NAVTTC Program/Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5+8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### print Text "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bawa'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = \"Bawa\"\n",
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variable can't start with number,space special characters\n",
    "num1 = 0\n",
    "num2 = num1+10\n",
    "num2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-12284.285714285714"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3+5/7-8**3 * (4*6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bawa\n"
     ]
    }
   ],
   "source": [
    "# PODMAS --> parenthesis,Exponential power,Division,multiplication,addition, subtraction\n",
    "# ** --> power\n",
    "if name == \"Bawa\":\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_9288/27501904.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmarks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Enter your marks\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmarks\u001b[0m \u001b[1;33m>=\u001b[0m\u001b[1;36m90\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"A+\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmarks\u001b[0m \u001b[1;33m>=\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"A\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\ipykernel\\kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[1;34m(self, prompt)\u001b[0m\n\u001b[0;32m   1005\u001b[0m                 \u001b[1;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1006\u001b[0m             )\n\u001b[1;32m-> 1007\u001b[1;33m         return self._input_request(\n\u001b[0m\u001b[0;32m   1008\u001b[0m             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1009\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"shell\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\ipykernel\\kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[1;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[0;32m   1050\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1051\u001b[0m                 \u001b[1;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1052\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Interrupted by user\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1053\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1054\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid Message:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "marks = int(input(\"Enter your marks\"))\n",
    "if marks >=90:\n",
    "    print(\"A+\")\n",
    "elif marks >=80:\n",
    "    print(\"A\")\n",
    "elif marks >=70:\n",
    "    print(\"B\")\n",
    "elif marks >=60:\n",
    "    print(\"C\")\n",
    "else:\n",
    "    print(\"Fail\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAVTTC\n",
      "NAVTTC\n",
      "NAVTTC\n",
      "NAVTTC\n",
      "NAVTTC\n"
     ]
    }
   ],
   "source": [
    "# While\n",
    "i = 5\n",
    "while(i!=0):\n",
    "    print(\"NAVTTC\")\n",
    "    i -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail\n"
     ]
    }
   ],
   "source": [
    "a = input(\"Enter first number\")\n",
    "b=  input(\"Enter second number\")\n",
    "a = int(a)\n",
    "b = int(b)\n",
    "c = int(a+b)\n",
    "if (c == 3):\n",
    "    print(\"Yeah\")\n",
    "else:\n",
    "    print(\"Fail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n",
      "20\n",
      "22\n",
      "24\n",
      "26\n",
      "28\n",
      "30\n",
      "32\n",
      "34\n",
      "36\n",
      "38\n",
      "40\n",
      "42\n",
      "44\n",
      "46\n",
      "48\n",
      "50\n",
      "52\n",
      "54\n",
      "56\n",
      "58\n",
      "60\n",
      "62\n",
      "64\n",
      "66\n",
      "68\n",
      "70\n",
      "72\n",
      "74\n",
      "76\n",
      "78\n",
      "80\n",
      "82\n",
      "84\n",
      "86\n",
      "88\n",
      "90\n",
      "92\n",
      "94\n",
      "96\n",
      "98\n"
     ]
    }
   ],
   "source": [
    "i = int(0)\n",
    "while(i<100):\n",
    "    print(i)\n",
    "    i = int(i+2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = [2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010]\n",
    "type(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = [2,3,4,5,6,7,8,9,10,11,12]\n",
    "pop2 = [2.5,3.8,4.4,5.0,6,1.17,8,9.4,10.1,11.12,12.43]\n",
    "pop3 = [3.5, 4.8, 2.4, 6.0,1, 6.17, 12, 54.5, 11.1, 12.12, 13.43]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f5d47a6890>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(year,pop , linewidth=5) # Used fir plotting in background\n",
    " #Used for showing the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f5d4481e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6HuTLwFkRcWb1vX1ltY9ja3HB/2fpv7R4DHi0+rgc+BHgfuAJ4J+Bk6vHB/Dn9Nd69wCzi/Z1PfC16uO6tnpaq56Bq4EXF+3jUeDNbfe32v/Pi/Z5LSN8ELPhr+1Lqv3sAW4Hjm+7v1X+2l5H/wDnXvo/rD7adm8N9vxj9F9VfQc4WN0+sbrvcvpnsTxJ/1X1smrwrfSSVKjWD2JKkoZjgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RC/R/9BRyqkqNykwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(year,pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f5d582cf40>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(year, pop,linewidth=3, label='Population 1')\n",
    "\n",
    "plt.plot(year,pop2,label = 'Population 2')\n",
    "plt.scatter(year, pop3, label='Population 3')\n",
    "plt.xlabel('Independent var')\n",
    "plt.ylabel('Dependent var')\n",
    "plt.title('Graph')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [1,2,3,4,5]\n",
    "S1 = [20,40,60,80,100]\n",
    "S2 = [10,20,30,40,50]\n",
    "S3 = [10,30,50,70,90]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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clMdJA49v45PZk0vHK1Fnck2cOHGtu49vaVnWWwpm9gHgYuA0YA/wK+CzyX7e3RcBiwDGjx/vJSUlHcpRWVlJRz+bScqVGuVKXS5lW7BiCwufCVoK15x9mO+vD34llU06jS+V5EZLIZeOV6JM5YrjQvPfAq+5+y53PwT8GrgAGGxmjUVqGLC9tQ2ISPcwe/JIauZNbTKvZt5UnTqKURxF4c/AeWbWz4IOfiYBG4BVwLRwncuAR2PIJiIxKJs0gsJBeZRNGhF3lB4v60XB3Z8HlgIvAuvDDIuA64B/MbNtwBDgJ9nOJiLxmD15JCcNPF4thBwQy91H7n4zcHOz2X8CJsQQR0REQnqiWUREIioKIiISUVEQEZGIioKIiERUFEREJKKiICIiERUFERGJqCiIiEhERUFERCIqCiIiElFREBGRiIqCiIhEVBRERCTSZlEws95m9p/ZCiMiPVP9wXpe3fMq9Qfr447S47VZFNz9CPDJLGURkR5qde1qDhw5wFO1T8UdpcdLZjyFl8zsMYKxlN9tnOnuv85YKhHpEeZUzaGytpKDRw4yq/8sbnj6Bm557hZKhpUw/9Pz447XIyVTFPKA3cCFCfOcYGxlEZEOu2rsVWx+ezM7GnYA0LdXX4r6F3H12KtjTtZztVsU3H1mNoKISM9zyqBTKB1TypzVc+hlvTh09BClY0oZPmh43NF6rHaLgpnlAZcDZxG0GgBw93/OYC4R6SGerHmS/D75DO03lPz9+VTUVDCleErcsXqsZG5J/RnwQeAzQBUwDNAtAiKSFjNHzWTZJcsYkjeEZZcsY+YonZyIUzLXFD7i7v9oZhe7+2Iz+zmgWwREJC1GFYyKpgvyCyjIL4gxjSTTUjgU/txjZqOAE4CTMhdJRETikkxLYZGZfQD4DvAYMCCcFhGRbiaZovDf4UNsVcCHM5xHRERilMzpo9fMbJGZTTIzy3giERGJTTJF4Qzg90ApUGNm95iZur4QEemG2i0K7v6eu//S3f8eGAMMIjiVJCIi3UxSXWeb2afN7AfAWoIH2L6U0VQiIhKLZJ5orgFeAn4JXOvu77b9CRER6aqSuftotLvvzXgSERGJXTLXFFQQRER6CA3HKSIikXaLgpmdlsw8ERHp+pJpKTzcwryl6Q4iIiLxa/VCs5mdQTCGwglm9vcJiwaRMK6CiIh0H221FE4HLgIGA3+X8DoH+EZndmpmg81sqZltMrONZna+mZ1oZivMbGv48wOd2YdIXBas2EJd/QEWrNgSdxSRlLVaFNz90XAozovcfWbC69vu/mwn97sQ+J27nwF8DNgIzAVWuvsIYGX4XqTLWbhyKzv37mfhyq1xRxFJWTLPKWwzsxuA4sT1Ozocp5mdAHwKmBFu5yBw0MwuBkrC1RYDlcB1HdmHSBwWrNjyvkJQPPdxyiaNYPbkkTGlEkmNuXvbK5g9SzDS2lrgSON8d2/pAnT7OzQbAywCNhC0EtYCZcB2dx8crmPA243vm31+FjALoLCwcNySJUs6EoOGhgYGDBjQoc9mknKlJpdy1dUfYOfe/QAU5sPOfcH8wkF5nDTw+BiTNZVLxyyRcqWmM7kmTpy41t3Ht7jQ3dt8AdXtrZPKCxgPHAY+Hr5fCPw7sKfZem+3t61x48Z5R61atarDn80k5UpNLuW6s2Kzn3rdcj/1uuV+94O/iabvrNgcd7QmcumYJVKu1HQmF7DGW/m9mswtqcvN7PMdKkctqwVq3f358P1SgovXO82sCCD8WZfGfYpk3OzJI6mZN7XJvJp5U3XqSLqUZIpCGUFh2G9me82s3sw63PWFu78FvGFmp4ezJhGcSnoMuCycdxnwaEf3IRKnskkjKByUR9mkEXFHEUlZuxea3X1gBvZ7NVBuZscBfwJmEhSoX5rZ5cDrqHtu6aJmTx5JZeUOvlSiFoJ0Pcl0nW3AdOA0d/93MxsOFLn7Cx3dqbtXE1xbaG5SR7cpIiKdl8zpox8A5wNfDd83APdmLJGIiMQmmecUPu7u55jZSwDu/nZ42kdERLqZZFoKh8ysN+AAZjYUOJrRVCIiEotkisLdwCPASWZ2G/A08H8ymkpERGKRzN1H5Wa2luAisAFfdPeNGU8mIiJZ11bX2ScmvK0DfpG4zN3/mslgIiKSfW21FNYSXEcw4BTg7XB6MPBnQKOviYh0M211nX2au38Y+D3wd+5e4O5DCMZYqMhWQBERyZ5kLjSf5+5PNL5x998Cn8hcJBERiUsyzynsMLObgAfD99OBHZmLJCIicUmmpXApMJTgttRHgJPCeSIi0s0kc0vqXwl6ShURkW4umQ7xRgL/yvuH47wwc7FERCQOyVxT+BVwH/BjEobjFJGW1R+s59U9rzLu4DgGHpeJnuelx6t/C+o2QP2ZMLAwrZtO5prCYXf/obu/4O5rG19pTSHSjayuXc2BIwd4qvapuKNId1U1H44chKrvpX3TyRSFZWZ2pZkVmdmJja+0JxHp4uZUzWFC+QRufPpGAG54+gYmlE9gTtWcmJNJt1L/FlSXB9PV5VC/M62bT6YoXAZcCzxL8JTzWmBNWlOIdANXjb2Kov5F9O3VF4C+vfpS1L+Iq8deHXMy6Vaq5oOHHVX70bS3FtotCuGTzc1fH05rCpFu4JRBp1A6ppRDRw/Ry3px6OghSseUMnzQ8LijSXfR2Eo4cjB4f+Rg2lsL7RYFM+tnZjeZ2aLw/QgzuyhtCUS6kSdrniS/Tz5D+w0lv08+FTXqEUbSKLGV0CjNrYVkTh/9N3CQY11bbAf+I20JRLqRmaNmsuySZQzJG8KyS5Yxc9TMuCNJd7L5iWOthEZHDgbz0ySZW1L/xt2/bGaXArj7e2ZmaUsg0o2MKhgVTRfkF1CQXxBjGul2rtl0bLqyEi59J+27SKalcNDM8jk2HOffAAfSnkRERGKXTEvhZuB3wHAzKwcuAGZkMpSIiMQjmb6PVpjZi8B5BIPslLn7XzKeTEREsi6ZlgLAp4FPEpxC6kvQW6qIiHQzydyS+gPgCmA98DLwTTO7N9PBREQk+5JpKVwInOnujReaFwOvZDSViIjEIpm7j7YBpyS8Hx7OExGRbiaZlsJAYKOZvUBwTWECsMbMHgNw9y9kMJ+IiGRRMkXh3zKeQkREckIyt6RWmdmpwAh3/334IFsfd6/PfDwREcmmZO4++gawFPivcNYw4DcZzCQiIjFJ5kJzKcFTzHsB3H0rcFImQ4mISDySKQoH3D3qls/M+hD2gyQiIt1LMkWhysxuAPLNbDLwK2BZZmOJiEgckikKc4FdBE80fxN4Aripszs2s95m9pKZLQ/fn2Zmz5vZNjN7yMyO6+w+JD0WrNhCXf0BFqzYEncU6a7q34K6DWkfb1hSl8xwnEcJLixf6e7T3P1HjU83d1IZsDHh/feABe7+EeBt4PI07EPSYOHKrezcu5+FK7fGHUW6q6r5wWAxaR5vWFLXalGwwC1m9hdgM7DZzHaZWaefWzCzYcBU4MeN+yLoTmNpuMpi4Iud3Y90zoIVWyie+3iTecVzH1eLQdKrcdxhSPt4w5I6a+2PfjP7F+BzwCx3fy2c92Hgh8Dv3H1Bh3dqthS4neBp6X8lGJ/hD2ErATMbDvzW3Ue18NlZwCyAwsLCcUuWLOlQhoaGBgYMGNChz2ZSLuWqqz/Azr37ASjMh537gvmFg/I4aeDxMSY7JpeOV6JczQU5mO2dWnhvNw3HFzHgwJvQbwicMCzuVJGcO16hzuSaOHHiWncf39Kyth5e+zowOXHsBHf/k5l9DagAOlQUzOwioM7d15pZSaqfd/dFwCKA8ePHe0lJypsAoLKyko5+NpNyKdeCFVtY+Exwyuiasw/z/fXB/y5lk07jSyUj44wWyaXjlShXc0GOZat/CxZ+GQ7vp/L0WynZfDP0yYOydTCwMO50QI4drwSZytXWNYW+LQ2m4+67CMZU6KgLgC+YWQ2whOC00UJgcHi7KwQPyG3vxD4kDWZPHknNvKlN5tXMm8rsyblREKQbqJoPfrTpPD+qawsxaqsoHOzgsja5+/XuPszdi4GvAP/j7tOBVcC0cLXLgEc7ug9Jr7JJIygclEfZpBFxR5HuZvMTwQXmREcOBvMlFm2dPvqYme1tYb4BeRnIch2wxMz+A3gJ+EkG9iEdMHvySCord+TMKSPpRq7ZdGy6shIufSe2KBJotSi4e+9M79zdK4HKcPpPBN1yi4hITJJ5eE1ERHoIFQUREYmoKIiISERFQUREIioKIiISUVEQEZGIioKIiERUFEREJKKiICIiERUFERGJqCiIiEhERUFERCIqCiIiElFRkHbVH6zn1T2vUn+wPu4oXUP9W1C3QWMNS5ekoiDtWl27mgNHDvBU7VNxR+kaquYHA8Vo9DDpglQUpFVzquYwoXwCNz59IwA3PH0DE8onMKdqTszJclj9W1BdHkxXl6u1IF2OioK06qqxV1HUv4i+vYIhufv26ktR/yKuHnt1zMlyWOKYwxprWLogFQVp1SmDTqF0TCmHjh6il/Xi0NFDlI4pZfig4XFHy02NrYTGMYePHFRrQbocFQVp05M1T5LfJ5+h/YaS3yefipqKuCPlrsRWQiO1FqSLUVGQNs0cNZNllyxjSN4Qll2yjJmjZsYdKXdtfuJYK6HRkYPBfJEuok/cASS3jSoYFU0X5BdQkF8QY5ocd82mY9OVlXDpO7FFEekotRRERCSioiAiIhEVBRERiagoiIhIREVBREQiKgoiIhJRURARkYiKgoiIRFQUREQkoqIgIiIRFQUREYmoKIiISERFQUREIioKIiISyXpRMLPhZrbKzDaY2StmVhbOP9HMVpjZ1vDnBzKVYcGKLdTVH2DBii2Z2kX3Uv8W1G3QCGIiPUAcLYXDwDXu/lHgPKDUzD4KzAVWuvsIYGX4PiMWrtzKzr37Wbhya6Z20b1UzQ8Gi9EIYiLdXtaLgru/6e4vhtP1wEbgQ8DFwOJwtcXAF9O97wUrtlA89/Em84rnPq4WQ1saxx0GjTcs0gOYu8e3c7NiYDUwCvizuw8O5xvwduP7Zp+ZBcwCKCwsHLdkyZKk91dXf4Cde/cDUJgPO/cF8wsH5XHSwOM7/kXSqKGhgQEDBsQd45h3auG93TQcX8SAA29CvyFwwrC4U0Vy7niFcjUX5G425UpNZ3JNnDhxrbuPb2lZbEXBzAYAVcBt7v5rM9uTWATM7G13b/O6wvjx433NmjVJ73PBii3RKaNrzj7M99cHo5GWTRrB7MkjU/8SGVBZWUlJSUncMQL1b8HCj8Hh/VSefislm2+GPnlQtg4GFsadDsix45UgV3NB7mZTrtR0JpeZtVoUYrn7yMz6Ag8D5e7+63D2TjMrCpcXAXXp3u/sySOpmTe1ybyaeVNzpiDknKr54EebzvOjurYg0o3FcfeRAT8BNrr7nQmLHgMuC6cvAx7NVIaySSMoHJRH2aQRmdpF97D5ieACc6IjB4P5ItIt9YlhnxcAXwfWm1l1OO8GYB7wSzO7HHgd+FKmAsyePJLKyh18qUQthDZds+nYdGUlXPpObFFEJDuyXhTc/WnAWlk8KZtZRESkKT3RLCIiERUFERGJqCiIiEhERUFERCIqCiIiElFREBGRiIqCiIhEVBRERCSioiAiIhEVBRERiagoiIhIREVBREQiKgoiIhLpkUWh/q+v8uruDdT/9dW4o4iI5JQeWRRWr7qJA8BTq26KO4qISE7pUUVhTtUcJjx4LjfWrwfghvr1THjwXOZUzYk5mYhIbuhRReGqsVdR5L3p68H7vg5F3purx14dbzARkRzRo4rCKXYcpXU7OGTQC+eQQWndDobbcXFHExHJCT2qKFA1nyfzjyPfnaGHj5DvTkX+cVD1vbiTiYjkhJ5VFDY/wcw977DsjR0MOXqUZW/sYOaed2DzE3EnExHJCX3iDpBV12xiVON0ZSUFl75NQZx5RERyTM9qKYiISJtUFEREJKKiICIiERUFERGJqCiIiEjE3D3uDB1mZruA1zv48QLgL2mMky7KlRrlSl2uZlOu1HQm16nuPrSlBV26KHSGma1x9/Fx52hOuVKjXKnL1WzKlZpM5dLpIxERiagoiIhIpCcXhUVxB2iFcqVGuVKXq9mUKzUZydVjrymIiMj79eSWgoiINKOiICIikW5dFMzsfjOrM7OXW1luZna3mW0zs3Vmdk6O5Coxs3fMrDp8/VuWcg03s1VmtsHMXjGzshbWyfoxSzJX1o+ZmeWZ2Qtm9v/CXLe2sM7xZvZQeLyeN7PiHMk1w8x2JRyv/5XpXAn77m1mL5nZ8haWZf14JZkrzuNVY2brw/2uaWF5ev9Nunu3fQGfAs4BXm5l+eeB3wIGnAc8nyO5SoDlMRyvIuCccHogsAX4aNzHLMlcWT9m4TEYEE73BZ4Hzmu2zpXAfeH0V4CHciTXDOCebP8/Fu77X4Cft/TfK47jlWSuOI9XDVDQxvK0/pvs1i0Fd18N/LWNVS4GfuqBPwCDzawoB3LFwt3fdPcXw+l6YCPwoWarZf2YJZkr68Jj0BC+7Ru+mt+5cTGwOJxeCkwyM8uBXLEws2HAVODHrayS9eOVZK5cltZ/k926KCThQ8AbCe9ryYFfNqHzw+b/b83srGzvPGy2jyX4KzNRrMesjVwQwzELTzlUA3XACndv9Xi5+2HgHWBIDuQC+IfwdMNSMxue6Uyhu4A5wNFWlsdyvJLIBfEcLwgKeoWZrTWzWS0sT+u/yZ5eFHLViwR9k3wM+L/Ab7K5czMbADwM/G9335vNfbelnVyxHDN3P+LuY4BhwAQzG9XOR7IiiVzLgGJ3Hw2s4Nhf5xljZhcBde6+NtP7SkWSubJ+vBJ80t3PAT4HlJrZpzK5s55eFLYDiRV/WDgvVu6+t7H57+5PAH3NLCsjh5pZX4JfvOXu/usWVonlmLWXK85jFu5zD7AK+GyzRdHxMrM+wAnA7rhzuftudz8Qvv0xMC4LcS4AvmBmNcAS4EIze7DZOnEcr3ZzxXS8Gve9PfxZBzwCTGi2Slr/Tfb0ovAY8E/h1fvzgHfc/c24Q5nZBxvPo5rZBIL/Thn/RRLu8yfARne/s5XVsn7MkskVxzEzs6FmNjiczgcmA5uarfYYcFk4PQ34Hw+vDsaZq9k55y8QXKfJKHe/3t2HuXsxwUXk/3H3rzVbLevHK5lccRyvcL/9zWxg4zQwBWh+12Ja/0326XDaLsDMfkFwV0qBmdUCNxNcdMPd7wOeILhyvw14D5iZI7mmAd8ys8PAPuArmf6HEboA+DqwPjwfDXADcEpCtjiOWTK54jhmRcBiM+tNUIR+6e7Lzey7wBp3f4ygmP3MzLYR3FzwlQxnSjbXt83sC8DhMNeMLORqUQ4cr2RyxXW8CoFHwr93+gA/d/ffmdkVkJl/k+rmQkREIj399JGIiCRQURARkYiKgoiIRFQUREQkoqIgIiIRFQXpksysof21mqxf0lLvl9mSat5mn51hZienM49Ia1QURHLfDCDtRSF8YlikCRUF6dLCFkBl2EnZJjMrT3iy+bPhvBeBv0/4TH8LxrR4wYL+8y8O588ws0fD7W01s5sTPvO1cP1qM/uv8MEwzKzBzG4LO+L7g5kVhvNPM7PnLOgH/z+aZb7WzP4Ydq52aziv2Mw2mtmPLBgDocLM8s1sGjAeKA/3nZ+wnTPM7IWE98Vmtj6c/rdwHy+b2aKEY1JpZndZ0C//+8alEFFRkO5gLPC/gY8CHwYuMLM84EfA3xH0U/PBhPVvJOjKYAIwEbgj7EIAgn5l/gEYDfyjmY03szOBLwMXhJ3MHQGmh+v3B/4QdsS3GvhGOH8h8EN3PxuIuhwwsynAiHA/Y4BxdqyDsxHAve5+FrAH+Ad3XwqsAaa7+xh339e4LXffBBxnZqeFs74MPBRO3+Pu57r7KCAfuCjh+x/n7uPd/fttH1bpiVQUpDt4wd1r3f0oUA0UA2cAr7n71rC7i8QOzqYAc8MuMyqBPMIuMwi6md4d/vL9NfBJYBJBYflj+JlJBMUH4CDQeK1ibbhvCLrm+EU4/bNm+54CvETQs+sZBMWAMG91C9tqyy8JigE0LQoTLRi5bD1wIZDYlfhDiLRC5xSlOziQMH2E9v+/NoK/wjc3mWn2cd4/GI2H6y929+tb2NahhD6Wmu+7pT5kDLjd3f+r2b6LW/ge+bTvIeBXZvZrgvF1toatpB8A4939DTO7haDwNXo3ie1KD6WWgnRXm4BiM/ub8P2lCcueBK5OOM8+NmHZZDM7MTx3/0XgGWAlMM3MTgrXP9HMTm1n/89wrDO36QnznwT+2YKxITCzDzVutw31BMOQvo+7v0pQQL7DsRZAYwH4S7ifae1sXySiloJ0S+6+34JRqh43s/eApzj2i/XfCUbaWmdmvYDXOHbO/QWCcRuGAQ+6+xoAM7uJYPSrXsAhoBR4vY0IZcDPzew64NGEXBXhNYrnwprUAHyN4Bd7ax4A7jOzfcD5idcVQg8BdwCnhfvYY2Y/Iuhi+S3gj21sW6QJ9ZIqEjKzGQSnXK6KO4tIXHT6SEREImopiIhIRC0FERGJqCiIiEhERUFERCIqCiIiElFREBGRyP8Hf5TEmF9vQGIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, S1, linewidth=3, marker='+',label='Student 1')\n",
    "\n",
    "plt.scatter(X, S2, marker='^',label='Student 2')\n",
    "plt.scatter(X, S3, marker='*',label='Student 3')\n",
    "plt.xlabel('Independent var')\n",
    "plt.ylabel('Dependent var')\n",
    "plt.title('Graph')\n",
    "plt.legend()\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pop,bins=3)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HomeWork"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean, Median, Mode, SD, Variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.5\n"
     ]
    }
   ],
   "source": [
    "#mean\n",
    "import numpy as np\n",
    "l1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "x = np.mean(l1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.5\n"
     ]
    }
   ],
   "source": [
    "#Median\n",
    "import numpy as np\n",
