. No.                     3.33e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.33e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### what difference does the multiple linear regression makes from the previously performed OLR:\n",
    "* Rsquared have increased : have increased the explanatory power of the data \n",
    "at the same trime adjusted r-squared have not chaged much tells us that the third value 'Rand 1,2,3' have not much significance with the rest of the data. indications to choose something more significant. that make sence as the 'Rand 1,2,3' are randomly assigned 1,2,3 to each of the data frames.\n",
    "* P value:\n",
    "the P-value is 0.762 and the significance threshold was <0.05, null hypothesis states that the b2 = 0 and it cant not reject null hypothesis with 76 % of significance value.\n",
    "* Intercepts :\n",
    "from the previous equation for OLR: $yHAT = 0.275 + 0.0017x1$ <br>\n",
    "currect equation will be : $yHAT = 0.296 + 0.0017x1 - 0.0083x2$\n",
    "\n",
    "> Bias of this variable is reflected into the coefficients of other variables\n",
    "\n",
    " <font color = red> The third variable 'Rand 1,2,3' affected the intercept i.e from initial value of 0.275 shifted to 0.296 <br> SIGNIFIES ITS NOT A GREAT VARIABLE TO WORK WITH AS ITS AFFECTING THE MODEL!!!!!!!!!!!!! </FONT>\n",
    " \n",
    "variable optimization is an intensive solution. multiple variables in a data set can give irrelavant result for that subject knowledge and expertise in the selection of the correct varible is very significant. \n",
    "\n",
    "## <font color = green>-----------------------------------------------HAPPY REGRESSING--------------------------------------------------------</font>\n",
    "\n"
   ]
  }
 ],
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