BasicsOfPython.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Expressions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-80"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-2 - 78"
   ]
  },
  {
   "attachments": {
    "expressions.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![expressions.png](attachment:expressions.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " 7 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 // 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 % 2"
   ]
  },
  {
   "attachments": {
    "mod.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![mod.png](attachment:mod.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25//4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25%4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(-2.0  -8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(4 / 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 % 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "15 % 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/2.0 + 1/4.0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.1 + 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2 + 2) * 2 # overriding precedence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "See more about precedence here: https://docs.python.org/3/reference/expressions.html#operator-precedence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Booleon Expression / Conditional Expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 == 7/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 < 4        # conditional expression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 == 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'a3' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-682e27535569>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma3\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m   \u001b[0;31m# ignore the *details* of the error for now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ma3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'a3' is not defined"
     ]
    }
   ],
   "source": [
    "a3 == 3   # ignore the *details* of the error for now\n",
    "a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 > 4 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 >= 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 <= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other types of expressions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abc'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'abc' #String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"xyz\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is a multi line string\\nit can span multiple lines'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"This is a multi line string\n",
    "it can span multiple lines\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcd'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"ab\" + \"cd\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"ab\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"a-\" * 3) * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and True "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False and True "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False and False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or True "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False or False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True or False and False or False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(True or False) and (False or False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Call Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "[Expression Evaluation Process](expressions.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Lets evaluate some call expressions!!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from operator import mul,add\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add ( 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul(add(2,4),5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "224"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul(add(4, mul(4, 6)), add(3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(7, 9, 12,2,45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(2+25, 9) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow(4, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max( min(1, -2) , min( pow(3, 5) , -4)    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [(4, 'a'), (3, 'b', 5), ('c', 5, 7)]\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a[2][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A cube has 8 corners:\n",
      "\n",
      "          7------8\n",
      "         /|     /|\n",
      "        3------4 |\n",
      "        | |    | |\n",
      "        | 5----|-6\n",
      "        |/     |/\n",
      "        1------2\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = '''\n",
    "A cube has 8 corners:\n",
    "\n",
    "          7------8\n",
    "         /|     /|\n",
    "        3------4 |\n",
    "        | |    | |\n",
    "        | 5----|-6\n",
    "        |/     |/\n",
    "        1------2\n",
    "\n",
    "'''\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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