    "l1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "x = np.median(l1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.7.3)\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scipy) (1.22.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install scipy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModeResult(mode=array([1]), count=array([1]))\n"
     ]
    }
   ],
   "source": [
    "#Mode\n",
    "from scipy import stats\n",
    "l1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "x = stats.mode(l1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8722813232690143\n"
     ]
    }
   ],
   "source": [
    "#Standard deviation\n",
    "import numpy as np\n",
    "l1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "x = np.std(l1)\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.166666666666666\n"
     ]
    }
   ],
   "source": [
    "#Variance\n",
    "import statistics as st\n",
    "l1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "x = st.variance(l1)\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Class work\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imran\n"
     ]
    }
   ],
   "source": [
    "print(input(\"Nalo budhayo bawa\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sahil Likes Basketball\n"
     ]
    }
   ],
   "source": [
    "#1\n",
    "a = input(\"Enter your name\")\n",
    "b = input(\"Enter your favorite spots\")\n",
    "print(a, \"Likes\" ,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.23606797749979\n"
     ]
    }
   ],
   "source": [
    "#2 distance between two points\n",
    "import math as m\n",
    "x2 = int(input(\"Enter x2\")) \n",
    "x1 = int(input(\"Enter x1\"))\n",
    "y2 = int(input(\"Enter y2\"))\n",
    "y1 = int(input(\"Enter y1\"))\n",
    "print(m.sqrt(((x2-x1)**2)+((y2-y1)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# addition of 2 numbers\n",
    "a = int(input(\"Enter first number\"))\n",
    "b = int(input(\"Enter second number\"))\n",
    "print(a+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sahil Ali\n",
      "str[0] S\n",
      "str[5]  \n",
      "str[7] l\n",
      "str[6:9] Ali\n"
     ]
    }
   ],
   "source": [
    "str = \"Sahil Ali\"\n",
    "print(str)\n",
    "print(\"str[0]\",str[0])\n",
    "print(\"str[5]\",str[5])\n",
    "print(\"str[7]\",str[7])\n",
    "print(\"str[6:9]\",str[6:9])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String methods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sahil\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'my_Str' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_10224/2321896408.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_Str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mmy_Str\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmy_Str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'my_Str' is not defined"
     ]
    }
   ],
   "source": [
    "my_Str = \"Sahil\"\n",
    "print(my_Str)\n",
    "del my_Str\n",
    "# del used to delete any string\n",
    "print(my_Str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SahilJatoi\n",
      "Sahil Jatoi\n"
     ]
    }
   ],
   "source": [
    "str1 = \"Sahil\"\n",
    "str2 = \"Jatoi\"\n",
    "#print(str1 +\" \"+ str2)\n",
    "print(str1 + str2)# + without built-in space\n",
    "print(str1 , str2)# , with built-in space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 Letters present\n"
     ]
    }
   ],
   "source": [
    "# Iterating\n",
    "count = 0\n",
    "for letter in 'Hello Wrold':\n",
    "    if(letter == 'l' or letter == 'L'):\n",
    "            count+= 1\n",
    "print(count, \"Letters present\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### String formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "He said, \"What's up?\"\n",
      "He said, \"What's up?\"\n",
      "He said, \"What's up?\"\n"
     ]
    }
   ],
   "source": [
    "# String formatting\n",
    "print('''He said, \"What's up?\"''')\n",
    "print(\"He said, \\\"What's up?\\\"\")\n",
    "print('He said, \"What\\'s up?\"')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sagar, Imran and Ali\n"
     ]
    }
   ],
   "source": [
    "# format method\n",
    "print(\"{}, {} and {}\".format('Sagar','Imran','Ali'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Big data, Small data\n"
     ]
    }
   ],
   "source": [
    "#f keyword\n",
    "s = \"data\"\n",
    "print(f\"Big {s}, Small {s}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sahil Jatoi is a programmer\n"
     ]
    }
   ],
   "source": [
    "fname = input(\"Enter your first name\")\n",
    "lname = input(\"Enter your last name\")\n",
    "print(f\"{fname} {lname} is a programmer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### String Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sahil\n",
      "sahil\n",
      "***********************************************Sahil************************************************\n",
      "1\n",
      "False\n",
      "0\n",
      "SAHIL\n",
      "sahil\n",
      "b'Sahil'\n",
      "0\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "<built-in method islower of str object at 0x0000029DD2888D70>\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "str = \"Sahil\"\n",
    "print(str.capitalize())\n",
    "print(str.casefold())\n",
    "print(str.center(100,'*'))\n",
    "print(str.count('S'))\n",
    "print(str.endswith(\" Ali\"))\n",
    "print(str.find('S'))\n",
    "print(str.upper())\n",
    "print(str.lower())\n",
    "print(str.encode())\n",
    "print(str.index('S'))\n",
    "print(str.isalnum())\n",
    "print(str.isalpha())\n",
    "print(str.isascii())\n",
    "print(str.isdecimal())\n",
    "print(str.islower)\n",
    "print(str.isidentifier())\n",
    "print(str.isprintable())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Guess word game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good Luck !  Sahil\n",
      "Guess the characters\n",
      "      \n",
      "a ['_', '_', '_', '_', '_', '_']\n",
      "     \n",
      "a ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "   \n",
      "Wrong\n",
      "You have 9 more guesses\n",
      "a ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "   \n",
      "a ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "e ['_', '_', '_', '_', '_', '_']\n",
      "  \n",
      "a ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "c ['_', '_', '_', '_', '_', '_']\n",
      "e ['_', '_', '_', '_', '_', '_']\n",
      "s ['_', '_', '_', '_', '_', '_']\n",
      "s ['_', '_', '_', '_', '_', '_']\n",
      "You Win\n",
      "The word is:  access\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "l = int(input(\"Enter number 6 or 10\"))\n",
    "name = input(\"What is your name? \")\n",
    "print(\"Good Luck ! \", name)\n",
    "lf6 = ['access', 'apple', 'accurate']\n",
    "lf10 = ['helloworld']\n",
    "l1 = ['_', '_', '_', '_', '_', '_']\n",
    "print(\"Guess the characters\")\n",
    "guesses = ''\n",
    "turns = 10\n",
    "while turns > 0:\n",
    "    failed = 0\n",
    "    if l == 10:\n",
    "        word = random.choice(lf10)\n",
    "    else:\n",
    "        word = random.choice(lf6)\n",
    "        for char in word:\n",
    "            if char in guesses:\n",
    "                print(char, l1)\n",
    "            else:\n",
    "                print(end=\" \")\n",
    "                failed += 1\n",
    "        if failed == 0:\n",
    "            print(\"You Win\")\n",
    "            print(\"The word is: \", word)\n",
    "            break\n",
    "        print()\n",
    "        guess = input(\"guess a character:\")\n",
    "        guesses += guess\n",
    "        if guess not in word:\n",
    "            turns -= 1\n",
    "            print(\"Wrong\")\n",
    "            print(\"You have\", + turns, 'more guesses')\n",
    "            if turns == 0:\n",
    "                print(\"You Loose\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ask user for his/her weight and then it is in pounds or kilograms then change into conventional term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrong input\n"
     ]
    }
   ],
   "source": [
    "weight = float(input(\"Please enter your weight\"))\n",
    "convertedWeight = 0.0\n",
    "a = input(\"Enter K if your weight is in kilogram or P if it is in pounds\")\n",
    "while(a == 'k' or a == 'K' or a == 'p' or a == 'P' or a != 'a' or a != 'A' or a != 'B' or a != 'b' or a != 'C' or a != 'c' or a != 'D' or a != 'd' or a != 'E' or a != 'e' or a != 'F' or a != 'f' or a != 'G' or a != 'g' or a != 'H' or a != 'h' or a != 'I' or a != 'i' or a != 'J' or a != 'j' or a != 'L' or a != 'l' or a != 'M' or a != 'm' or a != 'N' or a != 'n' or a != 'O' or a != 'o' or a != 'Q' or a != 'q' or a != 'R' or a != 'r' or a != 'S' or a != 's' or a != 'T' or a != 't' or a != 'U' or a != 'u' or a != 'V' or a != 'v' or a != 'W' or a != 'w' or a != 'X' or a != 'x' or a != 'Y' or a != 'y' or a != 'Z' or a != 'z'):\n",
    "    if (a == 'p' or a == 'P'):\n",
    "        convertedWeight = float(weight/2.2)\n",
    "        print(\"Weight is kilograms is \", convertedWeight, \"kg\")\n",
    "        break\n",
    "    elif (a == 'K' or a == 'k'):\n",
    "        convertedWeight = float(weight*2.2)\n",
    "        print(\"Weight is pounds is \", convertedWeight, \"lb\")\n",
    "        break\n",
    "    else:\n",
    "        print(\"Wrong input\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Even condition check "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "8\n",
      "10\n",
      "12\n",
      "13\n",
      "You entered an odd number\n"
     ]
    }
   ],
   "source": [
    "w = 0\n",
    "while(w%2 == 0):\n",
    "    w = int(input(\"Enter a number\"))\n",
    "    print(w)\n",
    "print(\"You entered an odd number\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Odd condition check "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "5\n",
      "7\n",
      "9\n",
      "11\n",
      "13\n",
      "15\n",
      "1\n",
      "2\n",
      "You entered an even number\n"
     ]
    }
   ],
   "source": [
    "w = 1\n",
    "while(w%2 != 0):\n",
    "    w = int(input(\"Enter a number\"))\n",
    "    print(w)\n",
    "print(\"You entered an even number\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Emoji converter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 🧜‍ \n"
     ]
    }
   ],
   "source": [
    "message = input(\"> \")\n",
    "words = message.split(\" \")\n",
    "emojis = {\n",
    "    \":)\": \"😀\",\n",
    "    \":(\": \"😞\",\n",
    "    \"lol\": \"😂\",\n",
    "    \"sick\": \"😨\",\n",
    "    \"happy\": \"😀\",\n",
    "    \"mermaid\": \"🧜‍\", \n",
    "    \"jalpari\": \"🧜‍\"\n",
    "}\n",
    "outcome = \" \"\n",
    "for word in words:\n",
    "   outcome += emojis.get(word, word) + \" \"\n",
    "print(outcome)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a list and reverse it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reversed List:  [5, 4, 3, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,4,5]\n",
    "l.reverse()\n",
    "print(\"Reversed List: \",l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a program to add two lists index-wise. Create a new list that contains the 0th index item from both the list, then the 1st index item, and so on till the last element. any leftover items will get added at the end of the new list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The First list is [1, 2, 3, 4, 5]\n",
      "The Second list is [6, 7, 8, 9, 10]\n",
      "The New addition of list is [(1, 6), (2, 7), (3, 8), (4, 9), (5, 10)]\n"
     ]
    }
   ],
   "source": [
    "l1 = [1, 2, 3, 4, 5]\n",
    "l2 = [6, 7, 8, 9, 10]\n",
    "print(f\"The First list is {l1}\")\n",
    "print(f\"The Second list is {l2}\")\n",
    "nl = zip(l1, l2)\n",
    "print(f\"The New addition of list is {list(nl)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Python program to create a tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 2, 3, 7, 5, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=(1,2,3,5,5,4)\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Python program to create a tuple with different data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, ['Samosa', 'Gulabjamon', 'Pakore'], 1, 'Big', 1.1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=(1,3,['Samosa','Gulabjamon','Pakore'],1,'Big',1.1)\n",
    "print(t)\n",
    "type(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Python program to create a tuple with numbers and print one item.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88\n"
     ]
    }
   ],
   "source": [
    "t = (44, 33, 77, 88, 11, 22, 44)\n",
    "print(t[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Python program to unpack a tuple in several variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Tuple is ('A', 'B', 1, 2.2)\n",
      "And The unpack values are: \n",
      "A\n",
      "B\n",
      "1\n",
      "2.2\n"
     ]
    }
   ],
   "source": [
    "a = ('A', 'B', 1, 2.2)\n",
    "print(f\"The Tuple is {a}\")\n",
    "# for unpack the tuple \n",
    "(alphabate1, alphabate2, number1, number2) = a\n",
    "# Printing the unpack values:\n",
    "print(\"And The unpack values are: \")\n",
    "print(alphabate1)\n",
    "print(alphabate2)\n",
    "print(number1)\n",
    "print(number2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Python program to add an item in a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are not suppose to add items in tuple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ask the user to type a four-digit number. And print it in words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Convert Number into text is :\n",
      "One Two Three Four "
     ]
    }
   ],
   "source": [
    "numb= input(\"Enter 4-dgiit number: Like(1234) :- \")\n",
    "num = [a for a in numb] \n",
    "MyDict={\n",
    " \"0\" : \"Zero\",\n",
    " \"1\" : \"One\",\n",
    " \"2\" : \"Two\",\n",
    " \"3\" : \"Three\",\n",
    " \"4\" : \"Four\"\n",
    "}\n",
    "print(\"The Convert Number into text is :\")\n",
    "for i in range(len(num)):\n",
    " print(MyDict.get(str(num[i]))+' ',end='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 5]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,4,5,6]\n",
    "l.append(5)\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4, 5, 6]\n",
    "l.insert(0,0)\n",
    "print(l)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,4,5,6]\n",
    "l.clear()\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4, 5, 6]\n",
    "print(l.count(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4, 5, 6]\n",
    "print(l.index(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4, 5, 6]\n",
    "l.pop(1) # remove with index\n",
    "print(l)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 6]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 4, 5, 6]\n",
    "l.remove(5) # remove with value\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuple Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "t = (1,2,3,4,5)\n",
    "print(t.count(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "t = (1, 2, 3, 4, 5)\n",
    "print(t.index(1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "s = {1,2,3,4,5}\n",
    "print(s.clear())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5}\n"
