AI-vanilla.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019-08-24 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### code source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/sharmasaravanan/Machine_learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://drive.google.com/drive/folders/17dMizJX_2XY845HuCl3Gy54OMBrBJpv7?usp=sharing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "a = 5\n",
    "b = 10\n",
    "print(a+b)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 5 # int \n",
    "b = 3.14 # float\n",
    "c = \"apple\" # string\n",
    "d = True # bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "print(type(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n"
     ]
    }
   ],
   "source": [
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "print(456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print('hello world')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Arithmetic operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ - * / %"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logical operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and, or , not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conditional Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "< , >, <=, >=, ==, !="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Misc operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is, in , not in "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### User input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your name : 85\n"
     ]
    }
   ],
   "source": [
    "name = input(\"Enter your name : \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85\n"
     ]
    }
   ],
   "source": [
    "print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number : l\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'l'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-5aaf6976f909>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter any number : \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'l'"
     ]
    }
   ],
   "source": [
    "num = int(input(\"Enter any number : \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "615 (555+55+5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "print(type(num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "615\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "ans = int(str(n)+str(n)+str(n))+int(str(n)+str(n))+n\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "615\n"
     ]
    }
   ],
   "source": [
    "n = '5'\n",
    "ans = int(n*3)+int(n*2)+int(n)\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "615\n"
     ]
    }
   ],
   "source": [
    "n = '5'\n",
    "ans = int(n+n+n)+int(n+n)+int(n)\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "star = \"* \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  * \n",
      " * * \n",
      "* * * \n"
     ]
    }
   ],
   "source": [
    "print(\"  \"+star)\n",
    "print(\" \"+star*2)\n",
    "print(star*3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hello apple \n",
    "\n",
    "hello 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method-1\n",
      "hello apple\n",
      "hello 5\n",
      "--------------------\n",
      "Method-2\n",
      "hello apple\n",
      "hello 5\n",
      "--------------------\n",
      "Method-3\n",
      "hello apple\n",
      "hello 5\n",
      "--------------------\n",
      "Method-4\n",
      "hello apple\n",
      "hello 5\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "print(\"Method-1\")\n",
    "print(\"hello \"+c)\n",
    "print(\"hello \"+str(a))\n",
    "print(\"----\"*5)\n",
    "\n",
    "print(\"Method-2\")\n",
    "print(\"hello\",c)\n",
    "print(\"hello\",a)\n",
    "print(\"----\"*5)\n",
    "\n",
    "print(\"Method-3\")\n",
    "print(\"hello %s\" %c)\n",
    "print(\"hello %d\" %a)\n",
    "print(\"----\"*5)\n",
    "\n",
    "print(\"Method-4\")\n",
    "print(\"hello {}\".format(c))\n",
    "print(\"hello {}\".format(a))\n",
    "print(\"----\"*5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i bought 2kgs of apple for 85.5rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 2\n",
    "f = 'apple'\n",
    "p = 85.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i bought 2kgs of apple for 85.5rs\n",
      "i bought 2 kgs of apple for 85.5 rs\n",
      "i bought 2kgs of apple for 85.5rs\n",
      "i bought 4kgs of apple for 256.5rs\n"
     ]
    }
   ],
   "source": [
    "print(\"i bought \"+str(w)+\"kgs of \"+f+\" for \"+str(p)+\"rs\")\n",
    "print(\"i bought\",w,\"kgs of\",f,\"for\",p,'rs')\n",
    "print(\"i bought %dkgs of %s for %0.1frs\"%(w,f,p))\n",
    "print(\"i bought {}kgs of {} for {}rs\".format(w*2,f,p*3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If-else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number : 74\n",
      "Even\n"
     ]
    }
   ],
   "source": [
    "num = int(input(\"Enter any number : \"))\n",
    "if num%2 == 0:\n",
    "    print('Even')\n",
    "else:\n",
    "    print('Odd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number : 20\n",
      "Divisible by 4 and even\n",
      "Even\n"
     ]
    }
   ],
   "source": [
    "num = int(input(\"Enter any number : \"))\n",
    "if num%4 == 0:\n",
    "    print(\"Divisible by 4 and even\")\n",
    "elif num%2 == 0:\n",
    "    print('Even')\n",
    "else:\n",
    "    print('odd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes!!\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "if num >= 10 and num <= 50:\n",
    "    print(\"yes!!\")\n",
    "print('hello')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "The area of the circle is 25sq.cm for the radius 3cm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number : 74\n",
      "Even\n",
      "Enter any number : 63\n",
