About_Data_Frames.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4499a36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7ec8cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import DataFrame,Series\n",
    "from numpy import nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6b5c7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64509528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import webbrowser\n",
    "website = 'https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html'\n",
    "webbrowser.open(website)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7f95ce4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/f02mOEt11OQ\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1aac214af10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "#code-fi / lofi beats to code/relax to\n",
    "#video by 'The AMP channel'\n",
    "YouTubeVideo('f02mOEt11OQ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2c73918d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename= 'C:/Users/Moin Dalvi/Downloads/1326466.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d07c6989",
   "metadata": {},
   "outputs": [],
   "source": [
    "#![ChessUrl](https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif \"chess\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e14c5e",
   "metadata": {},
   "source": [
    "![FocusUrl](https://media.giphy.com/media/z9FwUoGNksJEDBJnIF/giphy.gif \"Focus\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea2e447b",
   "metadata": {},
   "source": [
    "![ChessUrl](https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif \"chess\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbed7841",
   "metadata": {},
   "source": [
    "<iframe src=\"https://giphy.com/embed/z9FwUoGNksJEDBJnIF\" width=\"480\" height=\"312\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/ramseysolutions-focus-focused-pay-attention-z9FwUoGNksJEDBJnIF\">Focus Gif</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366ded6e",
   "metadata": {},
   "source": [
    "## <iframe src=\"https://giphy.com/embed/5kq0GCjHA8Rwc\" width=\"480\" height=\"360\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/throwback-rick-roll-astley-5kq0GCjHA8Rwc\"><p style='color:red;font-size:25px'>Yeah here look at the below Link</p></a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f78d43e",
   "metadata": {},
   "source": [
    "<iframe width=\"1280\" height=\"720\" src=\"https://www.youtube.com/embed/dQw4w9WgXcQ\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe><p><a href=\"https://youtu.be/dQw4w9WgXcQ\"><p style='color:red;font-size:35px'><b>Click Here for a shortcut to be a Millionare</b></p></a></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db77d7f2",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (Temp/ipykernel_4588/3070548811.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_4588/3070548811.py\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    example = pd.read_csv('C:\\Users\\Moin Dalvi\\Documents\\EXcelR Study and Assignment Material\\python\\Salaries.csv')\u001b[0m\n\u001b[1;37m                                                                                                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
     ]
    }
   ],
   "source": [
    "example = pd.read_csv('C:\\Users\\Moin Dalvi\\Documents\\EXcelR Study and Assignment Material\\python\\Salaries.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3bb962",
   "metadata": {},
   "source": [
    "# Absolute path to import files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5f6f4d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "example = pd.read_csv('C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/python/Salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "884f3024",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbe646b",
   "metadata": {},
   "source": [
    "# Relative path to import Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb07680",
   "metadata": {},
   "outputs": [],
   "source": [
    "C:\\Users\\Moin Dalvi\\Documents\\EXcelR Study and Assignment Material\\python\\Universities.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "113c2ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    " examplepath1 = pd.read_csv('../Documents/EXcelR Study and Assignment Material/python/Universities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b23692e",
   "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>Univ</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMU</td>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Columbia</td>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cornell</td>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dartmouth</td>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duke</td>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harvard</td>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JohnsHopkins</td>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIT</td>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwestern</td>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NotreDame</td>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PennState</td>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Princeton</td>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Purdue</td>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stanford</td>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TexasA&amp;M</td>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UCBerkeley</td>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UChicago</td>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UMichigan</td>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UPenn</td>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UVA</td>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWisconsin</td>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yale</td>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "0          Brown  1310     89      22       13     22704        94\n",
       "1        CalTech  1415    100      25        6     63575        81\n",
       "2            CMU  1260     62      59        9     25026        72\n",
       "3       Columbia  1310     76      24       12     31510        88\n",
       "4        Cornell  1280     83      33       13     21864        90\n",
       "5      Dartmouth  1340     89      23       10     32162        95\n",
       "6           Duke  1315     90      30       12     31585        95\n",
       "7     Georgetown  1255     74      24       12     20126        92\n",
       "8        Harvard  1400     91      14       11     39525        97\n",
       "9   JohnsHopkins  1305     75      44        7     58691        87\n",
       "10           MIT  1380     94      30       10     34870        91\n",
       "11  Northwestern  1260     85      39       11     28052        89\n",
       "12     NotreDame  1255     81      42       13     15122        94\n",
       "13     PennState  1081     38      54       18     10185        80\n",
       "14     Princeton  1375     91      14        8     30220        95\n",
       "15        Purdue  1005     28      90       19      9066        69\n",
       "16      Stanford  1360     90      20       12     36450        93\n",
       "17      TexasA&M  1075     49      67       25      8704        67\n",
       "18    UCBerkeley  1240     95      40       17     15140        78\n",
       "19      UChicago  1290     75      50       13     38380        87\n",
       "20     UMichigan  1180     65      68       16     15470        85\n",
       "21         UPenn  1285     80      36       11     27553        90\n",
       "22           UVA  1225     77      44       14     13349        92\n",
       "23    UWisconsin  1085     40      69       15     11857        71\n",
       "24          Yale  1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examplepath1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7b409a",
   "metadata": {},
   "source": [
    "# Create a <u>DataFrame</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8adbad04",
   "metadata": {},
   "source": [
    "### a.Read from ClipBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5fc98c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>Bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>992 Settled St</td>\n",
       "      <td>823,049</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1506 Guido St</td>\n",
       "      <td>784,049</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>247 Fort St</td>\n",
       "      <td>299,238</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132 Walrus Ave</td>\n",
       "      <td>299,001</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491 Python St</td>\n",
       "      <td>293,923</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4981 Anytown Rd</td>\n",
       "      <td>199,000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>938 Zeal Rd</td>\n",
       "      <td>148,398</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>123 Main St</td>\n",
       "      <td>99,000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Address    Price  Bedrooms\n",
       "0   992 Settled St  823,049         4\n",
       "1    1506 Guido St  784,049         3\n",
       "2      247 Fort St  299,238         3\n",
       "3   132 Walrus Ave  299,001         2\n",
       "4    491 Python St  293,923         4\n",
       "5  4981 Anytown Rd  199,000         4\n",
       "6      938 Zeal Rd  148,398         2\n",
       "7      123 Main St   99,000         1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfl = pd.read_clipboard()\n",
    "nfl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b4a9f832",
   "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>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>Bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>992 Settled St</td>\n",
       "      <td>823,049</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1506 Guido St</td>\n",
       "      <td>784,049</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>247 Fort St</td>\n",
       "      <td>299,238</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132 Walrus Ave</td>\n",
       "      <td>299,001</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491 Python St</td>\n",
       "      <td>293,923</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4981 Anytown Rd</td>\n",
       "      <td>199,000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>938 Zeal Rd</td>\n",
       "      <td>148,398</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>123 Main St</td>\n",
       "      <td>99,000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Address    Price  Bedrooms\n",
       "0   992 Settled St  823,049         4\n",
       "1    1506 Guido St  784,049         3\n",
       "2      247 Fort St  299,238         3\n",
       "3   132 Walrus Ave  299,001         2\n",
       "4    491 Python St  293,923         4\n",
       "5  4981 Anytown Rd  199,000         4\n",
       "6      938 Zeal Rd  148,398         2\n",
       "7      123 Main St   99,000         1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfl.to_csv('nfl.csv', index = False)\n",
    "nfl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f5ad10",
   "metadata": {},
   "source": [
    "### b.Read from .csv Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5d1dbb90",
   "metadata": {},
   "outputs": [],
   "source": [
    " ex = pd.read_csv('Universities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bcd84784",
   "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>Univ</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMU</td>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Columbia</td>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cornell</td>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dartmouth</td>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duke</td>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harvard</td>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JohnsHopkins</td>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIT</td>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwestern</td>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NotreDame</td>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PennState</td>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Princeton</td>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Purdue</td>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stanford</td>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TexasA&amp;M</td>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UCBerkeley</td>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UChicago</td>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UMichigan</td>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UPenn</td>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UVA</td>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWisconsin</td>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yale</td>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "0          Brown  1310     89      22       13     22704        94\n",