     ]
    }
   ],
   "source": [
    "s = {1, 2, 3, 4, 5}\n",
    "print(s.copy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5, 6}\n"
     ]
    }
   ],
   "source": [
    "s = {1, 2, 3, 4, 5}\n",
    "s.add(6)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Civil Engineer: QL', 'Doctor: SK', 'Carpenter: IK', 'Software Engineer: IS', 'Project Manager: SJ')\n"
     ]
    }
   ],
   "source": [
    "# Task 1 \n",
    "team = (\"Civil Engineer: QL\",\"Doctor: SK\",\"Carpenter: IK\",\"Software Engineer: IS\",\"Project Manager: SJ\")\n",
    "print(team)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before:  ['Toys', 'Clothes', 'JB', 'ABCD']\n",
      "After:  ['Toys', 'Clothes', 'Registers', 'Grocery']\n"
     ]
    }
   ],
   "source": [
    "# Task 2\n",
    "comp_prod = [\"Toys\",\"Clothes\",\"JB\",\"ABCD\"]\n",
    "print(\"Before: \", comp_prod)\n",
    "comp_prod.insert(2, \"Registers\")\n",
    "comp_prod.insert(3, \"Grocery\")\n",
    "comp_prod.remove(\"JB\")\n",
    "comp_prod.remove(\"ABCD\")\n",
    "print(\"After: \",comp_prod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SJ': (22, 0, 'Computer Systems', 0),\n",
       " 'SK': (22, 10, 'Computer Systems', 10),\n",
       " 'IS': (22, 8, 'Computer Systems', 9)}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 3\n",
    "dict =  {\"SJ\":(22,0,\"Computer Systems\",0),\"SK\":(22,10,\"Computer Systems\",10),\"IS\":(22,8,\"Computer Systems\",9)}\n",
    "dict.copy()\n",
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for data clearing\n",
    "\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>10350</td>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.           NaN  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38           NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine           NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.           NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"sales_data_sample.csv\", sep=\",\",\n",
    "                   encoding='Latin-1')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER            0\n",
       "QUANTITYORDERED        0\n",
       "PRICEEACH              0\n",
       "ORDERLINENUMBER        0\n",
       "SALES                  0\n",
       "ORDERDATE              0\n",
       "STATUS                 0\n",
       "QTR_ID                 0\n",
       "MONTH_ID               0\n",
       "YEAR_ID                0\n",
       "PRODUCTLINE            0\n",
       "MSRP                   0\n",
       "PRODUCTCODE            0\n",
       "CUSTOMERNAME           0\n",
       "PHONE                  0\n",
       "ADDRESSLINE1           0\n",
       "ADDRESSLINE2        2521\n",
       "CITY                   0\n",
       "STATE               1486\n",
       "POSTALCODE            76\n",
       "COUNTRY                0\n",
       "TERRITORY           1074\n",
       "CONTACTLASTNAME        0\n",
       "CONTACTFIRSTNAME       0\n",
       "DEALSIZE               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_clean=data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER         0\n",
       "QUANTITYORDERED     0\n",
       "PRICEEACH           0\n",
       "ORDERLINENUMBER     0\n",
       "SALES               0\n",
       "ORDERDATE           0\n",
       "STATUS              0\n",
       "QTR_ID              0\n",
       "MONTH_ID            0\n",
       "YEAR_ID             0\n",
       "PRODUCTLINE         0\n",
       "MSRP                0\n",
       "PRODUCTCODE         0\n",
       "CUSTOMERNAME        0\n",
       "PHONE               0\n",
       "ADDRESSLINE1        0\n",
       "ADDRESSLINE2        0\n",
       "CITY                0\n",
       "STATE               0\n",
       "POSTALCODE          0\n",
       "COUNTRY             0\n",
       "TERRITORY           0\n",
       "CONTACTLASTNAME     0\n",
       "CONTACTFIRSTNAME    0\n",
       "DEALSIZE            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are selected as a software engineer\n"
     ]
    }
   ],
   "source": [
    "#1.\n",
    "#GameXone offering a new position in their software house,\n",
    "#you are selected for an interview. The criteria for\n",
    "#selecting your position  either as a programmer or \n",
    "#management person is that if you are having 4-5 years \n",
    "#of experience in game development than you will be hired\n",
    "#as a Developer otherwise they will shortlist your for \n",
    "#management.\n",
    "\n",
    "experience = int(input(\"Enter the experience\"))\n",
    "if (experience >= 4 ):\n",
    "    print(\"You are selected as a software engineer\")\n",
    "else:\n",
    "    print(\"You are shortlisted for management\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arts\n"
     ]
    }
   ],
   "source": [
    "#2.\n",
    "#Design a career counseling system for students to select\n",
    "#their field. You are requested to think like a counsellor\n",
    "#who guide students to choose a career according to their\n",
    "#interest and passion. If their hobbies , or interest is \n",
    "#in arts, drawing , management , suggest them a field of \n",
    "#Arts , if they are logical, having expertise and interest \n",
    "#in programming , problem solving ,technologies than suggest\n",
    "#to pursue their career in Engineering domain .  \n",
    "#If they showed their interest in bio , science , discipline\n",
    "#and development of humanity than suggest them a medical\n",
    "#field.\n",
    "career_counseling_system = {\"Art\":\"Arts\",\"Drawing\":\"Arts\",\"Management\":\"Arts\",\"Logical\":\"Engineering\",\"Programing\":\"Software Engineering\",\"problem solving\":\"Software Engineering or Computer Systems\",\"technologies\":\"Computer Science\"}\n",
    "interest = input(\"Enter your interest\")\n",
    "print(career_counseling_system.get(interest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Addition = 4\n",
      "Subtract = 0\n",
      "Multiplication = 4\n",
      "Division = 1.0\n",
      "Floor Division = 1\n"
     ]
    }
   ],
   "source": [
    "#3.\n",
    "#Design a calculator that takes 2 values from user to \n",
    "#apply mathematical operations\n",
    "#Addition , \n",
    "#subtraction, \n",
    "#division, \n",
    "#multiplication and\n",
    "#floor division.\n",
    "f_n = int(input(\"Enter first number\"))\n",
    "s_n = int(input(\"Enter second number\"))\n",
    "print(\"Addition =\",f_n+s_n)\n",
    "print(\"Subtract =\",f_n-s_n)\n",
    "print(\"Multiplication =\",f_n*s_n)\n",
    "print(\"Division =\",f_n/s_n)\n",
    "print(\"Floor Division =\",f_n//s_n)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal\n"
     ]
    }
   ],
   "source": [
    "#4.\n",
    "# Takes 2 arguments from user and check whether they are equal or not\n",
    "f_a = input(\"Enter first argument\")\n",
    "s_a = input(\"Enter second argument\")\n",
    "if f_a == s_a or f_a.equals(s_a):\n",
    "    print(\"Equal\")\n",
    "else:\n",
    "    print(\"Not equal\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area is :  158.75\n"
     ]
    }
   ],
   "source": [
    "#5.\n",
    "# Design a system that Calculates the area of a plot.\n",
    "width = float(input(\"Enter Width\"))\n",
    "height = float(input(\"Enter Height\"))\n",
    "area = float(height * width)\n",
    "print(\"Area is : \", area)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLass Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
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      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n",
      "Liggi By Ritz\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "for i in range(0,1000):\n",
    "    print(\"Liggi By Ritz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n"
     ]
    }
   ],
   "source": [
    "#2\n",
    "a = 0\n",
    "for i in range(1,11):\n",
    "    a = a+i\n",
    "    #b = a+a\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "#3\n",
    "for i in range(1,21):\n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining products are: 95\n",
      "Remaining products are: 85\n",
      "Unable to fulfill your needs\n"
     ]
    }
   ],
   "source": [
    "#4\n",
    "products = 100\n",
    "while products != 0:\n",
    "    requirements = int(input((\"Enter Your requirement of number of products\")))\n",
    "    if (requirements <= products):\n",
    "        products = products - requirements\n",
    "        print(\"Remaining products are:\",products)\n",
    "    else:\n",
    "        print(\"Unable to fulfill your needs\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "#5\n",
    "a = int(input(\"Enter number of iterations from 0 to:\"))\n",
    "for i in range(a):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining products are: 95\n",
      "Remaining products are: 90\n",
      "Remaining products are: 85\n",
      "Remaining products are: 80\n",
      "Remaining products are: 75\n",
      "Remaining products are: 70\n",
      "Remaining products are: 65\n",
      "Remaining products are: 60\n",
      "Remaining products are: 55\n",
      "Remaining products are: 50\n",
      "Remaining products are: 45\n",
      "Remaining products are: 40\n",
      "Remaining products are: 35\n",
      "Remaining products are: 30\n",
      "Remaining products are: 25\n",
      "Remaining products are: 20\n",
      "Remaining products are: 15\n",
      "Remaining products are: 10\n",
      "Remaining products are: 5\n",
      "Remaining products are: 0\n"
     ]
    }
   ],
   "source": [
    "#1\n",
    "def Wending_Machine(requirements):\n",
    "    products = 100\n",
    "    while products != 0:\n",
    "        #requirements = int(input((\"Enter Your requirement of number of products\")))\n",
    "        if (requirements <= products):\n",
    "            products = products - requirements\n",
    "            print(\"Remaining products are:\", products)\n",
    "        else:\n",
    "            print(\"Unable to fulfill your needs\")\n",
    "            break\n",
    "\n",
    "Wending_Machine(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Won't be able to provide you a house in our building\n",
      "You may get house at floor 3\n",
      "You may get house at floor 1\n",
      "You may get house at floor 2\n",
      "Won't be able to provide you a house in our building\n"
     ]
    }
   ],
   "source": [
    "#2\n",
    "def House(rooms):\n",
    "    if (rooms == 2):\n",
    "        print(\"You may get house at floor 1\")\n",
    "    elif (rooms == 3):\n",
    "        print(\"You may get house at floor 2\")\n",
    "    elif (rooms == 4):\n",
    "        print(\"You may get house at floor 3\")\n",
    "    else:\n",
    "        print(\"Won't be able to provide you a house in our building\")\n",
    "\n",
    "House(1)\n",
    "House(4)\n",
    "House(2)\n",
    "House(3)\n",
    "House(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is present\n",
      "Data is not present\n",
      "Data is not present\n"
     ]
    }
   ],
   "source": [
    "#3\n",
    "def Record_Check(name,age,roll_number):\n",
    "    database = {\"SJ\":[\"Sahil\",22,14],\"IS\":[\"Imran\",22,20],\"SK\":[\"Sagar\",22,54]}\n",
    "    l = [name,age,roll_number] \n",
    "    for i in database:\n",
    "        if (l == database.get(i)):\n",
    "            print(\"Data is present\")\n",
    "        else:\n",
    "            print(\"Data is not present\")\n",
    "\n",
    "Record_Check(\"Sahil\",22,14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### args and kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NTS System\n",
      "Total Students are: 55\n"
     ]
    }
   ],
   "source": [
    "def total(*entry_test_student):\n",
    "    sum = 0\n",
    "    for i in entry_test_student:\n",
    "        sum = sum + i\n",
    "    print(\"NTS System\")\n",
    "    print(\"Total Students are:\",sum)\n",
    "total(1,2,3,4,5,6,7,8,9,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\n"
     ]
    }
   ],
   "source": [
    "def total(*argument):\n",
    "    print(argument)\n",
    "total(1,2,3,4,5,6,7,8,9,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': ['Sahil', 'Imran', 'Sagar'], 'age': [22, 22, 22], 'department': ['CS', 'CS', 'CS'], 'Report': ['TopLess', 'Topper', 'Topper']}\n"
     ]
    }
   ],
   "source": [
    "def SMS(**kwargs):\n",
    "    print(kwargs)\n",
    "SMS(name=[\"Sahil\",\"Imran\",\"Sagar\"],age=[22,22,22],department=[\"CS\",\"CS\",\"CS\"],Report=[\"TopLess\",\"Topper\",\"Topper\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oops Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Animal eat\n",
      "Animals walk\n"
     ]
    }
   ],
   "source": [
    "#1\n",
    "class animal():\n",
    "    def eat(self):\n",
    "        print(\"Animal eat\")\n",
    "    def walk(self):\n",
    "        print(\"Animals walk\")\n",
    "obj = animal()\n",
    "obj.eat()\n",
    "obj.walk()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['educational system', 'environment', 'light', 'gas', 'water']\n"
     ]
    }
   ],
   "source": [
    "#2\n",
    "class administration_system():\n",
    "    def __init__(self,management):\n",
    "        self.management = management\n",
    "        print(management)\n",
    "\n",
    "\n",
    "obj = administration_system([\"educational system\",\"environment\",\"light\",\"gas\",\"water\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congratulations, You have got the scolarship\n"
     ]
    }
   ],
   "source": [
    "#3\n",
    "class finantial_system():\n",
    "    def __init__(self, income,age):\n",
    "        self.income = income\n",
    "        self.age = age\n",
    "        if (age >= 18 and income <= 10000):\n",
    "            print(\"Congratulations, You have got the scolarship\")\n",
    "        else:\n",
    "            print(\"Sorry, but you are not eligible\")\n",
    "\n",
    "\n",
    "obj = finantial_system(10000,18)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### array in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('f', [2.200000047683716, 3.299999952316284, 4.400000095367432])\n"
     ]
    }
   ],
   "source": [
    "import array as ar\n",
    "arr = ar.array('f',[2.2,3.3,4.4])\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tasks\n",
    "#### 10 methods on array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('f', [2.200000047683716, 3.299999952316284, 4.400000095367432, 1.100000023841858])\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "import array as ar\n",
    "arr = ar.array('f', [2.2, 3.3, 4.4])\n",
    "arr.append(1.1)\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2\n",
    "import array as ar\n",
    "arr = ar.array('f', [2.2, 3.3, 4.4])\n",
    "# for length\n",
    "len(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('f', [2.200000047683716, 3.299999952316284, 4.400000095367432, 4.5, 6.300000190734863, 6.800000190734863])\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "import array as ar\n",
    "arr = ar.array('f', [2.2, 3.3, 4.4])\n",
    "arr.extend([4.5, 6.3, 6.8])\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('f', [2.200000047683716, 3.299999952316284, 3.799999952316284, 4.400000095367432])\n"
     ]
    }
   ],