      "odd\n",
      "Enter any number : 41\n",
      "odd\n",
      "Enter any number : 15\n",
      "skipping\n",
      "Enter any number : 21\n",
      "odd\n",
      "Enter any number : 23\n",
      "odd\n",
      "Enter any number : 25\n",
      "stopping\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    num = int(input(\"Enter any number : \"))\n",
    "    \n",
    "    if num == 25:\n",
    "        print(\"stopping\")\n",
    "        break\n",
    "    elif num == 15:\n",
    "        print(\"skipping\")\n",
    "        continue\n",
    "        \n",
    "    if num%4 == 0:\n",
    "        print(\"Divisible by 4 and even\")\n",
    "    elif num%2 == 0:\n",
    "        print('Even')\n",
    "    else:\n",
    "        print('odd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number : 63\n",
      "odd\n",
      "Enter any number : 74\n",
      "Even\n",
      "Enter any number : 15\n",
      "skipping\n",
      "Enter any number : 96\n",
      "Divisible by 4 and even\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while i <= 3:\n",
    "    num = int(input(\"Enter any number : \"))\n",
    "    \n",
    "    if num == 25:\n",
    "        print(\"stopping\")\n",
    "        break\n",
    "    elif num == 15:\n",
    "        print(\"skipping\")\n",
    "        continue\n",
    "        \n",
    "    if num%4 == 0:\n",
    "        print(\"Divisible by 4 and even\")\n",
    "    elif num%2 == 0:\n",
    "        print('Even')\n",
    "    else:\n",
    "        print('odd')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any number : 74\n",
      "Even\n",
      "Do you want to continue ? y/ny\n",
      "Enter any number : 32\n",
      "Divisible by 4 and even\n",
      "Do you want to continue ? y/nh\n"
     ]
    }
   ],
   "source": [
    "opt = 'y'\n",
    "while opt == 'y':\n",
    "    num = int(input(\"Enter any number : \"))\n",
    "    \n",
    "    if num == 25:\n",
    "        print(\"stopping\")\n",
    "        break\n",
    "    elif num == 15:\n",
    "        print(\"skipping\")\n",
    "        continue\n",
    "        \n",
    "    if num%4 == 0:\n",
    "        print(\"Divisible by 4 and even\")\n",
    "    elif num%2 == 0:\n",
    "        print('Even')\n",
    "    else:\n",
    "        print('odd')\n",
    "    \n",
    "    opt = input(\"Do you want to continue ? y/n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user1 == 'q' or user1 == 'quit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp = r.randint(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'apple'\n",
    "sent = \"elephant is an animal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ple'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'app'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ppl'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'eehn sa '"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent[0:15:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant is an '"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elppa'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Elephant is an animal'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.casefold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'      apple       '"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.center(18,' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.count('p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.encode('utf-16').decode('utf-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.endswith('lE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.startswith('ap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i           am          sharma'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"i\\tam\\tsharma\".expandtabs(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.find(\"p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "substring not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-a593ec84d793>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: substring not found"
     ]
    }
   ],
   "source": [
    "name.index('z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.isprintable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter something :Hooo\n",
      "HOOO\n"
     ]
    }
   ],
   "source": [
    "someInput = input(\"enter something :\")\n",
    "if someInput.isupper():\n",
    "    print(someInput.lower())\n",
    "else:\n",
    "    print(someInput.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Elephant Is An Animal'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a**le'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.replace('p',\"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' apple '"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant is an animal'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent.zfill(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \" apple \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple '"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.lstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' apple'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.rstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'app'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name.strip().strip('le')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = sent.split('an')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['eleph', 't is ', ' ', 'imal']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'elephant is an animal'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'an'.join(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rock beats scissor\n",
    "scissor beats paper\n",