       "1        CalTech  1415    100      25        6     63575        81\n",
       "2            CMU  1260     62      59        9     25026        72\n",
       "3       Columbia  1310     76      24       12     31510        88\n",
       "4        Cornell  1280     83      33       13     21864        90\n",
       "5      Dartmouth  1340     89      23       10     32162        95\n",
       "6           Duke  1315     90      30       12     31585        95\n",
       "7     Georgetown  1255     74      24       12     20126        92\n",
       "8        Harvard  1400     91      14       11     39525        97\n",
       "9   JohnsHopkins  1305     75      44        7     58691        87\n",
       "10           MIT  1380     94      30       10     34870        91\n",
       "11  Northwestern  1260     85      39       11     28052        89\n",
       "12     NotreDame  1255     81      42       13     15122        94\n",
       "13     PennState  1081     38      54       18     10185        80\n",
       "14     Princeton  1375     91      14        8     30220        95\n",
       "15        Purdue  1005     28      90       19      9066        69\n",
       "16      Stanford  1360     90      20       12     36450        93\n",
       "17      TexasA&M  1075     49      67       25      8704        67\n",
       "18    UCBerkeley  1240     95      40       17     15140        78\n",
       "19      UChicago  1290     75      50       13     38380        87\n",
       "20     UMichigan  1180     65      68       16     15470        85\n",
       "21         UPenn  1285     80      36       11     27553        90\n",
       "22           UVA  1225     77      44       14     13349        92\n",
       "23    UWisconsin  1085     40      69       15     11857        71\n",
       "24          Yale  1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "82e4d420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "90569c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Univ</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "0    Brown  1310     89      22       13     22704        94\n",
       "1  CalTech  1415    100      25        6     63575        81"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('Universities.csv', nrows=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7e795f0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SAT  Top10\n",
       "0   1310     89\n",
       "1   1415    100\n",
       "2   1260     62\n",
       "3   1310     76\n",
       "4   1280     83\n",
       "5   1340     89\n",
       "6   1315     90\n",
       "7   1255     74\n",
       "8   1400     91\n",
       "9   1305     75\n",
       "10  1380     94\n",
       "11  1260     85\n",
       "12  1255     81\n",
       "13  1081     38\n",
       "14  1375     91\n",
       "15  1005     28\n",
       "16  1360     90\n",
       "17  1075     49\n",
       "18  1240     95\n",
       "19  1290     75\n",
       "20  1180     65\n",
       "21  1285     80\n",
       "22  1225     77\n",
       "23  1085     40\n",
       "24  1375     95"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('Universities.csv', usecols = [1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cdc2f930",
   "metadata": {},
   "outputs": [],
   "source": [
    "series_file = pd.read_csv('Universities.csv', usecols = ['Univ'], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "06d2cd20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Brown\n",
       "1          CalTech\n",
       "2              CMU\n",
       "3         Columbia\n",
       "4          Cornell\n",
       "5        Dartmouth\n",
       "6             Duke\n",
       "7       Georgetown\n",
       "8          Harvard\n",
       "9     JohnsHopkins\n",
       "10             MIT\n",
       "11    Northwestern\n",
       "12       NotreDame\n",
       "13       PennState\n",
       "14       Princeton\n",
       "15          Purdue\n",
       "16        Stanford\n",
       "17        TexasA&M\n",
       "18      UCBerkeley\n",
       "19        UChicago\n",
       "20       UMichigan\n",
       "21           UPenn\n",
       "22             UVA\n",
       "23      UWisconsin\n",
       "24            Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4074c438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(series_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0a7693",
   "metadata": {},
   "source": [
    "## Selecting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0c234c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Univ</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>Brown</th>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CalTech</th>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMU</th>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cornell</th>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dartmouth</th>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duke</th>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgetown</th>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harvard</th>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JohnsHopkins</th>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIT</th>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwestern</th>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NotreDame</th>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PennState</th>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princeton</th>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purdue</th>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanford</th>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TexasA&amp;M</th>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCBerkeley</th>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UChicago</th>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UMichigan</th>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPenn</th>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVA</th>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UWisconsin</th>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yale</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                          \n",
       "Brown         1310     89      22       13     22704        94\n",
       "CalTech       1415    100      25        6     63575        81\n",
       "CMU           1260     62      59        9     25026        72\n",
       "Columbia      1310     76      24       12     31510        88\n",
       "Cornell       1280     83      33       13     21864        90\n",
       "Dartmouth     1340     89      23       10     32162        95\n",
       "Duke          1315     90      30       12     31585        95\n",
       "Georgetown    1255     74      24       12     20126        92\n",
       "Harvard       1400     91      14       11     39525        97\n",
       "JohnsHopkins  1305     75      44        7     58691        87\n",
       "MIT           1380     94      30       10     34870        91\n",
       "Northwestern  1260     85      39       11     28052        89\n",
       "NotreDame     1255     81      42       13     15122        94\n",
       "PennState     1081     38      54       18     10185        80\n",
       "Princeton     1375     91      14        8     30220        95\n",
       "Purdue        1005     28      90       19      9066        69\n",
       "Stanford      1360     90      20       12     36450        93\n",
       "TexasA&M      1075     49      67       25      8704        67\n",
       "UCBerkeley    1240     95      40       17     15140        78\n",
       "UChicago      1290     75      50       13     38380        87\n",
       "UMichigan     1180     65      68       16     15470        85\n",
       "UPenn         1285     80      36       11     27553        90\n",
       "UVA           1225     77      44       14     13349        92\n",
       "UWisconsin    1085     40      69       15     11857        71\n",
       "Yale          1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_ = pd.read_csv('Universities.csv', index_col = 'Univ')\n",
    "data_frame_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "09bec432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SAT', 'Top10', 'Accept', 'SFRatio', 'Expenses', 'GradRate'], dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f53baba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Univ\n",
       "Brown           22704\n",
       "CalTech         63575\n",
       "CMU             25026\n",
       "Columbia        31510\n",
       "Cornell         21864\n",
       "Dartmouth       32162\n",
       "Duke            31585\n",
       "Georgetown      20126\n",
       "Harvard         39525\n",
       "JohnsHopkins    58691\n",
       "MIT             34870\n",
       "Northwestern    28052\n",
       "NotreDame       15122\n",
       "PennState       10185\n",
       "Princeton       30220\n",
       "Purdue           9066\n",
       "Stanford        36450\n",
       "TexasA&M         8704\n",
       "UCBerkeley      15140\n",
       "UChicago        38380\n",
       "UMichigan       15470\n",
       "UPenn           27553\n",
       "UVA             13349\n",
       "UWisconsin      11857\n",
       "Yale            43514\n",
       "Name: Expenses, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
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    "data_frame_.Expenses"
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  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "833a50c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Univ\n",
       "Brown            89\n",
       "CalTech         100\n",
       "CMU              62\n",
       "Columbia         76\n",
       "Cornell          83\n",
       "Dartmouth        89\n",
       "Duke             90\n",
       "Georgetown       74\n",
       "Harvard          91\n",
       "JohnsHopkins     75\n",
       "MIT              94\n",
       "Northwestern     85\n",
       "NotreDame        81\n",
       "PennState        38\n",
       "Princeton        91\n",
       "Purdue           28\n",
       "Stanford         90\n",
       "TexasA&M         49\n",
       "UCBerkeley       95\n",
       "UChicago         75\n",
       "UMichigan        65\n",
       "UPenn            80\n",
       "UVA              77\n",
       "UWisconsin       40\n",
       "Yale             95\n",
       "Name: Top10, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
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    "data_frame_['Top10']"
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   "id": "f8e51a36",
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       "      <td>11</td>\n",
       "      <td>95</td>\n",
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       "              SFRatio  Top10\n",
       "Univ                        \n",
       "Brown              13     89\n",
       "CalTech             6    100\n",
       "CMU                 9     62\n",
       "Columbia           12     76\n",
       "Cornell            13     83\n",
       "Dartmouth          10     89\n",
       "Duke               12     90\n",
       "Georgetown         12     74\n",
       "Harvard            11     91\n",
       "JohnsHopkins        7     75\n",
       "MIT                10     94\n",
       "Northwestern       11     85\n",
       "NotreDame          13     81\n",
       "PennState          18     38\n",
       "Princeton           8     91\n",
       "Purdue             19     28\n",
       "Stanford           12     90\n",
       "TexasA&M           25     49\n",
       "UCBerkeley         17     95\n",
       "UChicago           13     75\n",
       "UMichigan          16     65\n",
       "UPenn              11     80\n",
       "UVA                14     77\n",
       "UWisconsin         15     40\n",
       "Yale               11     95"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data_frame_[['SFRatio','Top10']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4c7eca6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SAT          1375\n",
       "Top10          95\n",
       "Accept         19\n",
       "SFRatio        11\n",
       "Expenses    43514\n",
       "GradRate       96\n",
       "Name: Yale, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
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  {
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   "execution_count": 99,
   "id": "77944593",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Columbia</th>\n",
       "      <td>1310</td>\n",
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       "      <th>Cornell</th>\n",