   "source": [
    "# 4\n",
    "import array as ar\n",
    "arr = ar.array('f', [2.2, 3.3, 4.4])\n",
    "arr.insert(2, 3.8)\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array c =  array('d', [1.1, 2.1, 3.1, 2.6, 7.8, 3.7, 8.6])\n"
     ]
    }
   ],
   "source": [
    "# 5\n",
    "import array as arr\n",
    "a = arr.array('d', [1.1, 2.1, 3.1, 2.6, 7.8])\n",
    "b = arr.array('d', [3.7, 8.6])\n",
    "c = arr.array('d')\n",
    "c = a+b\n",
    "print(\"Array c = \", c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.6\n",
      "3.1\n"
     ]
    }
   ],
   "source": [
    "# 6\n",
    "import array as arr\n",
    "a = arr.array('d', [1.1, 2.2, 3.8, 3.1, 3.7, 1.2, 4.6])\n",
    "print(a.pop())\n",
    "print(a.pop(3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('d', [2.1, 3.1])\n"
     ]
    }
   ],
   "source": [
    "# 7\n",
    "import array as arr\n",
    "a = arr.array('d', [1.1, 2.1, 3.1])\n",
    "a.remove(1.1)\n",
    "print(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('d', [1.1, 2.1, 3.1])\n"
     ]
    }
   ],
   "source": [
    "# 8\n",
    "import array as arr\n",
    "a = arr.array('d', [1.1, 2.1, 3.1, 2.6, 7.8])\n",
    "print(a[0:3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All values\n",
      "1.1\n",
      "2.2\n",
      "3.8\n",
      "3.1\n",
      "3.7\n",
      "1.2\n",
      "4.6\n",
      "specific values\n",
      "2.2\n",
      "3.8\n"
     ]
    }
   ],
   "source": [
    "# 9\n",
    "import array as arr\n",
    "a = arr.array('d', [1.1, 2.2, 3.8, 3.1, 3.7, 1.2, 4.6])\n",
    "print(\"All values\")\n",
    "for x in a:\n",
    "    print(x)\n",
    "print(\"specific values\")\n",
    "for x in a[1:3]:\n",
    "    print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10 \n",
    "import array as arr\n",
    "a = arr.array('d', [1.1, 2.2, 3.8, 3.1, 3.7, 1.2, 4.6])\n",
    "len(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.array([[[1,2,3,4],[1,2,3,4]]])\n",
    "arr.ndim\n",
    "#print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tasks\n",
    "#### Methods of numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 2 3 4]\n",
      "  [1 2 3 4]]]\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "import numpy as np\n",
    "arr = np.array([[[1,2,3,4],[1,2,3,4]]])\n",
    "ar = arr.copy()\n",
    "print(ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# 2\n",
    "import numpy as np\n",
    "arr = np.array([[[1,2,3,4],[1,2,3,4]]])\n",
    "ar = arr.all()\n",
    "print(ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "import numpy as np\n",
    "arr = np.array([[[1,2,3,4],[1,2,3,4]]])\n",
    "ar = arr.any()\n",
    "print(ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4\n",
    "import numpy as np\n",
    "arr = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]]])\n",
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5\n",
    "import numpy as np\n",
    "arr = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]]])\n",
    "arr.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1],\n",
       "        [1]],\n",
       "\n",
       "       [[2],\n",
       "        [2]],\n",
       "\n",
       "       [[3],\n",
       "        [3]],\n",
       "\n",
       "       [[4],\n",
       "        [4]]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6\n",
    "import numpy as np\n",
    "arr = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]]])\n",
    "arr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[1, 2, 3, 4],\n",
       "         [1, 2, 3, 4]]])]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7\n",
    "import numpy as np\n",
    "arr = np.array([[[1,2,3,4],[1,2,3,4]]])\n",
    "np.split(arr, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3, 4],\n",
       "        [1, 2, 3, 4]],\n",
       "\n",
       "       [[5, 5, 5, 5],\n",
       "        [5, 5, 5, 5]]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8\n",
    "import numpy as np\n",
    "arr = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]]])\n",
    "np.insert(arr,1,5,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3, 4],\n",
       "        [1, 2, 3, 4]]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9\n",
    "import numpy as np\n",
    "arr = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]]])\n",
    "np.atleast_3d(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3, 4]],\n",
       "\n",
       "       [[1, 2, 3, 4]]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10\n",
    "import numpy as np\n",
    "arr = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]]])\n",
    "np.rot90(arr,1,(1,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>degree</th>\n",
       "      <th>fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>engr</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lumhs</td>\n",
       "      <td>doc</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sindh</td>\n",
       "      <td>gen</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni degree   fees\n",
       "0   muet   engr  30000\n",
       "1  lumhs    doc  32000\n",
       "2  sindh    gen  18000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary to list\n",
    "import pandas as pd\n",
    "dic = {'uni':['muet','lumhs','sindh'],'degree':['engr','doc','gen'],'fees':['30000','32000','18000']}\n",
    "d = pd.DataFrame(dic)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>degree</th>\n",
       "      <th>fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>engr</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lumhs</td>\n",
       "      <td>doc</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sindh</td>\n",
       "      <td>gen</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni degree   fees\n",
       "0   muet   engr  30000\n",
       "1  lumhs    doc  32000\n",
       "2  sindh    gen  18000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary to tuple\n",
    "import pandas as pd\n",
    "dic = {'uni': ('muet', 'lumhs', 'sindh'), 'degree': (\n",
    "    'engr', 'doc', 'gen'), 'fees': ('30000', '32000', '18000')}\n",
    "d = pd.DataFrame(dic)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>degree</th>\n",
       "      <th>fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>engr</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lumhs</td>\n",
       "      <td>doc</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sindh</td>\n",
       "      <td>gen</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni degree   fees\n",
       "0   muet   engr  30000\n",
       "1  lumhs    doc  32000\n",
       "2  sindh    gen  18000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List to dictionary\n",
    "import pandas as pd\n",
    "lis = [{'uni': 'muet', 'degree': 'engr', 'fees': '32000',\n",
    "        'uni': 'lumhs', 'degree': 'doc', 'fees': '30000', 'uni': 'sindh', 'degree': 'gen', 'fees': '18000'}]\n",
    "d = pd.DataFrame(dic)\n",
    "d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>degree_program</th>\n",
       "      <th>fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>lumhs</td>\n",
       "      <td>sindh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>engr</td>\n",
       "      <td>doc</td>\n",
       "      <td>gen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000</td>\n",
       "      <td>32000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni degree_program   fees\n",
       "0   muet          lumhs  sindh\n",
       "1   engr            doc    gen\n",
       "2  30000          32000  18000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list to list\n",
    "import pandas as pd\n",
    "dic = [['muet','lumhs','sindh'],['engr','doc','gen'],['30000','32000','18000']]\n",
    "d = pd.DataFrame(dic,columns=['uni','degree_program','fees'])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>degree_program</th>\n",
       "      <th>fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>lumhs</td>\n",
       "      <td>sindh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>engr</td>\n",
       "      <td>doc</td>\n",
       "      <td>gen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000</td>\n",
       "      <td>32000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni degree_program   fees\n",
       "0   muet          lumhs  sindh\n",
       "1   engr            doc    gen\n",
       "2  30000          32000  18000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tuple to tuple\n",
    "import pandas as pd\n",
    "dic = (('muet', 'lumhs', 'sindh'), (\n",
    "    'engr', 'doc', 'gen'), ('30000', '32000', '18000'))\n",
    "d = pd.DataFrame(dic, columns=['uni', 'degree_program', 'fees'])\n",
    "d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>degree_program</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>lumhs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>engr</td>\n",
       "      <td>doc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni degree_program\n",
       "0   muet          lumhs\n",
       "1   engr            doc\n",
       "2  30000          32000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t= d.iloc[:,0:2] # rows,columns\n",
    "# : --> all\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>uni</th>\n",
       "      <th>fees</th>\n",
       "      <th>degree_program</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>muet</td>\n",
       "      <td>sindh</td>\n",
       "      <td>lumhs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>engr</td>\n",
       "      <td>gen</td>\n",
       "      <td>doc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000</td>\n",
       "      <td>18000</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     uni   fees degree_program\n",
       "0   muet  sindh          lumhs\n",
       "1   engr    gen            doc\n",
       "2  30000  18000          32000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t= d.loc[[0,1,2],['uni','fees','degree_program']]\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make conversions examples "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary to List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbac8552",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Uni': ['LUMHS', 'MUET', 'Sindh'], 'Degree': ['Medical', 'Engineering', 'General'], 'Fees': [30000, 32000, 18000]}\n",
      "     Uni       Degree   Fees\n",
      "0  LUMHS      Medical  30000\n",
      "1   MUET  Engineering  32000\n",
      "2  Sindh      General  18000\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "import pandas as pd\n",
    "Dictionary = {\n",
    "    'Uni': ['LUMHS', 'MUET', 'Sindh'],\n",
    "    'Degree': ['Medical', 'Engineering', 'General'],\n",
    "    'Fees': [30000, 32000, 18000]\n",
    "}\n",
    "\n",
    "print(Dictionary)\n",
    "data=pd.DataFrame(Dictionary)\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec2d089b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Floor': ['1st', '2nd', '3rd'], 'Room': ['100', '106', '110'], 'Rent': [10000, 9000, 8000]}\n",
      "  Floor Room   Rent\n",
      "0   1st  100  10000\n",
      "1   2nd  106   9000\n",
      "2   3rd  110   8000\n"
     ]
    }
   ],
   "source": [
    "# 2 \n",
    "Dictionary1 = {\n",
    "    'Floor': ['1st', '2nd', '3rd'],\n",
    "    'Room': ['100', '106', '110'],\n",
    "    'Rent': [10000, 9000, 8000]\n",
    "}\n",
    "\n",
    "print(Dictionary1)\n",
    "data1=pd.DataFrame(Dictionary1)\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9882f44a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Hopital': ['Jamshoro', 'Hyderabad', 'Kotri'], 'Department': ['Cardiology', 'Nuerology', 'Dentist'], 'Patient': [50, 70, 30]}\n",
      "     Hopital  Department  Patient\n",
      "0   Jamshoro  Cardiology       50\n",
      "1  Hyderabad   Nuerology       70\n",
      "2      Kotri     Dentist       30\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "Dictionary2 = {\n",
    "    'Hopital': ['Jamshoro', 'Hyderabad', 'Kotri'],\n",
    "    'Department': ['Cardiology', 'Nuerology', 'Dentist'],\n",
    "    'Patient': [50, 70, 30]\n",
    "}\n",
    "\n",
    "print(Dictionary2)\n",
    "data2=pd.DataFrame(Dictionary2)\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3523a0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Business': ['Pharmaceutical', 'Bank', 'Restaurent'], 'Post': ['Field Manager', 'Bank Manger', 'Waiter'], 'salary': [40000, 60000, 15000]}\n",
      "         Business           Post  salary\n",
      "0  Pharmaceutical  Field Manager   40000\n",
      "1            Bank    Bank Manger   60000\n",
      "2      Restaurent         Waiter   15000\n"
     ]
    }
   ],
   "source": [
    "# 4\n",
    "Dictionary3 = {\n",
    "    'Business': ['Pharmaceutical', 'Bank', 'Restaurent'],\n",
    "    'Post': ['Field Manager', 'Bank Manger', 'Waiter'],\n",
    "    'salary': [40000, 60000, 15000]\n",
    "}\n",
    "\n",
    "print(Dictionary3)\n",
    "data3=pd.DataFrame(Dictionary3)\n",
    "print(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f2f8b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'School': ['Educators', 'Mehran', 'County'], 'Designation': ['Head Master', 'Teacher', 'Clerk'], 'salary': [150000, 60000, 30000]}\n",
      "      School  Designation  salary\n",
      "0  Educators  Head Master  150000\n",
      "1     Mehran      Teacher   60000\n",
      "2     County        Clerk   30000\n"
     ]
    }
   ],
   "source": [
    "# 5\n",
    "Dictionary4 = {\n",
    "    'School': ['Educators', 'Mehran', 'County'],\n",
    "    'Designation': ['Head Master', 'Teacher', 'Clerk'],\n",
    "    'salary': [150000, 60000, 30000]\n",
    "}\n",
    "print(Dictionary4)\n",
    "data4=pd.DataFrame(Dictionary4)\n",
    "print(data4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f88c7d",
   "metadata": {},
   "source": [
    "# Dictionary to tuple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e56169ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Uni': ('LUMHS', 'MUET', 'Sindh'), 'Degree': ('Medical', 'Engineering', 'General'), 'Fees': (30000, 32000, 18000)}\n",
      "     Uni       Degree   Fees\n",
      "0  LUMHS      Medical  30000\n",
      "1   MUET  Engineering  32000\n",
      "2  Sindh      General  18000\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "Dictionary = {\n",
    "    'Uni': ('LUMHS', 'MUET', 'Sindh'),\n",
    "    'Degree': ('Medical', 'Engineering', 'General'),\n",
    "    'Fees': (30000, 32000, 18000)\n",
    "}\n",
    "print(Dictionary)\n",
    "data=pd.DataFrame(Dictionary)\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb8cd01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Floor': ('1st', '2nd', '3rd'), 'Room': ('100', '106', '110'), 'Rent': (10000, 9000, 8000)}\n",
      "  Floor Room   Rent\n",
      "0   1st  100  10000\n",
      "1   2nd  106   9000\n",
      "2   3rd  110   8000\n"
     ]
    }
   ],
   "source": [
    "# 2 \n",
    "Dictionary1 = {\n",
    "    'Floor': ('1st', '2nd', '3rd'),\n",
    "    'Room': ('100', '106', '110'),\n",
    "    'Rent': (10000, 9000, 8000)\n",
    "}\n",
    "print(Dictionary1)\n",
    "data1=pd.DataFrame(Dictionary1)\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ed2ab1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Hopital': ('Jamshoro', 'Hyderabad', 'Kotri'), 'Department': ('Cardiology', 'Nuerology', 'Dentist'), 'Patient': (50, 70, 30)}\n",