    "paper beats rock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2019-09-21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [2,4,6,1.3,3.5,5.7,'apple','mango','papaya']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 6, 1.3, 3.5, 5.7, 'apple', 'mango', 'papaya']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 5.7]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[2:8:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "l[2] = 58"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 1.3, 3.5, 5.7, 'apple', 'mango', 'papaya']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "del l[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2 = [58,36,41,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[58, 36, 41, 12]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "l3 = l+l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya', 58, 36, 41, 12]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.append('orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya', 'orange']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.insert(3,78945)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 78945, 3.5, 5.7, 'apple', 'mango', 'papaya', 'orange']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'orange'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 78945, 3.5, 5.7, 'apple', 'mango', 'papaya']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.remove(5.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 78945, 3.5, 'apple', 'mango', 'papaya']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "l4 = l3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.count(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.index(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.extend(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2,\n",
       " 4,\n",
       " 58,\n",
       " 78945,\n",
       " 3.5,\n",
       " 'apple',\n",
       " 'mango',\n",
       " 'papaya',\n",
       " 2,\n",
       " 4,\n",
       " 58,\n",
       " 3.5,\n",
       " 5.7,\n",
       " 'apple',\n",
       " 'mango',\n",
       " 'papaya',\n",
       " 58,\n",
       " 36,\n",
       " 41,\n",
       " 12]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "l.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2,\n",
       " 4,\n",
       " 58,\n",
       " 78945,\n",
       " 3.5,\n",
       " 'apple',\n",
       " 'mango',\n",
       " 'papaya',\n",
       " 2,\n",
       " 4,\n",
       " 58,\n",
       " 3.5,\n",
       " 5.7,\n",
       " 'apple',\n",
       " 'mango',\n",
       " 'papaya',\n",
       " 58,\n",
       " 36,\n",
       " 41,\n",
       " 12]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-fb07ac7c73ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'"
     ]
    }
   ],
   "source": [
    "l.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya', 58, 36, 41, 12]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = (2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya', 58, 36, 41, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya', 58, 36, 41, 12)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-52-221bcce4377e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "t[2] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object doesn't support item deletion",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-2d0f41a77003>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object doesn't support item deletion"
     ]
    }
   ],
   "source": [
    "del t[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'a':23,\"b\":3.14,2:\"apple\",'d':[85,96,45],\"e\":{'fruit':\"mango\",\"vege\":\"carrot\"}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 23,\n",
       " 'b': 3.14,\n",
       " 2: 'apple',\n",
       " 'd': [85, 96, 45],\n",
       " 'e': {'fruit': 'mango', 'vege': 'carrot'}}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['b'] = 6.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "del d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 23,\n",
       " 'b': 6.56,\n",
       " 'd': [85, 96, 45],\n",
       " 'e': {'fruit': 'mango', 'vege': 'carrot'}}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': None, 4: None, 6: None}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.fromkeys(['a',4,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-67-7657742692bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'x'"
     ]
    }
   ],
   "source": [
    "d['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.get('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a', 'b', 'd', 'e'])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([23, 6.56, [85, 96, 45], {'fruit': 'mango', 'vege': 'carrot'}])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.56"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.pop('b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 23, 'd': [85, 96, 45], 'e': {'fruit': 'mango', 'vege': 'carrot'}}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.popitem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.update({2:45,'c':[788,990]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 23,\n",
       " 'd': [85, 96, 45],\n",
       " 'e': {'fruit': 'mango', 'vege': 'carrot'},\n",
       " 2: 45,\n",
       " 'c': [788, 990]}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi 21\n",
      "hi 23\n",
      "hi 25\n",
      "hi 27\n",
      "hi 29\n"
     ]
    }
   ],
   "source": [
    "for i in range(21,31,2):\n",
    "    print('hi',i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(l3)):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "58\n",
      "3.5\n",
      "5.7\n",
      "apple\n",
      "mango\n",
      "papaya\n",
      "58\n",
      "36\n",
      "41\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "for i in l3:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3.5\n",
      "1 5.7\n",
      "2 apple\n",
      "3 mango\n"
     ]
    }
   ],
   "source": [