       "      <td>1280</td>\n",
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       "      <td>13</td>\n",
       "      <td>21864</td>\n",
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       "      <th>Dartmouth</th>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>Duke</th>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
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       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>Georgetown</th>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
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       "      <th>Harvard</th>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JohnsHopkins</th>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIT</th>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwestern</th>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
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       "    <tr>\n",
       "      <th>NotreDame</th>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
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       "    <tr>\n",
       "      <th>PennState</th>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princeton</th>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purdue</th>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanford</th>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TexasA&amp;M</th>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCBerkeley</th>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UChicago</th>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UMichigan</th>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPenn</th>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVA</th>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UWisconsin</th>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>Yale</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
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      ],
      "text/plain": [
       "               SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                          \n",
       "Brown         1310     89      22       13     22704        94\n",
       "CalTech       1415    100      25        6     63575        81\n",
       "CMU           1260     62      59        9     25026        72\n",
       "Columbia      1310     76      24       12     31510        88\n",
       "Cornell       1280     83      33       13     21864        90\n",
       "Dartmouth     1340     89      23       10     32162        95\n",
       "Duke          1315     90      30       12     31585        95\n",
       "Georgetown    1255     74      24       12     20126        92\n",
       "Harvard       1400     91      14       11     39525        97\n",
       "JohnsHopkins  1305     75      44        7     58691        87\n",
       "MIT           1380     94      30       10     34870        91\n",
       "Northwestern  1260     85      39       11     28052        89\n",
       "NotreDame     1255     81      42       13     15122        94\n",
       "PennState     1081     38      54       18     10185        80\n",
       "Princeton     1375     91      14        8     30220        95\n",
       "Purdue        1005     28      90       19      9066        69\n",
       "Stanford      1360     90      20       12     36450        93\n",
       "TexasA&M      1075     49      67       25      8704        67\n",
       "UCBerkeley    1240     95      40       17     15140        78\n",
       "UChicago      1290     75      50       13     38380        87\n",
       "UMichigan     1180     65      68       16     15470        85\n",
       "UPenn         1285     80      36       11     27553        90\n",
       "UVA           1225     77      44       14     13349        92\n",
       "UWisconsin    1085     40      69       15     11857        71\n",
       "Yale          1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ac0a9f3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Univ\n",
       "Brown            True\n",
       "CalTech         False\n",
       "CMU             False\n",
       "Columbia        False\n",
       "Cornell         False\n",
       "Dartmouth        True\n",
       "Duke             True\n",
       "Georgetown       True\n",
       "Harvard          True\n",
       "JohnsHopkins    False\n",
       "MIT              True\n",
       "Northwestern    False\n",
       "NotreDame        True\n",
       "PennState       False\n",
       "Princeton        True\n",
       "Purdue          False\n",
       "Stanford         True\n",
       "TexasA&M        False\n",
       "UCBerkeley      False\n",
       "UChicago        False\n",
       "UMichigan       False\n",
       "UPenn           False\n",
       "UVA              True\n",
       "UWisconsin      False\n",
       "Yale             True\n",
       "Name: GradRate, dtype: bool"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_['GradRate'] > 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7beb1190",
   "metadata": {},
   "outputs": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Univ</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>Brown</th>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dartmouth</th>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duke</th>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgetown</th>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harvard</th>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIT</th>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NotreDame</th>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princeton</th>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanford</th>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVA</th>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yale</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                        \n",
       "Brown       1310     89      22       13     22704        94\n",
       "Dartmouth   1340     89      23       10     32162        95\n",
       "Duke        1315     90      30       12     31585        95\n",
       "Georgetown  1255     74      24       12     20126        92\n",
       "Harvard     1400     91      14       11     39525        97\n",
       "MIT         1380     94      30       10     34870        91\n",
       "NotreDame   1255     81      42       13     15122        94\n",
       "Princeton   1375     91      14        8     30220        95\n",
       "Stanford    1360     90      20       12     36450        93\n",
       "UVA         1225     77      44       14     13349        92\n",
       "Yale        1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_[ data_frame_['GradRate'] > 90 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bf7fb1e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Univ</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>Brown</th>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CalTech</th>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMU</th>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cornell</th>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                      \n",
       "Brown     1310     89      22       13     22704        94\n",
       "CalTech   1415    100      25        6     63575        81\n",
       "CMU       1260     62      59        9     25026        72\n",
       "Columbia  1310     76      24       12     31510        88\n",
       "Cornell   1280     83      33       13     21864        90"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1c693231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Univ</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>UMichigan</th>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPenn</th>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVA</th>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UWisconsin</th>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yale</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                        \n",
       "UMichigan   1180     65      68       16     15470        85\n",
       "UPenn       1285     80      36       11     27553        90\n",
       "UVA         1225     77      44       14     13349        92\n",
       "UWisconsin  1085     40      69       15     11857        71\n",
       "Yale        1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e41eee",
   "metadata": {},
   "source": [
    "# Droping a <u>Row</u> or a <u>Column</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "339eb8e3",
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pop</th>\n",
       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pop  Size  Year\n",
       "LA    0     1     2\n",
       "NY    3     4     5\n",
       "SF    6     7     8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe = DataFrame( np.arange(9).reshape((3,3)),index=['LA','NY','SF'],columns=['Pop','Size','Year'])\n",
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f6ad8f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>year</th>\n",
       "      <th>size</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LL</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoLo</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hi</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HiHi</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop  year  size  age\n",
       "LL      0     1     2    3\n",
       "LoLo    4     5     6    7\n",
       "Hi      8     9    10   11\n",
       "HiHi   12    13    14   15"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1 = DataFrame( np.arange(16).reshape(4,4),index=['LL','LoLo','Hi','HiHi'],columns=['pop','year','size','age'])\n",
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5234e96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = DataFrame(np.arange(4).reshape(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "12abd6cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   0  1   2   3\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
       "2  8  9  10  11"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2 = DataFrame( np.arange(12).reshape(3,4))\n",
    "dframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "771a3a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2\n",
       "0  0   1   2\n",
       "1  3   4   5\n",
       "2  6   7   8\n",
       "3  9  10  11"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe3 = DataFrame(np.arange(12).reshape(4,3))\n",
    "dframe3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "238da266",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
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       "  </thead>\n",
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       "      <th>SF</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Pop  Size  Year\n",
       "NY    3     4     5\n",
       "SF    6     7     8"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.drop('LA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c92737e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.drop of     Pop  Size  Year\n",
       "LA    0     1     2\n",
       "NY    3     4     5\n",
       "SF    6     7     8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5cf56b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dframe.drop('LA', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3c796e4",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>SF</th>\n",
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      "text/plain": [
       "    Pop  Size  Year\n",
       "NY    3     4     5\n",
       "SF    6     7     8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "593971f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pop  Size\n",
       "NY    3     4\n",
       "SF    6     7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.drop(\"Year\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3816bceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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      "text/plain": [
       "    Pop  Size  Year\n",
       "SF    6     7     8"
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     "execution_count": 15,
     "metadata": {},
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    }
   ],
   "source": [
    "dframe.drop(\"NY\", axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3053ca48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>Brown</th>\n",
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       "      <td>22</td>\n",
       "      <td>13</td>\n",
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       "      <td>94</td>\n",
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       "      <th>CalTech</th>\n",
       "      <td>100</td>\n",
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       "      <th>CMU</th>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
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       "    <tr>\n",