      "     Hopital  Department  Patient\n",
      "0   Jamshoro  Cardiology       50\n",
      "1  Hyderabad   Nuerology       70\n",
      "2      Kotri     Dentist       30\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "Dictionary2 = {\n",
    "    'Hopital': ('Jamshoro', 'Hyderabad', 'Kotri'),\n",
    "    'Department': ('Cardiology', 'Nuerology', 'Dentist'),\n",
    "    'Patient': (50, 70, 30)\n",
    "}\n",
    "print(Dictionary2)\n",
    "data2=pd.DataFrame(Dictionary2)\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5567696b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Business': ('Pharmaceutical', 'Bank', 'Restaurent'), 'Post': ('Field Manager', 'Bank Manger', 'Waiter'), 'salary': (40000, 60000, 15000)}\n",
      "         Business           Post  salary\n",
      "0  Pharmaceutical  Field Manager   40000\n",
      "1            Bank    Bank Manger   60000\n",
      "2      Restaurent         Waiter   15000\n"
     ]
    }
   ],
   "source": [
    "# 4\n",
    "Dictionary3 = {\n",
    "    'Business': ('Pharmaceutical', 'Bank', 'Restaurent'),\n",
    "    'Post': ('Field Manager', 'Bank Manger', 'Waiter'),\n",
    "    'salary': (40000, 60000, 15000)\n",
    "}\n",
    "print(Dictionary3)\n",
    "data3=pd.DataFrame(Dictionary3)\n",
    "print(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "940e496c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'School': ('Educators', 'Mehran', 'County'), 'Designation': ('Head Master', 'Teacher', 'Clerk'), 'salary': (150000, 60000, 30000)}\n",
      "      School  Designation  salary\n",
      "0  Educators  Head Master  150000\n",
      "1     Mehran      Teacher   60000\n",
      "2     County        Clerk   30000\n"
     ]
    }
   ],
   "source": [
    "# 5\n",
    "Dictionary4 = {\n",
    "    'School': ('Educators', 'Mehran', 'County'),\n",
    "    'Designation': ('Head Master', 'Teacher', 'Clerk'),\n",
    "    'salary': (150000, 60000, 30000)\n",
    "}\n",
    "print(Dictionary4)\n",
    "data4=pd.DataFrame(Dictionary4)\n",
    "print(data4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81818f3",
   "metadata": {},
   "source": [
    "# List ot Dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5d46c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Floor': '1st', 'Room': '100', 'Rent': 10000}, {'Floor': '2nd', 'Room': '106', 'Rent': 9000}, {'Floor': '3rd', 'Room': '110', 'Rent': 8000}]\n",
      "  Floor Room   Rent\n",
      "0   1st  100  10000\n",
      "1   2nd  106   9000\n",
      "2   3rd  110   8000\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "list1=[{'Floor':'1st', 'Room': '100', 'Rent': 10000}, \n",
    "       {'Floor':'2nd', 'Room': '106', 'Rent': 9000}, \n",
    "       {'Floor':'3rd', 'Room': '110', 'Rent': 8000}]\n",
    "print(list1)\n",
    "data1=pd.DataFrame(list1)\n",
    "print(data1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "729fa07d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Uni': 'LUMHS', 'Degree': 'Medical', 'Fees': 32000}, {'Uni': 'MUET', 'Degree': 'Engineering', 'Fees': 35000}, {'Uni': 'Sindh', 'Degree': 'General', 'Fees': 45000}]\n",
      "     Uni       Degree   Fees\n",
      "0  LUMHS      Medical  32000\n",
      "1   MUET  Engineering  35000\n",
      "2  Sindh      General  45000\n"
     ]
    }
   ],
   "source": [
    "# 2\n",
    "list2=[{'Uni':'LUMHS', 'Degree': 'Medical', 'Fees':32000}, \n",
    "       {'Uni':'MUET', 'Degree': 'Engineering', 'Fees':35000}, \n",
    "       {'Uni':'Sindh', 'Degree': 'General', 'Fees':45000}]\n",
    "print(list2)\n",
    "data2=pd.DataFrame(list2)\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c810919",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Hospital': 'Jamshoro', 'Department': 'Cardiology', 'Patient': 50}, {'Hospital': 'Hyderabad', 'Department': 'Nuerology', 'Patient': 70}, {'Hospital': 'Kotri', 'Department': 'Dentist', 'Patient': 30}]\n",
      "    Hospital  Department  Patient\n",
      "0   Jamshoro  Cardiology       50\n",
      "1  Hyderabad   Nuerology       70\n",
      "2      Kotri     Dentist       30\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "list3=[{'Hospital':'Jamshoro', 'Department': 'Cardiology', 'Patient': 50}, \n",
    "       {'Hospital':'Hyderabad', 'Department': 'Nuerology', 'Patient': 70}, \n",
    "       {'Hospital':'Kotri', 'Department': 'Dentist', 'Patient': 30}]\n",
    "print(list3)\n",
    "data3=pd.DataFrame(list3)\n",
    "print(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09bdffbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Business': 'Pharmaceutical', 'Post': 'Field Manager', 'Salary': 40000}, {'Business': 'Bank', 'Post': 'Bank Manager', 'Salary': 60000}, {'Business': 'Restaurent', 'Post': 'Waiter', 'Salary': 15000}]\n",
      "         Business           Post  Salary\n",
      "0  Pharmaceutical  Field Manager   40000\n",
      "1            Bank   Bank Manager   60000\n",
      "2      Restaurent         Waiter   15000\n"
     ]
    }
   ],
   "source": [
    "# 4\n",
    "list4=[{'Business':'Pharmaceutical', 'Post': 'Field Manager', 'Salary': 40000}, \n",
    "       {'Business':'Bank', 'Post': 'Bank Manager', 'Salary': 60000}, \n",
    "       {'Business':'Restaurent', 'Post': 'Waiter', 'Salary': 15000}]\n",
    "print(list4)\n",
    "data4=pd.DataFrame(list4)\n",
    "print(data4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70413ce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'School': 'Educators', 'Designation': 'Head Master', 'Salary': 150000}, {'School': 'Mehran', 'Designation': 'Teacher', 'Salary': 60000}, {'School': 'Country', 'Designation': 'Clerk', 'Salary': 30000}]\n",
      "      School  Designation  Salary\n",
      "0  Educators  Head Master  150000\n",
      "1     Mehran      Teacher   60000\n",
      "2    Country        Clerk   30000\n"
     ]
    }
   ],
   "source": [
    "# 5\n",
    "list5=[{'School':'Educators', 'Designation': 'Head Master', 'Salary': 150000}, \n",
    "       {'School':'Mehran', 'Designation': 'Teacher', 'Salary': 60000}, \n",
    "       {'School':'Country', 'Designation': 'Clerk', 'Salary': 30000}]\n",
    "print(list5)\n",
    "data5=pd.DataFrame(list5)\n",
    "print(data5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d96ff2",
   "metadata": {},
   "source": [
    "# List ot List:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de9e3e67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Biryani  Samosa  Dahi  Pepsi  Raita\n",
      "0        9       7     6      3      1\n",
      "1        1       8     6      7      9\n",
      "2        8       7     5      3      2\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "list_to_list = [[9, 7, 6, 3, 1],[1, 8, 6, 7, 9],[8, 7, 5, 3, 2]]\n",
    "data =pd.DataFrame(list_to_list, columns=['Biryani', 'Samosa', 'Dahi', 'Pepsi', 'Raita'])\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4c3faf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   MIT  MSIM  DBMS  DSAA  FST\n",
      "0   92    42    94    93   38\n",
      "1   93    91    87    92   40\n",
      "2   87    94    85    93   42\n"
     ]
    }
   ],
   "source": [
    "# 2\n",
    "list_to_list1 = [[92, 42, 94, 93, 38], [93, 91, 87, 92, 40], [87, 94, 85, 93, 42]]\n",
    "data1 = pd.DataFrame(list_to_list1, columns=['MIT', 'MSIM', 'DBMS', 'DSAA', 'FST'])\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79d27422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sugar  Rice  Oil  Wheat  Chilles\n",
      "0     63    72  140     53      108\n",
      "1     93    91   87     92       40\n",
      "2     87    94   85     93       42\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "list_to_list2 = [[63, 72, 140, 53, 108], [93, 91, 87, 92, 40], [87, 94, 85, 93, 42]]\n",
    "data2 = pd.DataFrame(list_to_list2, columns=['Sugar', 'Rice', 'Oil', 'Wheat', 'Chilles'])\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4a9ee9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Strawberry  Apple  Banana  Orange  Mangoes\n",
      "0          63     72     140      53      108\n",
      "1          93     91      87      92       40\n",
      "2          87     94      85      93       42\n"
     ]
    }
   ],
   "source": [
    "# 4\n",
    "list_to_list3 = [[63, 72, 140, 53, 108], [93, 91, 87, 92, 40], [87, 94, 85, 93, 42]]\n",
    "data3 = pd.DataFrame(list_to_list3, columns=['Strawberry', 'Apple', 'Banana', 'Orange', 'Mangoes'])\n",
    "print(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc319c53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Milk  Dahi  Bread  Eggs  Ketchup\n",
      "0    63    72    140    53      108\n",
      "1    93    91     87    92       40\n",
      "2    87    94     85    93       42\n"
     ]
    }
   ],
   "source": [
    "# 5\n",
    "list_to_list2 = [[63, 72, 140, 53, 108], [93, 91, 87, 92, 40], [87, 94, 85, 93, 42]]\n",
    "data2 = pd.DataFrame(list_to_list2, columns=['Milk', 'Dahi', 'Bread', 'Eggs', 'Ketchup'])\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ab39d7",
   "metadata": {},
   "source": [
    "# Tuple to Tuple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58296125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Biryani  Samosa  Dahi  Pepsi  Raita\n",
      "0        9       7     6      3      1\n",
      "1        1       8     6      7      9\n",
      "2        8       7     5      3      2\n"
     ]
    }
   ],
   "source": [
    "tuple_to_tuple = ((9, 7, 6, 3, 1), (1, 8, 6, 7, 9), (8, 7, 5, 3, 2))\n",
    "data =pd.DataFrame(tuple_to_tuple, columns=['Biryani', 'Samosa', 'Dahi', 'Pepsi', 'Raita'])\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70af4f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   MIT  MSIM  DBMS  DSAA  FST\n",
      "0   92    42    94    93   38\n",
      "1   93    91    87    92   40\n",
      "2   87    94    85    93   42\n"
     ]
    }
   ],
   "source": [
    "# 2\n",
    "tuple1_to_tuple1 = ((92, 42, 94, 93, 38), (93, 91, 87, 92, 40), (87, 94, 85, 93, 42))\n",
    "data1 = pd.DataFrame(list_to_list1, columns=['MIT', 'MSIM', 'DBMS', 'DSAA', 'FST'])\n",
    "print(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6806b1c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sugar  Rice  Oil  Wheat  Chilles\n",
      "0     63    72  140     53      108\n",
      "1     93    91   87     92       40\n",
      "2     87    94   85     93       42\n"
     ]
    }
   ],
   "source": [
    "# 3\n",
    "tuple2_to_tuple2 = ((63, 72, 140, 53, 108), (93, 91, 87, 92, 40), (87, 94, 85, 93, 42))\n",
    "data2 = pd.DataFrame(list_to_list2, columns=['Sugar', 'Rice', 'Oil', 'Wheat', 'Chilles'])\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60e5b709",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Strawberry  Apple  Banana  Orange  Mangoes\n",
      "0          63     72     140      53      108\n",
      "1          93     91      87      92       40\n",
      "2          87     94      85      93       42\n"
     ]
    }
   ],
   "source": [
    "# 4\n",
    "tuple3_to_tuple3 = ((63, 72, 140, 53, 108), (93, 91, 87, 92, 40), (87, 94, 85, 93, 42))\n",
    "data3 = pd.DataFrame(list_to_list3, columns=['Strawberry', 'Apple', 'Banana', 'Orange', 'Mangoes'])\n",
    "print(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d73c60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Milk  Dahi  Bread  Eggs  Ketchup\n",
      "0    63    72    140    53      108\n",
      "1    93    91     87    92       40\n",
      "2    87    94     85    93       42\n"
     ]
    }
   ],
   "source": [
    "# 5\n",
    "tuple4_to_tuple4 = ((63, 72, 140, 53, 108), (93, 91, 87, 92, 40), (87, 94, 85, 93, 42))\n",
    "data2 = pd.DataFrame(list_to_list2, columns=['Milk', 'Dahi', 'Bread', 'Eggs', 'Ketchup'])\n",
    "print(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d76926",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Biryani  Samosa  Dahi  Pepsi  Raita\n",
      "0        9       7     6      3      1\n",
      "1        1       8     6      7      9\n",
      "2        8       7     5      3      2 \n",
      "\n",
      "   Biryani  Samosa\n",
      "0        9       7\n",
      "1        1       8\n",
      "2        8       7\n"
     ]
    }
   ],
   "source": [
    "# Slicing the Datafeame\n",
    "# LOC Method: \n",
    "tuple_to_tuple = ((9, 7, 6, 3, 1), (1, 8, 6, 7, 9), (8, 7, 5, 3, 2))\n",
    "data =pd.DataFrame(tuple_to_tuple, columns=['Biryani', 'Samosa', 'Dahi', 'Pepsi', 'Raita'])\n",
    "print(data, '\\n')\n",
    "sliced = data.iloc[:, 0:2]\n",
    "print(sliced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ffb8a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Biryani  Samosa  Dahi\n",
      "0        9       7     6\n",
      "2        8       7     5\n"
     ]
    }
   ],
   "source": [
    "locked = data.loc[[0, 2], [\"Biryani\",\"Samosa\",\"Dahi\"]]\n",
    "print(locked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions for EDA in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>10350</td>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.           NaN  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38           NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine           NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.           NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sales_data_sample.csv',sep=\",\", encoding='Latin-1')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER         2823\n",
       "QUANTITYORDERED     2823\n",
       "PRICEEACH           2823\n",
       "ORDERLINENUMBER     2823\n",
       "SALES               2823\n",
       "ORDERDATE           2823\n",
       "STATUS              2823\n",
       "QTR_ID              2823\n",
       "MONTH_ID            2823\n",
       "YEAR_ID             2823\n",
       "PRODUCTLINE         2823\n",
       "MSRP                2823\n",
       "PRODUCTCODE         2823\n",
       "CUSTOMERNAME        2823\n",
       "PHONE               2823\n",
       "ADDRESSLINE1        2823\n",
       "ADDRESSLINE2         302\n",
       "CITY                2823\n",
       "STATE               1337\n",
       "POSTALCODE          2747\n",
       "COUNTRY             2823\n",
       "TERRITORY           1749\n",
       "CONTACTLASTNAME     2823\n",
       "CONTACTFIRSTNAME    2823\n",
       "DEALSIZE            2823\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2823 entries, 0 to 2822\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ORDERNUMBER       2823 non-null   int64  \n",
      " 1   QUANTITYORDERED   2823 non-null   int64  \n",
      " 2   PRICEEACH         2823 non-null   float64\n",
      " 3   ORDERLINENUMBER   2823 non-null   int64  \n",
      " 4   SALES             2823 non-null   float64\n",
      " 5   ORDERDATE         2823 non-null   object \n",
      " 6   STATUS            2823 non-null   object \n",
      " 7   QTR_ID            2823 non-null   int64  \n",
      " 8   MONTH_ID          2823 non-null   int64  \n",
      " 9   YEAR_ID           2823 non-null   int64  \n",
      " 10  PRODUCTLINE       2823 non-null   object \n",
      " 11  MSRP              2823 non-null   int64  \n",
      " 12  PRODUCTCODE       2823 non-null   object \n",