    "for i,ele in enumerate(l3[3:7]):\n",
    "    print(i,ele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 58, 3.5, 5.7, 'apple', 'mango', 'papaya', 58, 36, 41, 12]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12,\n",
       " 36,\n",
       " 41,\n",
       " 58,\n",
       " 'papaya',\n",
       " 'mango',\n",
       " 'apple',\n",
       " 5.7,\n",
       " 3.5,\n",
       " 58,\n",
       " 4,\n",
       " 2,\n",
       " 'papaya',\n",
       " 'mango',\n",
       " 'apple',\n",
       " 3.5,\n",
       " 78945,\n",
       " 58,\n",
       " 4,\n",
       " 2]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 12\n",
      "4 36\n",
      "58 41\n",
      "3.5 58\n",
      "5.7 papaya\n",
      "apple mango\n",
      "mango apple\n",
      "papaya 5.7\n",
      "58 3.5\n",
      "36 58\n",
      "41 4\n",
      "12 2\n"
     ]
    }
   ],
   "source": [
    "for i,j in zip(l3,l):\n",
    "    print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "d\n",
      "e\n",
      "2\n",
      "c\n"
     ]
    }
   ],
   "source": [
    "for k in d.keys():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "[85, 96, 45]\n",
      "{'fruit': 'mango', 'vege': 'carrot'}\n",
      "45\n",
      "[788, 990]\n"
     ]
    }
   ],
   "source": [
    "for v in d.values():\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', 23)\n",
      "('d', [85, 96, 45])\n",
      "('e', {'fruit': 'mango', 'vege': 'carrot'})\n",
      "(2, 45)\n",
      "('c', [788, 990])\n"
     ]
    }
   ],
   "source": [
    "for k in d.items():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1. take two numerical lists of different length and find out common elements without duplicates \n",
    "\n",
    "a = [2,2,3,4,5,3,4,2]\n",
    "b = [2,3,3,4,5,4]\n",
    "\n",
    "2. create a new list with the elements less than 7 in the list a "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [2,2,3,4,5,3,4,2]\n",
    "b = [2,3,3,4,5,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = []\n",
    "for i in a:\n",
    "    for j in b:\n",
    "        if i==j and i not in c:\n",
    "            c.append(i)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 5]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 5]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = []\n",
    "for i in a:\n",
    "    if i in b and i not in c:\n",
    "            c.append(i)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = []\n",
    "for i in a:\n",
    "    if i <= 3:\n",
    "        d.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 3, 3, 2]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 3, 3, 2]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in a if i<=3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2, 3, 4, 5}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(a)&set(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.randint(100,999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cow => correct number and correct position\n",
    "bull => correct number and wrong position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-105-e52f8f3d08a4>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-105-e52f8f3d08a4>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    error handling\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "file handling\n",
    "functions\n",
    "oops\n",
    "visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'g' 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-106-3a98be72e994>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'g' is not defined"
     ]
    }
   ],
   "source": [
    "print(g)\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is an error!!\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(g)\n",
    "except:\n",
    "    print(\"There is an error!!\")\n",
    "print('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 'g' is not defined\n",
      "There is an error\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(g)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    print(\"There is an error\")\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'g' is an invalid keyword argument for this function",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-110-f94923099865>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'g' is an invalid keyword argument for this function"
     ]
    }
   ],
   "source": [
    "print(g=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'g' is an invalid keyword argument for this function\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(g=4)\n",
    "except (NameError,TypeError) as e:\n",
    "    print(e)\n",
    "print('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Test value error",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-116-6a0e9bcbe0d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test value error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: Test value error"
     ]
    }
   ],
   "source": [
    "raise ValueError(\"Test value error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(g)\n",
    "except:\n",
    "    pass\n",
    "print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(44)\n",
    "except:\n",
    "    print(\"error\")\n",
    "finally:\n",
    "    print(\"hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert a == 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install <library name>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(1,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.randint(1,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import factorial,log10,sin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factorial(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3010299956639812"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log10(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Deep_Learning.customLib import addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your name : ss\n",