       "      <th>Cornell</th>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
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       "    <tr>\n",
       "      <th>Dartmouth</th>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>Duke</th>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgetown</th>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harvard</th>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JohnsHopkins</th>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIT</th>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwestern</th>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NotreDame</th>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PennState</th>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princeton</th>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purdue</th>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanford</th>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TexasA&amp;M</th>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCBerkeley</th>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UChicago</th>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
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       "    <tr>\n",
       "      <th>UMichigan</th>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>UPenn</th>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVA</th>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UWisconsin</th>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yale</th>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                    \n",
       "Brown            89      22       13     22704        94\n",
       "CalTech         100      25        6     63575        81\n",
       "CMU              62      59        9     25026        72\n",
       "Columbia         76      24       12     31510        88\n",
       "Cornell          83      33       13     21864        90\n",
       "Dartmouth        89      23       10     32162        95\n",
       "Duke             90      30       12     31585        95\n",
       "Georgetown       74      24       12     20126        92\n",
       "Harvard          91      14       11     39525        97\n",
       "JohnsHopkins     75      44        7     58691        87\n",
       "MIT              94      30       10     34870        91\n",
       "Northwestern     85      39       11     28052        89\n",
       "NotreDame        81      42       13     15122        94\n",
       "PennState        38      54       18     10185        80\n",
       "Princeton        91      14        8     30220        95\n",
       "Purdue           28      90       19      9066        69\n",
       "Stanford         90      20       12     36450        93\n",
       "TexasA&M         49      67       25      8704        67\n",
       "UCBerkeley       95      40       17     15140        78\n",
       "UChicago         75      50       13     38380        87\n",
       "UMichigan        65      68       16     15470        85\n",
       "UPenn            80      36       11     27553        90\n",
       "UVA              77      44       14     13349        92\n",
       "UWisconsin       40      69       15     11857        71\n",
       "Yale             95      19       11     43514        96"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_.drop(\"SAT\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e28c506",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>CalTech</th>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
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       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
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       "    <tr>\n",
       "      <th>CMU</th>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cornell</th>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
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       "    <tr>\n",
       "      <th>Dartmouth</th>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duke</th>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgetown</th>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harvard</th>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JohnsHopkins</th>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIT</th>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwestern</th>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NotreDame</th>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PennState</th>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Princeton</th>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purdue</th>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanford</th>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TexasA&amp;M</th>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCBerkeley</th>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UChicago</th>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UMichigan</th>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPenn</th>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UWisconsin</th>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yale</th>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "Univ                                                          \n",
       "Brown         1310     89      22       13     22704        94\n",
       "CalTech       1415    100      25        6     63575        81\n",
       "CMU           1260     62      59        9     25026        72\n",
       "Columbia      1310     76      24       12     31510        88\n",
       "Cornell       1280     83      33       13     21864        90\n",
       "Dartmouth     1340     89      23       10     32162        95\n",
       "Duke          1315     90      30       12     31585        95\n",
       "Georgetown    1255     74      24       12     20126        92\n",
       "Harvard       1400     91      14       11     39525        97\n",
       "JohnsHopkins  1305     75      44        7     58691        87\n",
       "MIT           1380     94      30       10     34870        91\n",
       "Northwestern  1260     85      39       11     28052        89\n",
       "NotreDame     1255     81      42       13     15122        94\n",
       "PennState     1081     38      54       18     10185        80\n",
       "Princeton     1375     91      14        8     30220        95\n",
       "Purdue        1005     28      90       19      9066        69\n",
       "Stanford      1360     90      20       12     36450        93\n",
       "TexasA&M      1075     49      67       25      8704        67\n",
       "UCBerkeley    1240     95      40       17     15140        78\n",
       "UChicago      1290     75      50       13     38380        87\n",
       "UMichigan     1180     65      68       16     15470        85\n",
       "UPenn         1285     80      36       11     27553        90\n",
       "UWisconsin    1085     40      69       15     11857        71\n",
       "Yale          1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_.drop('UVA',axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aee710d",
   "metadata": {},
   "source": [
    "# Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8967828b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
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       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1266.440000</td>\n",
       "      <td>76.480000</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>12.72000</td>\n",
       "      <td>27388.000000</td>\n",
       "      <td>86.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>108.359771</td>\n",
       "      <td>19.433905</td>\n",
       "      <td>19.727308</td>\n",
       "      <td>4.06735</td>\n",
       "      <td>14424.883165</td>\n",
       "      <td>9.057778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1005.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>8704.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1240.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>15140.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1285.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>27553.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1340.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>34870.000000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1415.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>63575.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT       Top10     Accept   SFRatio      Expenses   GradRate\n",
       "count    25.000000   25.000000  25.000000  25.00000     25.000000  25.000000\n",
       "mean   1266.440000   76.480000  39.200000  12.72000  27388.000000  86.720000\n",
       "std     108.359771   19.433905  19.727308   4.06735  14424.883165   9.057778\n",
       "min    1005.000000   28.000000  14.000000   6.00000   8704.000000  67.000000\n",
       "25%    1240.000000   74.000000  24.000000  11.00000  15140.000000  81.000000\n",
       "50%    1285.000000   81.000000  36.000000  12.00000  27553.000000  90.000000\n",
       "75%    1340.000000   90.000000  50.000000  14.00000  34870.000000  94.000000\n",
       "max    1415.000000  100.000000  90.000000  25.00000  63575.000000  97.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame_.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a44b0b",
   "metadata": {},
   "source": [
    "# Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a4c8692e",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 28,
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   "source": [
    "Null_df = DataFrame([1.0,2.0,3.0,np.nan,5.0,6.0,7.0,np.nan,9.0,np.nan,np.nan,np.nan])\n",
    "Null_df"
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   "execution_count": 31,
   "id": "f6d514ad",
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    "ull_df = DataFrame((1.0,2.0,3.0,np.nan,5.0,6.0,7.0,np.nan,9.0,np.nan,np.nan,np.nan))\n",
    "ull_df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3618dc5a",
   "metadata": {},
   "outputs": [
    {
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    "ll_df = DataFrame([1,np.nan,7,np.nan],[2,5,np.nan,np.nan])\n",
    "ll_df"
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2adab820",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_1804/1291794364.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnull_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\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[0mnull_df\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
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    "nullframe_.fillna({0:1,1:2,2:3,3:4})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aca93332",
   "metadata": {},
   "outputs": [
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    "nullframe_.fillna({0:1})"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cbf0574b",
   "metadata": {},
   "outputs": [
    {
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       "3  NaN  NaN  NaN  22"
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   "source": [
    "nullframe_[2][1]=2\n",
    "nullframe_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c6607742",
   "metadata": {},
   "outputs": [
    {
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    "nullframe_"
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  {
   "cell_type": "markdown",
   "id": "851a295b",
   "metadata": {},
   "source": [
    "# Additional Practise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07d162ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>sat_reading</th>\n",
       "      <th>hs_gpa</th>\n",
       "      <th>hs_city</th>\n",
       "      <th>hs_state</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111111</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111112</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111113</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111114</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>Race/ethnicity unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>111115</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>111116</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>111117</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>111118</td>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>111119</td>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id first_name last_name date_of_birth               ethnicity gender  \\\n",
       "0  111111       John       Doe       01/2000                Hispanic      M   \n",
       "1  111112       Jane     Smith       05/2001                Hispanic      F   \n",
       "2  111113      Sarah    Thomas       21/2002                Hispanic      M   \n",
       "3  111114      Frank     Brown       13/2002  Race/ethnicity unknown      M   \n",
       "4  111115       Mike     Davis       31/2001                   White      F   \n",