      " 13  CUSTOMERNAME      2823 non-null   object \n",
      " 14  PHONE             2823 non-null   object \n",
      " 15  ADDRESSLINE1      2823 non-null   object \n",
      " 16  ADDRESSLINE2      302 non-null    object \n",
      " 17  CITY              2823 non-null   object \n",
      " 18  STATE             1337 non-null   object \n",
      " 19  POSTALCODE        2747 non-null   object \n",
      " 20  COUNTRY           2823 non-null   object \n",
      " 21  TERRITORY         1749 non-null   object \n",
      " 22  CONTACTLASTNAME   2823 non-null   object \n",
      " 23  CONTACTFIRSTNAME  2823 non-null   object \n",
      " 24  DEALSIZE          2823 non-null   object \n",
      "dtypes: float64(2), int64(7), object(16)\n",
      "memory usage: 551.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>10165</td>\n",
       "      <td>31</td>\n",
       "      <td>71.10</td>\n",
       "      <td>18</td>\n",
       "      <td>2204.10</td>\n",
       "      <td>10/22/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>Bronz Sok., Bronz Apt. 3/6 Tesvikiye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79903</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Natividad</td>\n",
       "      <td>Eric</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>10386</td>\n",
       "      <td>21</td>\n",
       "      <td>74.77</td>\n",
       "      <td>18</td>\n",
       "      <td>1570.17</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>10159</td>\n",
       "      <td>42</td>\n",
       "      <td>51.48</td>\n",
       "      <td>18</td>\n",
       "      <td>2162.16</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>10106</td>\n",
       "      <td>41</td>\n",
       "      <td>83.44</td>\n",
       "      <td>18</td>\n",
       "      <td>3421.04</td>\n",
       "      <td>2/17/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>Via Ludovico il Moro 22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bergamo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24100</td>\n",
       "      <td>Italy</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Rovelli</td>\n",
       "      <td>Giovanni</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>10398</td>\n",
       "      <td>28</td>\n",
       "      <td>72.26</td>\n",
       "      <td>18</td>\n",
       "      <td>2023.28</td>\n",
       "      <td>3/30/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0          10107               30      95.70                2  2871.00   \n",
       "1          10121               34      81.35                5  2765.90   \n",
       "2          10134               41      94.74                2  3884.34   \n",
       "3          10145               45      83.26                6  3746.70   \n",
       "4          10159               49     100.00               14  5205.27   \n",
       "..           ...              ...        ...              ...      ...   \n",
       "758        10165               31      71.10               18  2204.10   \n",
       "800        10386               21      74.77               18  1570.17   \n",
       "808        10159               42      51.48               18  2162.16   \n",
       "935        10106               41      83.44               18  3421.04   \n",
       "958        10398               28      72.26               18  2023.28   \n",
       "\n",
       "           ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0     2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1      5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2      7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3     8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4    10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "..               ...       ...     ...       ...      ...  ...   \n",
       "758  10/22/2003 0:00   Shipped       4        10     2003  ...   \n",
       "800    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "808  10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "935   2/17/2003 0:00   Shipped       1         2     2003  ...   \n",
       "958   3/30/2005 0:00   Shipped       1         3     2005  ...   \n",
       "\n",
       "                             ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0                 897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1                      59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2           27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3                      78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                         7734 Strong St.           NaN  San Francisco    CA   \n",
       "..                                    ...           ...            ...   ...   \n",
       "758  Bronz Sok., Bronz Apt. 3/6 Tesvikiye           NaN      Singapore   NaN   \n",
       "800                    C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "808                       7734 Strong St.           NaN  San Francisco    CA   \n",
       "935               Via Ludovico il Moro 22           NaN        Bergamo   NaN   \n",
       "958                    59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "\n",
       "    POSTALCODE    COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0        10022        USA       NaN              Yu             Kwai    Small  \n",
       "1        51100     France      EMEA         Henriot             Paul    Small  \n",
       "2        75508     France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3        90003        USA       NaN           Young            Julie   Medium  \n",
       "4          NaN        USA       NaN           Brown            Julie   Medium  \n",
       "..         ...        ...       ...             ...              ...      ...  \n",
       "758      79903  Singapore     Japan       Natividad             Eric    Small  \n",
       "800      28034      Spain      EMEA          Freyre            Diego    Small  \n",
       "808        NaN        USA       NaN           Brown            Julie    Small  \n",
       "935      24100      Italy      EMEA         Rovelli         Giovanni   Medium  \n",
       "958      51100     France      EMEA         Henriot             Paul    Small  \n",
       "\n",
       "[90 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gk = df.groupby('ORDERLINENUMBER')\n",
    "gk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER           int64\n",
       "QUANTITYORDERED       int64\n",
       "PRICEEACH           float64\n",
       "ORDERLINENUMBER       int64\n",
       "SALES               float64\n",
       "ORDERDATE            object\n",
       "STATUS               object\n",
       "QTR_ID                int64\n",
       "MONTH_ID              int64\n",
       "YEAR_ID               int64\n",
       "PRODUCTLINE          object\n",
       "MSRP                  int64\n",
       "PRODUCTCODE          object\n",
       "CUSTOMERNAME         object\n",
       "PHONE                object\n",
       "ADDRESSLINE1         object\n",
       "ADDRESSLINE2         object\n",
       "CITY                 object\n",
       "STATE                object\n",
       "POSTALCODE           object\n",
       "COUNTRY              object\n",
       "TERRITORY            object\n",
       "CONTACTLASTNAME      object\n",
       "CONTACTFIRSTNAME     object\n",
       "DEALSIZE             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2823, 25)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70575"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ORDERNUMBER', 'QUANTITYORDERED', 'PRICEEACH', 'ORDERLINENUMBER',\n",
       "       'SALES', 'ORDERDATE', 'STATUS', 'QTR_ID', 'MONTH_ID', 'YEAR_ID',\n",
       "       'PRODUCTLINE', 'MSRP', 'PRODUCTCODE', 'CUSTOMERNAME', 'PHONE',\n",
       "       'ADDRESSLINE1', 'ADDRESSLINE2', 'CITY', 'STATE', 'POSTALCODE',\n",
       "       'COUNTRY', 'TERRITORY', 'CONTACTLASTNAME', 'CONTACTFIRSTNAME',\n",
       "       'DEALSIZE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>PRODUCTLINE</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10107</th>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10121</th>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10134</th>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10145</th>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10159</th>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10350</th>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10373</th>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10386</th>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10397</th>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10414</th>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "ORDERNUMBER                                                         \n",
       "10107                     30      95.70                2  2871.00   \n",
       "10121                     34      81.35                5  2765.90   \n",
       "10134                     41      94.74                2  3884.34   \n",
       "10145                     45      83.26                6  3746.70   \n",
       "10159                     49     100.00               14  5205.27   \n",
       "...                      ...        ...              ...      ...   \n",
       "10350                     20     100.00               15  2244.40   \n",
       "10373                     29     100.00                1  3978.51   \n",
       "10386                     43     100.00                4  5417.57   \n",
       "10397                     34      62.24                1  2116.16   \n",
       "10414                     47      65.52                9  3079.44   \n",
       "\n",
       "                   ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  \\\n",
       "ORDERNUMBER                                                         \n",
       "10107         2/24/2003 0:00   Shipped       1         2     2003   \n",
       "10121          5/7/2003 0:00   Shipped       2         5     2003   \n",
       "10134          7/1/2003 0:00   Shipped       3         7     2003   \n",
       "10145         8/25/2003 0:00   Shipped       3         8     2003   \n",
       "10159        10/10/2003 0:00   Shipped       4        10     2003   \n",
       "...                      ...       ...     ...       ...      ...   \n",
       "10350         12/2/2004 0:00   Shipped       4        12     2004   \n",
       "10373         1/31/2005 0:00   Shipped       1         1     2005   \n",
       "10386          3/1/2005 0:00  Resolved       1         3     2005   \n",
       "10397         3/28/2005 0:00   Shipped       1         3     2005   \n",
       "10414          5/6/2005 0:00   On Hold       2         5     2005   \n",
       "\n",
       "             PRODUCTLINE  ...                   ADDRESSLINE1 ADDRESSLINE2  \\\n",
       "ORDERNUMBER               ...                                               \n",
       "10107        Motorcycles  ...        897 Long Airport Avenue          NaN   \n",
       "10121        Motorcycles  ...             59 rue de l'Abbaye          NaN   \n",
       "10134        Motorcycles  ...  27 rue du Colonel Pierre Avia          NaN   \n",
       "10145        Motorcycles  ...             78934 Hillside Dr.          NaN   \n",
       "10159        Motorcycles  ...                7734 Strong St.          NaN   \n",
       "...                  ...  ...                            ...          ...   \n",
       "10350              Ships  ...             C/ Moralzarzal, 86          NaN   \n",
       "10373              Ships  ...                    Torikatu 38          NaN   \n",
       "10386              Ships  ...             C/ Moralzarzal, 86          NaN   \n",
       "10397              Ships  ...          1 rue Alsace-Lorraine          NaN   \n",
       "10414              Ships  ...             8616 Spinnaker Dr.          NaN   \n",
       "\n",
       "                      CITY STATE POSTALCODE  COUNTRY TERRITORY  \\\n",
       "ORDERNUMBER                                                      \n",
       "10107                  NYC    NY      10022      USA       NaN   \n",
       "10121                Reims   NaN      51100   France      EMEA   \n",
       "10134                Paris   NaN      75508   France      EMEA   \n",
       "10145             Pasadena    CA      90003      USA       NaN   \n",
       "10159        San Francisco    CA        NaN      USA       NaN   \n",
       "...                    ...   ...        ...      ...       ...   \n",
       "10350               Madrid   NaN      28034    Spain      EMEA   \n",
       "10373                 Oulu   NaN      90110  Finland      EMEA   \n",
       "10386               Madrid   NaN      28034    Spain      EMEA   \n",
       "10397             Toulouse   NaN      31000   France      EMEA   \n",
       "10414               Boston    MA      51003      USA       NaN   \n",
       "\n",
       "            CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "ORDERNUMBER                                            \n",
       "10107                    Yu             Kwai    Small  \n",
       "10121               Henriot             Paul    Small  \n",
       "10134              Da Cunha           Daniel   Medium  \n",
       "10145                 Young            Julie   Medium  \n",
       "10159                 Brown            Julie   Medium  \n",
       "...                     ...              ...      ...  \n",
       "10350                Freyre            Diego    Small  \n",
       "10373             Koskitalo           Pirkko   Medium  \n",
       "10386                Freyre            Diego   Medium  \n",
       "10397                Roulet          Annette    Small  \n",
       "10414               Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 24 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('ORDERNUMBER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>PRODUCTLINE</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES        ORDERDATE  \\\n",
       "0                  30      95.70                2  2871.00   2/24/2003 0:00   \n",
       "1                  34      81.35                5  2765.90    5/7/2003 0:00   \n",
       "2                  41      94.74                2  3884.34    7/1/2003 0:00   \n",
       "3                  45      83.26                6  3746.70   8/25/2003 0:00   \n",
       "4                  49     100.00               14  5205.27  10/10/2003 0:00   \n",
       "...               ...        ...              ...      ...              ...   \n",
       "2818               20     100.00               15  2244.40   12/2/2004 0:00   \n",
       "2819               29     100.00                1  3978.51   1/31/2005 0:00   \n",
       "2820               43     100.00                4  5417.57    3/1/2005 0:00   \n",
       "2821               34      62.24                1  2116.16   3/28/2005 0:00   \n",
       "2822               47      65.52                9  3079.44    5/6/2005 0:00   \n",
       "\n",
       "        STATUS  QTR_ID  MONTH_ID  YEAR_ID  PRODUCTLINE  ...  \\\n",
       "0      Shipped       1         2     2003  Motorcycles  ...   \n",