      "ss\n"
     ]
    }
   ],
   "source": [
    "addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def displays(name):\n",
    "    print(\"Name : \",name)\n",
    "\n",
    "def show(name):\n",
    "    price = input(\"enter price : \")\n",
    "    print(\"Price : \",price)\n",
    "    print('Name : ',name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"pine apple\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name :  pine apple\n"
     ]
    }
   ],
   "source": [
    "displays(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter price : 12\n",
      "Price :  12\n",
      "Name :  pine apple\n"
     ]
    }
   ],
   "source": [
    "show(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class A:\n",
    "    def display(self,name):\n",
    "        self.name = name\n",
    "        print(\"Name : \",self.name)\n",
    "    \n",
    "    def show(self):\n",
    "        self.price = input(\"enter price : \")\n",
    "        print(\"Price : \",self.price)\n",
    "        print('Name : ',name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = A()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name :  papaya\n"
     ]
    }
   ],
   "source": [
    "a.display(\"papaya\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter price : 34\n",
      "Price :  34\n",
      "Name :  pine apple\n"
     ]
    }
   ],
   "source": [
    "a.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class B:\n",
    "    def display(self,name):\n",
    "        self.name = name\n",
    "        print(\"Name : \",self.name)\n",
    "    \n",
    "    def show(self):\n",
    "        self.price = input(\"enter price : \")\n",
    "        print(\"Price : \",self.price)\n",
    "        print('Name : ',self.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = B()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name :  kiwi\n"
     ]
    }
   ],
   "source": [
    "b.display('kiwi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter price : 85\n",
      "Price :  85\n",
      "Name :  kiwi\n"
     ]
    }
   ],
   "source": [
    "b.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class C:\n",
    "    def __init__(self,name):\n",
    "        self.name = name\n",
    "        self.price = input(\"enter price : \")\n",
    "        \n",
    "    def display(self):\n",
    "        print(\"Name : \",self.name)\n",
    "    \n",
    "    def show(self):\n",
    "        self.display()\n",
    "        print(\"Price : \",self.price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter price : 37\n"
     ]
    }
   ],
   "source": [
    "c = C('apple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'37'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name :  apple\n",
      "Price :  37\n"
     ]
    }
   ],
   "source": [
    "c.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "class D:\n",
    "    def __init__(self,name):\n",
    "        self._name = name\n",
    "        self._price = input(\"enter price : \")\n",
    "        \n",
    "    def display(self):\n",
    "        print(\"Name : \",self._name)\n",
    "    \n",
    "    def show(self):\n",
    "        self.display()\n",
    "        print(\"Price : \",self._price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter price : 78\n"
     ]
    }
   ],
   "source": [
    "d  = D('orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name :  orange\n",
      "Price :  78\n"
     ]
    }
   ],
   "source": [
    "d.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class parent:\n",
    "    def printDetails(self):\n",
    "        print(\"Name : \",self.name)\n",
    "        print(\"Company : \",self.company)\n",
    "    \n",
    "    def getDesignation(self):\n",
    "        self.designation = input(\"enter designation : \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class child(parent):\n",
    "    def __init__(self, name , company):\n",
    "        self.name = name\n",
    "        self.company = company\n",
    "    \n",
    "    def displayAll(self):\n",
    "        self.getDesignation()\n",
    "        self.printDetails()\n",
    "        print(\"Designation : \",self.designation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "child = child('aa','xyz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter designation : qwe\n",
      "Name :  aa\n",
      "Company :  xyz\n",
      "Designation :  qwe\n"
     ]
    }
   ],
   "source": [
    "child.displayAll()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class p:\n",
    "    def method(self):\n",
    "        print(\"I am from P\")\n",
    "\n",
    "class c(p):\n",
    "    def method(self):\n",
    "        print(\"I am from C\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = c()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am from C\n"
     ]
    }
   ],
   "source": [
    "p.method()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "csvFile = pd.read_csv(\"./train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>class</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            class  petal_length  petal_width  sepal_length  sepal_width\n",
       "0  Iris-virginica           5.5          1.8           6.4          3.1\n",
       "1  Iris-virginica           5.9          2.3           6.8          3.2\n",
       "2  Iris-virginica           5.4          2.3           6.2          3.4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csvFile.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsvFile = pd.read_csv(\"./fruits_vege.tsv\",sep='\\t',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>fruit</th>\n",
       "      <th>vegetable</th>\n",