       "5  111116   Jennifer    Wilson       01/2002                   Asian      M   \n",
       "6  111117    Jessica    Garcia       01/2000                   White      F   \n",
       "7  111118       Fred     Clark       17/1999                Hispanic      F   \n",
       "8  111119        Bob     Lopez       04/1998                   White      F   \n",
       "\n",
       "   sat_reading  hs_gpa      hs_city    hs_state                email  \n",
       "0          NaN    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "1          NaN    3.73     New York    New York   jsmith@example.com  \n",
       "2          NaN    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "3        210.0    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "4          NaN    3.46      Seattle  Washington   mdavis@example.com  \n",
       "5          NaN    4.24       Denver    Colorado  jwilson@example.com  \n",
       "6          NaN     NaN       Austin       Texas  jgarcia@example.com  \n",
       "7          NaN    2.54       Denver    Colorado   fclark@example.com  \n",
       "8        220.0    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list=['id','first_name','last_name','date_of_birth','ethnicity','gender','sat_reading','hs_gpa','hs_city','hs_state','email']\n",
    "students = pd.read_csv(\"C:/Users/Moin Dalvi/Downloads/students - Sheet1.csv\",\n",
    "                      usecols=col_list,nrows=9)\n",
    "students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9cdc3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<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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       "    <tr>\n",
       "      <th>id</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>111111</th>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111112</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111113</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111114</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>Race/ethnicity unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111115</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111116</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111117</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111118</th>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111119</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name date_of_birth               ethnicity gender  \\\n",
       "id                                                                         \n",
       "111111       John       Doe       01/2000                Hispanic      M   \n",
       "111112       Jane     Smith       05/2001                Hispanic      F   \n",
       "111113      Sarah    Thomas       21/2002                Hispanic      M   \n",
       "111114      Frank     Brown       13/2002  Race/ethnicity unknown      M   \n",
       "111115       Mike     Davis       31/2001                   White      F   \n",
       "111116   Jennifer    Wilson       01/2002                   Asian      M   \n",
       "111117    Jessica    Garcia       01/2000                   White      F   \n",
       "111118       Fred     Clark       17/1999                Hispanic      F   \n",
       "111119        Bob     Lopez       04/1998                   White      F   \n",
       "\n",
       "        sat_reading  hs_gpa      hs_city    hs_state                email  \n",
       "id                                                                         \n",
       "111111          NaN    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "111112          NaN    3.73     New York    New York   jsmith@example.com  \n",
       "111113          NaN    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "111114        210.0    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "111115          NaN    3.46      Seattle  Washington   mdavis@example.com  \n",
       "111116          NaN    4.24       Denver    Colorado  jwilson@example.com  \n",
       "111117          NaN     NaN       Austin       Texas  jgarcia@example.com  \n",
       "111118          NaN    2.54       Denver    Colorado   fclark@example.com  \n",
       "111119        220.0    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.set_index('id',inplace=True),\n",
    "students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b8245cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
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       "        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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>sat_reading</th>\n",
       "      <th>hs_gpa</th>\n",
       "      <th>hs_city</th>\n",
       "      <th>hs_state</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</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>111111</th>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111112</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111113</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111114</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>Race/ethnicity unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111115</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111116</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111117</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111118</th>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111119</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name date_of_birth               ethnicity gender  \\\n",
       "ID                                                                         \n",
       "111111       John       Doe       01/2000                Hispanic      M   \n",
       "111112       Jane     Smith       05/2001                Hispanic      F   \n",
       "111113      Sarah    Thomas       21/2002                Hispanic      M   \n",
       "111114      Frank     Brown       13/2002  Race/ethnicity unknown      M   \n",
       "111115       Mike     Davis       31/2001                   White      F   \n",
       "111116   Jennifer    Wilson       01/2002                   Asian      M   \n",
       "111117    Jessica    Garcia       01/2000                   White      F   \n",
       "111118       Fred     Clark       17/1999                Hispanic      F   \n",
       "111119        Bob     Lopez       04/1998                   White      F   \n",
       "\n",
       "        sat_reading  hs_gpa      hs_city    hs_state                email  \n",
       "ID                                                                         \n",
       "111111          NaN    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "111112          NaN    3.73     New York    New York   jsmith@example.com  \n",
       "111113          NaN    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "111114        210.0    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "111115          NaN    3.46      Seattle  Washington   mdavis@example.com  \n",
       "111116          NaN    4.24       Denver    Colorado  jwilson@example.com  \n",
       "111117          NaN     NaN       Austin       Texas  jgarcia@example.com  \n",
       "111118          NaN    2.54       Denver    Colorado   fclark@example.com  \n",
       "111119        220.0    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.index.names=['ID']\n",
    "students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a550b958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>sat_reading</th>\n",
       "      <th>hs_gpa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>111115.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>3.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.738613</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>0.609473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>111111.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>2.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>111113.000000</td>\n",
       "      <td>212.500000</td>\n",
       "      <td>2.692500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>111115.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>3.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111117.000000</td>\n",
       "      <td>217.500000</td>\n",
       "      <td>3.692500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>111119.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>4.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  sat_reading    hs_gpa\n",
       "count       9.000000     2.000000  8.000000\n",
       "mean   111115.000000   215.000000  3.280000\n",
       "std         2.738613     7.071068  0.609473\n",
       "min    111111.000000   210.000000  2.540000\n",
       "25%    111113.000000   212.500000  2.692500\n",
       "50%    111115.000000   215.000000  3.350000\n",
       "75%    111117.000000   217.500000  3.692500\n",
       "max    111119.000000   220.000000  4.240000"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "20676e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215.0"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_students=students['sat_reading'].mean()\n",
    "mean_students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ed1ca173",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First_Name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
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       "    <tr>\n",
       "      <th>id</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>111111</th>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111112</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111113</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111114</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111115</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111116</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111117</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111118</th>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111119</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       First_Name last_name date_of_birth ethnicity gender  sat_reading  \\\n",
       "id                                                                        \n",
       "111111       John       Doe       01/2000  Hispanic      M          NaN   \n",
       "111112       Jane     Smith       05/2001  Hispanic      F          NaN   \n",
       "111113      Sarah    Thomas       21/2002  Hispanic      M          NaN   \n",
       "111114      Frank     Brown       13/2002       NaN      M        210.0   \n",
       "111115       Mike     Davis       31/2001     White      F          NaN   \n",
       "111116   Jennifer    Wilson       01/2002     Asian      M          NaN   \n",
       "111117    Jessica    Garcia       01/2000     White      F          NaN   \n",
       "111118       Fred     Clark       17/1999  Hispanic      F          NaN   \n",
       "111119        Bob     Lopez       04/1998     White      F        220.0   \n",
       "\n",
       "        hs_gpa      hs_city    hs_state                email  \n",
       "id                                                            \n",
       "111111    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "111112    3.73     New York    New York   jsmith@example.com  \n",
       "111113    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "111114    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "111115    3.46      Seattle  Washington   mdavis@example.com  \n",
       "111116    4.24       Denver    Colorado  jwilson@example.com  \n",
       "111117     NaN       Austin       Texas  jgarcia@example.com  \n",
       "111118    2.54       Denver    Colorado   fclark@example.com  \n",
       "111119    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students.rename(columns={'first_name':'First Name','last_name'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "687b3735",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated Sat Readings:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>sat_reading</th>\n",
       "      <th>hs_gpa</th>\n",
       "      <th>hs_city</th>\n",
       "      <th>hs_state</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</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>111111</th>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111112</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111113</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111114</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>Race/ethnicity unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111115</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111116</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111117</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111118</th>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111119</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name date_of_birth               ethnicity gender  \\\n",