       "1      Shipped       2         5     2003  Motorcycles  ...   \n",
       "2      Shipped       3         7     2003  Motorcycles  ...   \n",
       "3      Shipped       3         8     2003  Motorcycles  ...   \n",
       "4      Shipped       4        10     2003  Motorcycles  ...   \n",
       "...        ...     ...       ...      ...          ...  ...   \n",
       "2818   Shipped       4        12     2004        Ships  ...   \n",
       "2819   Shipped       1         1     2005        Ships  ...   \n",
       "2820  Resolved       1         3     2005        Ships  ...   \n",
       "2821   Shipped       1         3     2005        Ships  ...   \n",
       "2822   On Hold       2         5     2005        Ships  ...   \n",
       "\n",
       "                       ADDRESSLINE1 ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue          NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye          NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia          NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.          NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.          NaN  San Francisco    CA   \n",
       "...                             ...          ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86          NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38          NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86          NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine          NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.          NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['ORDERNUMBER'],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inplace \n",
    "df.drop('ORDERLINENUMBER',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>PRODUCTLINE</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
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       "      <td>2003</td>\n",
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       "      <td>95</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
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       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
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       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>Ships</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>Ships</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      QUANTITYORDERED  PRICEEACH    SALES        ORDERDATE    STATUS  QTR_ID  \\\n",
       "0                  30      95.70  2871.00   2/24/2003 0:00   Shipped       1   \n",
       "1                  34      81.35  2765.90    5/7/2003 0:00   Shipped       2   \n",
       "2                  41      94.74  3884.34    7/1/2003 0:00   Shipped       3   \n",
       "3                  45      83.26  3746.70   8/25/2003 0:00   Shipped       3   \n",
       "4                  49     100.00  5205.27  10/10/2003 0:00   Shipped       4   \n",
       "...               ...        ...      ...              ...       ...     ...   \n",
       "2818               20     100.00  2244.40   12/2/2004 0:00   Shipped       4   \n",
       "2819               29     100.00  3978.51   1/31/2005 0:00   Shipped       1   \n",
       "2820               43     100.00  5417.57    3/1/2005 0:00  Resolved       1   \n",
       "2821               34      62.24  2116.16   3/28/2005 0:00   Shipped       1   \n",
       "2822               47      65.52  3079.44    5/6/2005 0:00   On Hold       2   \n",
       "\n",
       "      MONTH_ID  YEAR_ID  PRODUCTLINE  MSRP  ...  \\\n",
       "0            2     2003  Motorcycles    95  ...   \n",
       "1            5     2003  Motorcycles    95  ...   \n",
       "2            7     2003  Motorcycles    95  ...   \n",
       "3            8     2003  Motorcycles    95  ...   \n",
       "4           10     2003  Motorcycles    95  ...   \n",
       "...        ...      ...          ...   ...  ...   \n",
       "2818        12     2004        Ships    54  ...   \n",
       "2819         1     2005        Ships    54  ...   \n",
       "2820         3     2005        Ships    54  ...   \n",
       "2821         3     2005        Ships    54  ...   \n",
       "2822         5     2005        Ships    54  ...   \n",
       "\n",
       "                       ADDRESSLINE1 ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue          NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye          NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia          NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.          NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.          NaN  San Francisco    CA   \n",
       "...                             ...          ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86          NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38          NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86          NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine          NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.          NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 23 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   1  2  3  4  5\n",
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    "t = ('1','2','3','4','5')\n",
    "t = pd.get_dummies(t)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Classic Cars  Motorcycles  Planes  Ships  Trains  Trucks and Buses  \\\n",
       "0                0            1       0      0       0                 0   \n",
       "1                0            1       0      0       0                 0   \n",
       "2                0            1       0      0       0                 0   \n",
       "3                0            1       0      0       0                 0   \n",
       "4                0            1       0      0       0                 0   \n",
       "...            ...          ...     ...    ...     ...               ...   \n",
       "2818             0            0       0      1       0                 0   \n",
       "2819             0            0       0      1       0                 0   \n",
       "2820             0            0       0      1       0                 0   \n",
       "2821             0            0       0      1       0                 0   \n",
       "2822             0            0       0      1       0                 0   \n",
       "\n",
       "      Vintage Cars  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "2818             0  \n",
       "2819             0  \n",
       "2820             0  \n",
       "2821             0  \n",
       "2822             0  \n",
       "\n",
       "[2823 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['PRODUCTLINE'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
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       "      <th>CITY</th>\n",
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       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
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       "      <td>Yu</td>\n",
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       "      <td>Small</td>\n",
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       "      <td>2765.90</td>\n",
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       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
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       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
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       "    <tr>\n",
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       "      <td>10145</td>\n",
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       "      <td>6</td>\n",
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       "      <td>8</td>\n",
       "      <td>2003</td>\n",
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       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
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       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
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       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>10350</td>\n",
       "      <td>20</td>\n",
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       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1                59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2     27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3                78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                   7734 Strong St.           NaN  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2819                    Torikatu 38           NaN           Oulu   NaN   \n",
       "2820             C/ Moralzarzal, 86           NaN         Madrid   NaN   \n",
       "2821          1 rue Alsace-Lorraine           NaN       Toulouse   NaN   \n",
       "2822             8616 Spinnaker Dr.           NaN         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA       NaN              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA       NaN           Young            Julie   Medium  \n",
       "4           NaN      USA       NaN           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA       NaN         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename({'PRODUCTLINE':'Productline'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>10350</td>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  abcd  PRICEEACH  ORDERLINENUMBER    SALES        ORDERDATE  \\\n",
       "0           10107    30      95.70                2  2871.00   2/24/2003 0:00   \n",
       "1           10121    34      81.35                5  2765.90    5/7/2003 0:00   \n",
       "2           10134    41      94.74                2  3884.34    7/1/2003 0:00   \n",
       "3           10145    45      83.26                6  3746.70   8/25/2003 0:00   \n",
       "4           10159    49     100.00               14  5205.27  10/10/2003 0:00   \n",
       "...           ...   ...        ...              ...      ...              ...   \n",
       "2818        10350    20     100.00               15  2244.40   12/2/2004 0:00   \n",
       "2819        10373    29     100.00                1  3978.51   1/31/2005 0:00   \n",
       "2820        10386    43     100.00                4  5417.57    3/1/2005 0:00   \n",
       "2821        10397    34      62.24                1  2116.16   3/28/2005 0:00   \n",
       "2822        10414    47      65.52                9  3079.44    5/6/2005 0:00   \n",
       "\n",
       "        STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...                   ADDRESSLINE1  \\\n",
       "0      Shipped       1         2     2003  ...        897 Long Airport Avenue   \n",
       "1      Shipped       2         5     2003  ...             59 rue de l'Abbaye   \n",
       "2      Shipped       3         7     2003  ...  27 rue du Colonel Pierre Avia   \n",
       "3      Shipped       3         8     2003  ...             78934 Hillside Dr.   \n",
       "4      Shipped       4        10     2003  ...                7734 Strong St.   \n",
       "...        ...     ...       ...      ...  ...                            ...   \n",
       "2818   Shipped       4        12     2004  ...             C/ Moralzarzal, 86   \n",
       "2819   Shipped       1         1     2005  ...                    Torikatu 38   \n",
       "2820  Resolved       1         3     2005  ...             C/ Moralzarzal, 86   \n",
       "2821   Shipped       1         3     2005  ...          1 rue Alsace-Lorraine   \n",
       "2822   On Hold       2         5     2005  ...             8616 Spinnaker Dr.   \n",
       "\n",
       "      ADDRESSLINE2           CITY STATE POSTALCODE  COUNTRY TERRITORY  \\\n",
       "0              NaN            NYC    NY      10022      USA       NaN   \n",
       "1              NaN          Reims   NaN      51100   France      EMEA   \n",
       "2              NaN          Paris   NaN      75508   France      EMEA   \n",
       "3              NaN       Pasadena    CA      90003      USA       NaN   \n",
       "4              NaN  San Francisco    CA        NaN      USA       NaN   \n",
       "...            ...            ...   ...        ...      ...       ...   \n",
       "2818           NaN         Madrid   NaN      28034    Spain      EMEA   \n",
       "2819           NaN           Oulu   NaN      90110  Finland      EMEA   \n",
       "2820           NaN         Madrid   NaN      28034    Spain      EMEA   \n",
       "2821           NaN       Toulouse   NaN      31000   France      EMEA   \n",
       "2822           NaN         Boston    MA      51003      USA       NaN   \n",
       "\n",
       "     CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0                 Yu             Kwai    Small  \n",
       "1            Henriot             Paul    Small  \n",
       "2           Da Cunha           Daniel   Medium  \n",
       "3              Young            Julie   Medium  \n",
       "4              Brown            Julie   Medium  \n",
       "...              ...              ...      ...  \n",
       "2818          Freyre            Diego    Small  \n",
       "2819       Koskitalo           Pirkko   Medium  \n",
       "2820          Freyre            Diego   Medium  \n",
       "2821          Roulet          Annette    Small  \n",
       "2822         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'QUANTITYORDERED':'abcd'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Wrangling\n",
    "\n",
    "df.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>0</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>0</td>\n",
       "      <td>Reims</td>\n",
       "      <td>0</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>0</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>0</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>0</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>10350</td>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>0</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>0</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>0</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>0</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>0</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue             0            NYC    NY   \n",
       "1                59 rue de l'Abbaye             0          Reims     0   \n",
       "2     27 rue du Colonel Pierre Avia             0          Paris     0   \n",
       "3                78934 Hillside Dr.             0       Pasadena    CA   \n",
       "4                   7734 Strong St.             0  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86             0         Madrid     0   \n",
       "2819                    Torikatu 38             0           Oulu     0   \n",
       "2820             C/ Moralzarzal, 86             0         Madrid     0   \n",
       "2821          1 rue Alsace-Lorraine             0       Toulouse     0   \n",
       "2822             8616 Spinnaker Dr.             0         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA         0              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA         0           Young            Julie   Medium  \n",