       "      <th>fruits 2</th>\n",
       "      <th>vegetable 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>apple</td>\n",
       "      <td>onion</td>\n",
       "      <td>papaya</td>\n",
       "      <td>onion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>mango</td>\n",
       "      <td>potato</td>\n",
       "      <td>cherry</td>\n",
       "      <td>potato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>orange</td>\n",
       "      <td>beans</td>\n",
       "      <td>pine apple</td>\n",
       "      <td>carrot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit vegetable    fruits 2 vegetable 2\n",
       "a   apple     onion      papaya       onion\n",
       "b   mango    potato      cherry      potato\n",
       "c  orange     beans  pine apple      carrot"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsvFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "excelFile = pd.read_excel(\"./Course Syllabus.xlsx\", sheet_name=\"Machine Learning\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Syllabus</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.Introduction to Machine learning</td>\n",
       "      <td>60 mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.a  Introduction to Data Operation</td>\n",
       "      <td>150 mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Operations</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Moving Data Around</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Computing on Data</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Syllabus  Duration\n",
       "0   1.Introduction to Machine learning   60 mins\n",
       "1  1.a  Introduction to Data Operation  150 mins\n",
       "2                     Basic Operations       NaN\n",
       "3                   Moving Data Around       NaN\n",
       "4                    Computing on Data       NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excelFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF[\"Fruits\"] = [\"apple\",'mango','orange','papaya','kiwi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF['Vegetables'] = pd.Series(['carrot','onion','potato'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Fruits</th>\n",
       "      <th>Vegetables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>carrot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mango</td>\n",
       "      <td>onion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orange</td>\n",
       "      <td>potato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>papaya</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kiwi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fruits Vegetables\n",
       "0   apple     carrot\n",
       "1   mango      onion\n",
       "2  orange     potato\n",
       "3  papaya        NaN\n",
       "4    kiwi        NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF.to_csv(\"vanilla.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF.to_csv(\"vanilla.tsv\",sep=\"\\t\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF.to_excel(\"vanilla.xlsx\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile = open(\"vanilla.txt\",'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inFile.write(\"how do you do?\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile.writelines(\"i am good.\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "outFile = open('vanilla.txt','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hi, hello how are you?\\n', 'i am good.\\n', 'how do you do?\\n']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = outFile.readlines()\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hi, hello how are you?', 'i am good.', 'how do you do?']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out.splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hi, hello how are you?', 'i am good.', 'how do you do?']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outFile.read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,3,4,5,6,7]\n",
    "y = [1,4,9,16,25,36,49]\n",
    "y3 = [1,8,27,64,125,6**3,7**3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'rp',lw=2)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel('f(x)')\n",
    "plt.title(\"Simple graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'rp-',x,y3,'bv-')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel('f(x)')\n",
    "plt.title(\"Simple graph\")\n",
    "plt.legend(['x vs x^2','x vs x^3'],loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.plot(x,y,'rp-')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel('f(x)')\n",
    "plt.title(\"Simple graph\")\n",
    "plt.legend(['x vs x^2','x vs x^3'],loc='best')\n",
    "plt.figure(2)\n",
    "plt.plot(x,y3,'bv-')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel('f(x)')\n",
    "plt.title(\"Simple graph\")\n",
    "plt.legend(['x vs x^2','x vs x^3'],loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.plot(x,y,'rp-')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel('f(x)')\n",
    "plt.title(\"Simple graph\")\n",
    "plt.legend(['x vs x^2','x vs x^3'],loc='best')\n",
    "plt.subplot(122)\n",
    "plt.plot(x,y3,'bv-')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel('f(x)')\n",
    "plt.title(\"Simple graph\")\n",
    "plt.legend(['x vs x^2','x vs x^3'],loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "histList = ['a','a','b','c','c','b','b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(histList)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['apple','mango','orange'],[25,37,54],facecolor='white',edgecolor='red')\n",
    "plt.text('apple',35,'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie([25,37,54],labels=['apple','mango','orange'],labeldistance=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}