       "ID                                                                         \n",
       "111111       John       Doe       01/2000                Hispanic      M   \n",
       "111112       Jane     Smith       05/2001                Hispanic      F   \n",
       "111113      Sarah    Thomas       21/2002                Hispanic      M   \n",
       "111114      Frank     Brown       13/2002  Race/ethnicity unknown      M   \n",
       "111115       Mike     Davis       31/2001                   White      F   \n",
       "111116   Jennifer    Wilson       01/2002                   Asian      M   \n",
       "111117    Jessica    Garcia       01/2000                   White      F   \n",
       "111118       Fred     Clark       17/1999                Hispanic      F   \n",
       "111119        Bob     Lopez       04/1998                   White      F   \n",
       "\n",
       "        sat_reading  hs_gpa      hs_city    hs_state                email  \n",
       "ID                                                                         \n",
       "111111        215.0    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "111112        215.0    3.73     New York    New York   jsmith@example.com  \n",
       "111113        215.0    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "111114        210.0    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "111115        215.0    3.46      Seattle  Washington   mdavis@example.com  \n",
       "111116        215.0    4.24       Denver    Colorado  jwilson@example.com  \n",
       "111117        215.0     NaN       Austin       Texas  jgarcia@example.com  \n",
       "111118        215.0    2.54       Denver    Colorado   fclark@example.com  \n",
       "111119        220.0    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "students['sat_reading'].fillna(value=mean_students,inplace=True)\n",
    "print('Updated Sat Readings:')\n",
    "display(students)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "33f57ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>sat_reading</th>\n",
       "      <th>hs_gpa</th>\n",
       "      <th>hs_city</th>\n",
       "      <th>hs_state</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</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>111111</th>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111112</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111113</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111114</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111115</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111116</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111117</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111118</th>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111119</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name date_of_birth ethnicity gender  sat_reading  \\\n",
       "ID                                                                        \n",
       "111111       John       Doe       01/2000  Hispanic      M          NaN   \n",
       "111112       Jane     Smith       05/2001  Hispanic      F          NaN   \n",
       "111113      Sarah    Thomas       21/2002  Hispanic      M          NaN   \n",
       "111114      Frank     Brown       13/2002       NaN      M        210.0   \n",
       "111115       Mike     Davis       31/2001     White      F          NaN   \n",
       "111116   Jennifer    Wilson       01/2002     Asian      M          NaN   \n",
       "111117    Jessica    Garcia       01/2000     White      F          NaN   \n",
       "111118       Fred     Clark       17/1999  Hispanic      F          NaN   \n",
       "111119        Bob     Lopez       04/1998     White      F        220.0   \n",
       "\n",
       "        hs_gpa      hs_city    hs_state                email  \n",
       "ID                                                            \n",
       "111111    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "111112    3.73     New York    New York   jsmith@example.com  \n",
       "111113    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "111114    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "111115    3.46      Seattle  Washington   mdavis@example.com  \n",
       "111116    4.24       Denver    Colorado  jwilson@example.com  \n",
       "111117     NaN       Austin       Texas  jgarcia@example.com  \n",
       "111118    2.54       Denver    Colorado   fclark@example.com  \n",
       "111119    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students[\"ethnicity\"][111114]=np.nan\n",
    "students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "9795c2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated HS GPA:\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>sat_reading</th>\n",
       "      <th>hs_gpa</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111111</th>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.71</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>jdoe@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111112</th>\n",
       "      <td>Jane</td>\n",
       "      <td>Smith</td>\n",
       "      <td>05/2001</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.73</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>jsmith@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111113</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>21/2002</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.64</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>sthomas@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111114</th>\n",
       "      <td>Frank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>13/2002</td>\n",
       "      <td>Race/ethnicity unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Pheonix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>fbrown@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111115</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Davis</td>\n",
       "      <td>31/2001</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.46</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>mdavis@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111116</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Wilson</td>\n",
       "      <td>01/2002</td>\n",
       "      <td>Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>jwilson@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111117</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>01/2000</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>jgarcia@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111118</th>\n",
       "      <td>Fred</td>\n",
       "      <td>Clark</td>\n",
       "      <td>17/1999</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>fclark@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111119</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>04/1998</td>\n",
       "      <td>White</td>\n",
       "      <td>F</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>blopez@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_name last_name date_of_birth               ethnicity gender  \\\n",
       "ID                                                                         \n",
       "111111       John       Doe       01/2000                Hispanic      M   \n",
       "111112       Jane     Smith       05/2001                Hispanic      F   \n",
       "111113      Sarah    Thomas       21/2002                Hispanic      M   \n",
       "111114      Frank     Brown       13/2002  Race/ethnicity unknown      M   \n",
       "111115       Mike     Davis       31/2001                   White      F   \n",
       "111116   Jennifer    Wilson       01/2002                   Asian      M   \n",
       "111117    Jessica    Garcia       01/2000                   White      F   \n",
       "111118       Fred     Clark       17/1999                Hispanic      F   \n",
       "111119        Bob     Lopez       04/1998                   White      F   \n",
       "\n",
       "        sat_reading  hs_gpa      hs_city    hs_state                email  \n",
       "ID                                                                         \n",
       "111111        215.0    2.71  Albuquerque  New Mexico     jdoe@example.com  \n",
       "111112        215.0    3.73     New York    New York   jsmith@example.com  \n",
       "111113        215.0    2.64      Pheonix     Arizona  sthomas@example.com  \n",
       "111114        210.0    3.68      Pheonix     Arizona   fbrown@example.com  \n",
       "111115        215.0    3.46      Seattle  Washington   mdavis@example.com  \n",
       "111116        215.0    4.24       Denver    Colorado  jwilson@example.com  \n",
       "111117        215.0    3.28       Austin       Texas  jgarcia@example.com  \n",
       "111118        215.0    2.54       Denver    Colorado   fclark@example.com  \n",
       "111119        220.0    3.24       Denver    Colorado   blopez@example.com  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_hsgpa=students['hs_gpa'].mean()\n",
    "students['hs_gpa'].fillna(value=mean_hsgpa,inplace=True)\n",
    "print('Updated HS GPA:')\n",
    "students"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32ccf3e",
   "metadata": {},
   "source": [
    "# Merge Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce92c96c",
   "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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mission Impossible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Blade Runner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ghost Rider</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id         movie_title\n",
       "0         1         Toy Story 2\n",
       "1         2  Mission Impossible\n",
       "2         3              Avatar\n",
       "3         4        Blade Runner\n",
       "4         5         Ghost Rider"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies=pd.DataFrame({'movie_id':[1,2,3,4,5],\n",
    "                    'movie_title':['Toy Story 2','Mission Impossible','Avatar','Blade Runner','Ghost Rider']})\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c56f94b6",
   "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>mem_id</th>\n",
       "      <th>name</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id   name  movie_id\n",
       "0       1    Tom       1.0\n",
       "1       2   John       2.0\n",
       "2       3   Aron       3.0\n",
       "3       4  Bobby       NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members= DataFrame({'mem_id':[1,2,3,4],\n",
    "                    'name':['Tom','John','Aron','Bobby'],\n",
    "                    'movie_id':[1,2,3,nan]})\n",
    "members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5ae06c8",
   "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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>mem_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mission Impossible</td>\n",
       "      <td>2.0</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Aron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Blade Runner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ghost Rider</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id         movie_title  mem_id  name\n",
       "0         1         Toy Story 2     1.0   Tom\n",
       "1         2  Mission Impossible     2.0  John\n",
       "2         3              Avatar     3.0  Aron\n",
       "3         4        Blade Runner     NaN   NaN\n",
       "4         5         Ghost Rider     NaN   NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(movies,members, on='movie_id',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c43e5ba0",
   "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>mem_id</th>\n",
       "      <th>name</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mission Impossible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Blade Runner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Ghost Rider</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id  name  movie_id         movie_title\n",
       "0     1.0   Tom       1.0         Toy Story 2\n",
       "1     2.0  John       2.0  Mission Impossible\n",
       "2     3.0  Aron       3.0              Avatar\n",
       "3     NaN   NaN       4.0        Blade Runner\n",
       "4     NaN   NaN       5.0         Ghost Rider"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(members,movies,on='movie_id',how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9a16d6b",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mem_id</th>\n",
       "      <th>name</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mission Impossible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id   name  movie_id         movie_title\n",
       "0       1    Tom       1.0         Toy Story 2\n",
       "1       2   John       2.0  Mission Impossible\n",
       "2       3   Aron       3.0              Avatar\n",