       "4             0      USA         0           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA         0         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>0</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>0</td>\n",
       "      <td>Reims</td>\n",
       "      <td>0</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>0</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>0</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>0</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>10350</td>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>0</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>0</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>0</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>0</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>0</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
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       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue             0            NYC    NY   \n",
       "1                59 rue de l'Abbaye             0          Reims     0   \n",
       "2     27 rue du Colonel Pierre Avia             0          Paris     0   \n",
       "3                78934 Hillside Dr.             0       Pasadena    CA   \n",
       "4                   7734 Strong St.             0  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86             0         Madrid     0   \n",
       "2819                    Torikatu 38             0           Oulu     0   \n",
       "2820             C/ Moralzarzal, 86             0         Madrid     0   \n",
       "2821          1 rue Alsace-Lorraine             0       Toulouse     0   \n",
       "2822             8616 Spinnaker Dr.             0         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA         0              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA         0           Young            Julie   Medium  \n",
       "4             0      USA         0           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA         0         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# forwardfill --> ffill --> ascending fill values by last one, row etc\n",
    "# backwordfill --> bfill --> decending fill values by last one, row etc \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
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       "...           ...              ...        ...              ...      ...   \n",
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       "\n",
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       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
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       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
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       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue             0            NYC    NY   \n",
       "1                59 rue de l'Abbaye             0          Reims     0   \n",
       "2     27 rue du Colonel Pierre Avia             0          Paris     0   \n",
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       "4                   7734 Strong St.             0  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
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       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
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       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA         0           Young            Julie   Medium  \n",
       "4             0      USA         0           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA         0         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_9288/592730996.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.sort_index will be keyword-only\n",
      "  df.sort_index(0)\n"
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       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>0</td>\n",
       "      <td>Reims</td>\n",
       "      <td>0</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>0</td>\n",
       "      <td>Paris</td>\n",
       "      <td>0</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>0</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>0</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>10350</td>\n",
       "      <td>20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>15</td>\n",
       "      <td>2244.40</td>\n",
       "      <td>12/2/2004 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2004</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>10373</td>\n",
       "      <td>29</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3978.51</td>\n",
       "      <td>1/31/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>Torikatu 38</td>\n",
       "      <td>0</td>\n",
       "      <td>Oulu</td>\n",
       "      <td>0</td>\n",
       "      <td>90110</td>\n",
       "      <td>Finland</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Koskitalo</td>\n",
       "      <td>Pirkko</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>10386</td>\n",
       "      <td>43</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5417.57</td>\n",
       "      <td>3/1/2005 0:00</td>\n",
       "      <td>Resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>0</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>10397</td>\n",
       "      <td>34</td>\n",
       "      <td>62.24</td>\n",
       "      <td>1</td>\n",
       "      <td>2116.16</td>\n",
       "      <td>3/28/2005 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>1 rue Alsace-Lorraine</td>\n",
       "      <td>0</td>\n",
       "      <td>Toulouse</td>\n",
       "      <td>0</td>\n",
       "      <td>31000</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Roulet</td>\n",
       "      <td>Annette</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>10414</td>\n",
       "      <td>47</td>\n",
       "      <td>65.52</td>\n",
       "      <td>9</td>\n",
       "      <td>3079.44</td>\n",
       "      <td>5/6/2005 0:00</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>...</td>\n",
       "      <td>8616 Spinnaker Dr.</td>\n",
       "      <td>0</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>51003</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>Yoshido</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2823 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0           10107               30      95.70                2  2871.00   \n",
       "1           10121               34      81.35                5  2765.90   \n",
       "2           10134               41      94.74                2  3884.34   \n",
       "3           10145               45      83.26                6  3746.70   \n",
       "4           10159               49     100.00               14  5205.27   \n",
       "...           ...              ...        ...              ...      ...   \n",
       "2818        10350               20     100.00               15  2244.40   \n",
       "2819        10373               29     100.00                1  3978.51   \n",
       "2820        10386               43     100.00                4  5417.57   \n",
       "2821        10397               34      62.24                1  2116.16   \n",
       "2822        10414               47      65.52                9  3079.44   \n",
       "\n",
       "            ORDERDATE    STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0      2/24/2003 0:00   Shipped       1         2     2003  ...   \n",
       "1       5/7/2003 0:00   Shipped       2         5     2003  ...   \n",
       "2       7/1/2003 0:00   Shipped       3         7     2003  ...   \n",
       "3      8/25/2003 0:00   Shipped       3         8     2003  ...   \n",
       "4     10/10/2003 0:00   Shipped       4        10     2003  ...   \n",
       "...               ...       ...     ...       ...      ...  ...   \n",
       "2818   12/2/2004 0:00   Shipped       4        12     2004  ...   \n",
       "2819   1/31/2005 0:00   Shipped       1         1     2005  ...   \n",
       "2820    3/1/2005 0:00  Resolved       1         3     2005  ...   \n",
       "2821   3/28/2005 0:00   Shipped       1         3     2005  ...   \n",
       "2822    5/6/2005 0:00   On Hold       2         5     2005  ...   \n",
       "\n",
       "                       ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0           897 Long Airport Avenue             0            NYC    NY   \n",
       "1                59 rue de l'Abbaye             0          Reims     0   \n",
       "2     27 rue du Colonel Pierre Avia             0          Paris     0   \n",
       "3                78934 Hillside Dr.             0       Pasadena    CA   \n",
       "4                   7734 Strong St.             0  San Francisco    CA   \n",
       "...                             ...           ...            ...   ...   \n",
       "2818             C/ Moralzarzal, 86             0         Madrid     0   \n",
       "2819                    Torikatu 38             0           Oulu     0   \n",
       "2820             C/ Moralzarzal, 86             0         Madrid     0   \n",
       "2821          1 rue Alsace-Lorraine             0       Toulouse     0   \n",
       "2822             8616 Spinnaker Dr.             0         Boston    MA   \n",
       "\n",
       "     POSTALCODE  COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         10022      USA         0              Yu             Kwai    Small  \n",
       "1         51100   France      EMEA         Henriot             Paul    Small  \n",
       "2         75508   France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3         90003      USA         0           Young            Julie   Medium  \n",
       "4             0      USA         0           Brown            Julie   Medium  \n",
       "...         ...      ...       ...             ...              ...      ...  \n",
       "2818      28034    Spain      EMEA          Freyre            Diego    Small  \n",
       "2819      90110  Finland      EMEA       Koskitalo           Pirkko   Medium  \n",
       "2820      28034    Spain      EMEA          Freyre            Diego   Medium  \n",
       "2821      31000   France      EMEA          Roulet          Annette    Small  \n",
       "2822      51003      USA         0         Yoshido             Juri   Medium  \n",
       "\n",
       "[2823 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Graphs\n",
    "- Chart Visualization\n",
    "- basic Plotting : plot()\n",
    "\n",
    "- ‘bar’ or ‘barh’ for bar plots\n",
    "\n",
    "- ‘hist’ for histogram\n",
    "\n",
    "- ‘box’ for boxplot\n",
    "\n",
    "- ‘kde’ or ‘density’ for density plots\n",
    "\n",
    "- ‘area’ for area plots\n",
    "\n",
    "- ‘scatter’ for scatter plots\n",
    "\n",
    "- ‘hexbin’ for hexagonal bin plots\n",
    "\n",
    "- ‘pie’ for pie plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Graphs\n",
    "- Bar Graph\n",
    "- Pie Chart\n",
    "- Box Plot\n",
    "- Histogram\n",
    "- Line Chart and Subplots\n",
    "- Scatter Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn Graphs\n",
    "#### Distribution Plots\n",
    "-  Dist-Plot\n",
    "-  Joint Plot\n",
    "-  Pair Plot\n",
    "-  Rug Plot\n",
    "#### Categorical Plots\n",
    "-   Bar Plot\n",
    "-   Count Plot\n",
    "-   Box Plot\n",
    "-   Violin Plot\n",
    "#### Advanced Plots\n",
    "-   Strip Plot\n",
    "-   Swarm Plot\n",
    "#### Matrix Plots\n",
    "-   Heat Map\n",
    "-   Cluster Map\n",
    "#### Grids\n",
    "-   Facet Grid\n",
    "#### Regression Plots\n",
    "-   lmplot\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (0.11.2)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (1.7.3)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (3.5.1)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (1.3.5)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (1.22.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (3.0.6)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (9.0.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (4.28.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phool = sns.load_dataset('iris')\n",
    "kashti = sns.load_dataset('titanic')\n",
    "phool\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(phool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kashti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kashti.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_9732/1679233432.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mbarplot\u001b[1;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   3180\u001b[0m ):\n\u001b[0;32m   3181\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3182\u001b[1;33m     plotter = _BarPlotter(x, y, hue, data, order, hue_order,\n\u001b[0m\u001b[0;32m   3183\u001b[0m                           \u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3184\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1582\u001b[0m                  errwidth, capsize, dodge):\n\u001b[0;32m   1583\u001b[0m         \u001b[1;34m\"\"\"Initialize the plotter.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1584\u001b[1;33m         self.establish_variables(x, y, hue, data, orient,\n\u001b[0m\u001b[0;32m   1585\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m   1586\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m             orient = infer_orient(\n\u001b[0m\u001b[0;32m    157\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequire_numeric\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m             )\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(x, y, orient, require_numeric)\u001b[0m\n\u001b[0;32m   1309\u001b[0m     \"\"\"\n\u001b[0;32m   1310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1311\u001b[1;33m     \u001b[0mx_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mvariable_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1312\u001b[0m     \u001b[0my_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mvariable_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36mvariable_type\u001b[1;34m(vector, boolean_type)\u001b[0m\n\u001b[0;32m   1227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1228\u001b[0m     \u001b[1;31m# Special-case all-na data, which is always \"numeric\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1229\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1230\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"numeric\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1535\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1536\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1537\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m   1538\u001b[0m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1539\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "sns.barplot(phool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'gammas',\n",
       " 'geyser',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('planets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('Metaverse.xlsx',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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