       "3       4  Bobby       NaN                 NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(members,movies,on='movie_id',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6cbb0a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "    .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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>mem_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Mission Impossible</td>\n",
       "      <td>2.0</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Aron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Blade Runner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Ghost Rider</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bobby</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id         movie_title  mem_id   name\n",
       "0       1.0         Toy Story 2     1.0    Tom\n",
       "1       2.0  Mission Impossible     2.0   John\n",
       "2       3.0              Avatar     3.0   Aron\n",
       "3       4.0        Blade Runner     NaN    NaN\n",
       "4       5.0         Ghost Rider     NaN    NaN\n",
       "5       NaN                 NaN     4.0  Bobby"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(movies,members,on='movie_id',how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3412d871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>mem_id</th>\n",
       "      <th>name</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mission Impossible</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Blade Runner</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Ghost Rider</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id   name  movie_id         movie_title      _merge\n",
       "0     1.0    Tom       1.0         Toy Story 2        both\n",
       "1     2.0   John       2.0  Mission Impossible        both\n",
       "2     3.0   Aron       3.0              Avatar        both\n",
       "3     4.0  Bobby       NaN                 NaN   left_only\n",
       "4     NaN    NaN       4.0        Blade Runner  right_only\n",
       "5     NaN    NaN       5.0         Ghost Rider  right_only"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(members,movies,on='movie_id',how='outer',indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "089011e3",
   "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",
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       "\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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>mem_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>1</td>\n",
       "      <td>Tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mission Impossible</td>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>3</td>\n",
       "      <td>Aron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id         movie_title  mem_id  name\n",
       "0         1         Toy Story 2       1   Tom\n",
       "1         2  Mission Impossible       2  John\n",
       "2         3              Avatar       3  Aron"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(movies,members,on='movie_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c8e7b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mem_id</th>\n",
       "      <th>name</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mission Impossible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id  name  movie_id         movie_title\n",
       "0       1   Tom       1.0         Toy Story 2\n",
       "1       2  John       2.0  Mission Impossible\n",
       "2       3  Aron       3.0              Avatar"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(members,movies,how='inner',on='movie_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ce32966",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_mem={'mem_id':5,'name':'Amy','movie_id':3}\n",
    "members_updated=members.append(add_mem,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bc51ca7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>mem_id</th>\n",
       "      <th>name</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Amy</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id   name  movie_id\n",
       "0       1    Tom       1.0\n",
       "1       2   John       2.0\n",
       "2       3   Aron       3.0\n",
       "3       4  Bobby       NaN\n",
       "4       5    Amy       3.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "380b2705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Tom</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Toy Story 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mission Impossible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Aron</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Amy</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mem_id   name  movie_id         movie_title\n",
       "0       1    Tom       1.0         Toy Story 2\n",
       "1       2   John       2.0  Mission Impossible\n",
       "2       3   Aron       3.0              Avatar\n",
       "3       4  Bobby       NaN                 NaN\n",
       "4       5    Amy       3.0              Avatar"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(members_updated,movies,on='movie_id',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "43183b0c",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 2</td>\n",
       "      <td>Tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mission Impossible</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Aron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Amy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          movie_title  name\n",
       "0         Toy Story 2   Tom\n",
       "1  Mission Impossible  John\n",
       "2              Avatar  Aron\n",
       "3              Avatar   Amy"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(members_updated,movies,on='movie_id')[['movie_title','name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c703bcb",
   "metadata": {},
   "source": [
    "# Handling Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15b3c7c2",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1  col2\n",
       "0    A     2\n",
       "1    A     2\n",
       "2    A     2\n",
       "3    B     3\n",
       "4    B     3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup = DataFrame({'col1':['A','A','A','B','B'],\n",
    "                'col2':[2,2,2,3,3]})\n",
    "dup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a368ed75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c44af88a",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1  col2\n",
       "0    A     2\n",
       "1    A     2\n",
       "2    B     2\n",
       "3    B     3\n",
       "4    B     3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup['col1'][2]='B'\n",
    "dup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d03004e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup.duplicated('col1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ca4dce13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup.duplicated('col2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "826bc3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "  col1  col2\n",
       "0    A     2\n",
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       "3    B     3"
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     "metadata": {},
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   "execution_count": 68,
   "id": "d8a25db3",
   "metadata": {},
   "outputs": [
    {
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       "  col1  col2\n",
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     "metadata": {},
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    "dup.drop_duplicates('col2')"
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   "cell_type": "code",
   "execution_count": 49,
   "id": "af5e53e1",
   "metadata": {},
   "outputs": [
    {
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       "  col1  col2\n",
       "1    A     2\n",
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       "4    B     3"
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     "execution_count": 49,
     "metadata": {},
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    "dup.drop_duplicates(keep='last')"
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   "execution_count": 50,
   "id": "982aa7cb",
   "metadata": {},
   "outputs": [
    {
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       "  col1  col2\n",
       "0    A     2\n",
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       "3    B     3"
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    "dup.drop_duplicates(keep='first')"
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   "id": "d728f724",
   "metadata": {},
   "outputs": [
    {
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       "  col1  col2\n",
       "2    B     2"
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     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dup.drop_duplicates(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8cd03434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>6</td>\n",
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       "     Name  ID\n",
       "0   Surya   1\n",
       "1   Surya   1\n",
       "2  Roshan   2\n",
       "3  Roshan   2\n",
       "4    Neha   4\n",
       "5  Amreen   4\n",
       "6  Amreen   4\n",
       "7    Sana   6\n",
       "8  Saleem   7"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate=DataFrame({'Name':['Surya','Surya','Roshan','Roshan','Neha','Amreen','Amreen','Sana','Saleem'],\n",
    "                    'ID':[1,1,2,2,4,4,4,6,7]})\n",
    "duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "24d0709d",
   "metadata": {},
   "outputs": [
    {
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       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
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       "6     True\n",
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       "8    False\n",
       "dtype: bool"
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     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "duplicate.duplicated()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ceb531d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>AmReen</td>\n",
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       "      <td>Sana</td>\n",
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       "      <th>8</th>\n",
       "      <td>Saleem</td>\n",
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      "text/plain": [
       "     Name  ID\n",
       "0   Surya   1\n",
       "1   surya   1\n",
       "2  Roshan   2\n",
       "3  roshaN   2\n",
       "4    Neha   4\n",
       "5  Amreen   4\n",
       "6  AmReen   4\n",
       "7    Sana   6\n",
       "8  Saleem   7"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicat=DataFrame({'Name':['Surya','surya','Roshan','roshaN','Neha','Amreen','AmReen','Sana','Saleem'],\n",
    "                    'ID':[1,1,2,2,4,4,4,6,7]})\n",
    "duplicat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b827a247",
   "metadata": {},
   "outputs": [
    {
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       "0    False\n",
       "1    False\n",
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       "6    False\n",
       "7    False\n",
       "8    False\n",
       "dtype: bool"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicat.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "05682129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>6</th>\n",
       "      <td>AmReen</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sana</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Saleem</td>\n",
       "      <td>7</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     Name  ID\n",
       "0   Surya   1\n",
       "1   surya   1\n",
       "2  Roshan   2\n",
       "3  roshaN   2\n",
       "4    Neha   4\n",
       "5  Amreen   5\n",
       "6  AmReen   5\n",
       "7    Sana   6\n",
       "8  Saleem   7"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplica=DataFrame({'Name':['Surya','surya','Roshan','roshaN','Neha','Amreen','AmReen','Sana','Saleem'],\n",
    "                    'ID':[1,1,2,2,4,5,5,6,7]})\n",
    "duplica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6e7a53ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7    False\n",
       "8    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplica.duplicated('ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cdcbfa56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "  col1  col2\n",
       "0    A     2\n",
       "3    B     3"
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     "execution_count": 5,
     "metadata": {},
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    "dup.drop_duplicates('col1')"
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   "cell_type": "code",
   "execution_count": 6,
   "id": "263652d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "  col1  col2\n",
       "0    A     2\n",
       "3    B     3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup.drop_duplicates('col2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851b48a9",
   "metadata": {},
   "source": [
    "# Replace Duplicated Value with Nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d41d19c1",
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "  col1  col2\n",
       "0    A     2\n",
       "1  NaN     2\n",
       "2  NaN     2\n",
       "3    B     3\n",
       "4  NaN     3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup.loc[dup.col2.duplicated(),'col1']=nan\n",
    "dup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "093daeb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1  col2\n",
       "0    A   2.0\n",
       "1    A   NaN\n",
       "2    A   NaN\n",
       "3    B   3.0\n",
       "4    B   NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup.loc[dup.col1.duplicated(),'col2']=nan\n",
    "dup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85db589f",
   "metadata": {},
   "source": [
    "# Rename columns and Index of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79efaa46",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popu</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    popu  size  year\n",
       "SF     0     1     2\n",
       "NY     3     4     5\n",
       "LA     6     7     8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_name=DataFrame(np.arange(9).reshape(3,3),columns=['popu','size','year'],index=['SF','NY','LA'])\n",
    "re_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63376fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    population  size  year\n",
       "SF           0     1     2\n",
       "NY           3     4     5\n",
       "LA           6     7     8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_name.rename(columns={'popu':'population'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d780cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "re_name.rename(columns={'popu':'population'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b382df49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>size</th>\n",
       "      <th>year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "    popu  size  year\n",
       "FL     0     1     2\n",
       "NY     3     4     5\n",
       "LA     6     7     8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "re_name.rename(index={'SF':'FL'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50c41709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>size</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Population  size  Year\n",
       "FL           0     1     2\n",
       "NY           3     4     5\n",
       "AL           6     7     8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_name.rename(columns={'popu':'Population','year':'Year'},index={'SF':'FL','LA':'AL'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27026015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Population  Size  Year\n",
       "SF           0     1     2\n",
       "NY           3     4     5\n",
       "LA           6     7     8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_name.columns=['Population','Size','Year']\n",
    "re_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "20eaee42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popu</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    popu  size  year\n",
       "SF     0     1     2\n",
       "NY     3     4     5\n",
       "LA     6     7     8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_name.set_axis(['popu','size','year'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395468eb",
   "metadata": {},
   "source": [
    " # Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ef0d145",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_by= pd.read_csv('Salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a730e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>discipline</th>\n",
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       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
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       "      <th>1</th>\n",
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       "      <td>93000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ce27ef7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "grp_by=group_by.groupby('rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1b3ac3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000028342B21D30>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11044015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AssocProf'"
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>103750</td>\n",
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       "    <tr>\n",
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       "      <td>AssocProf</td>\n",
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      "text/plain": [
       "         rank discipline     sex  salary\n",
       "6   AssocProf          A    Male   81285\n",
       "30  AssocProf          B    Male  107008\n",
       "34  AssocProf          B    Male  119800\n",
       "41  AssocProf          A  Female   74830\n",
       "47  AssocProf          B  Female  103613\n",
       "54  AssocProf          A  Female   62884\n",
       "56  AssocProf          A  Female   77500\n",
       "59  AssocProf          B  Female  103994\n",
       "61  AssocProf          B  Female  103750\n",
       "62  AssocProf          B  Female  109650\n",
       "64  AssocProf          A  Female   73300\n",
       "71  AssocProf          B  Female   71065\n",
       "74  AssocProf          B  Female  104542"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'AsstProf'"
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       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
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       "      <th>42</th>\n",
       "      <td>AsstProf</td>\n",
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       "      <th>46</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
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       "      <th>50</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
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       "      <td>97032</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>73500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
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       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>63100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>77500</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>78500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        rank discipline     sex  salary\n",
       "12  AsstProf          B    Male   88000\n",
       "16  AsstProf          B    Male   75044\n",
       "17  AsstProf          B    Male   92000\n",
       "20  AsstProf          B    Male   92000\n",
       "22  AsstProf          A    Male   73000\n",
       "23  AsstProf          A    Male   85000\n",
       "28  AsstProf          B    Male   91300\n",
       "38  AsstProf          B    Male   91000\n",
       "42  AsstProf          B  Female   80225\n",
       "43  AsstProf          B  Female   77000\n",
       "46  AsstProf          B  Female   74692\n",
       "50  AsstProf          B  Female   97032\n",
       "53  AsstProf          A  Female   73500\n",
       "55  AsstProf          A  Female   72500\n",
       "57  AsstProf          A  Female   72500\n",
       "60  AsstProf          B  Female   92000\n",
       "66  AsstProf          A  Female   63100\n",
       "68  AsstProf          A  Female   77500\n",
       "70  AsstProf          A  Female   78500"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "'Prof'"
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       "      <th>5</th>\n",
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       "      <td>A</td>\n",
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       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
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       "      <td>A</td>\n",
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       "      <td>94350</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
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       "      <th>13</th>\n",
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       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
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       "      <th>14</th>\n",
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       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>107300</td>\n",
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       "      <th>19</th>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>103106</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
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       "      <th>25</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>99418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>103275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>102000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>111512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>91000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>117555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>90450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>116450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline     sex  salary\n",
       "0   Prof          B    Male  186960\n",
       "1   Prof          A    Male   93000\n",
       "2   Prof          A    Male  110515\n",
       "3   Prof          A    Male  131205\n",
       "4   Prof          B    Male  104800\n",
       "5   Prof          A    Male  122400\n",
       "7   Prof          A    Male  126300\n",
       "8   Prof          A    Male   94350\n",
       "9   Prof          A    Male   57800\n",
       "10  Prof          B    Male  128250\n",
       "11  Prof          B    Male  134778\n",
       "13  Prof          B    Male  162200\n",
       "14  Prof          B    Male  153750\n",
       "15  Prof          B    Male  150480\n",
       "18  Prof          A    Male  107300\n",
       "19  Prof          A    Male  150500\n",
       "21  Prof          A    Male  103106\n",
       "24  Prof          A    Male   91100\n",
       "25  Prof          B    Male   99418\n",
       "26  Prof          A    Male  148750\n",
       "27  Prof          A    Male  155865\n",
       "29  Prof          B    Male  123683\n",
       "31  Prof          B    Male  155750\n",
       "32  Prof          A    Male  103275\n",
       "33  Prof          B    Male  120000\n",
       "35  Prof          B    Male  126933\n",
       "36  Prof          B    Male  146856\n",
       "37  Prof          A    Male  102000\n",
       "39  Prof          B  Female  129000\n",
       "40  Prof          A  Female  137000\n",
       "44  Prof          B  Female  151768\n",
       "45  Prof          B  Female  140096\n",
       "48  Prof          B  Female  111512\n",
       "49  Prof          B  Female  122960\n",
       "51  Prof          B  Female  127512\n",
       "52  Prof          A  Female  105000\n",
       "58  Prof          B  Female  144651\n",
       "63  Prof          A  Female   91000\n",
       "65  Prof          A  Female  117555\n",
       "67  Prof          A  Female   90450\n",
       "69  Prof          A  Female  116450\n",
       "72  Prof          B  Female  161101\n",
       "73  Prof          B  Female  105450\n",
       "75  Prof          B  Female  124312\n",
       "76  Prof          A  Female  109954\n",
       "77  Prof          A  Female  109646"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for rank,group in grp_by:\n",
    "    display(rank,group[['rank','discipline','sex','salary']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b2b155c9",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>107300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>103106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>91100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>99418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>103275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>102000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>151768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>111512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "      <td>91000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>117555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>90450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>116450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "0   Prof          B   56       49    Male  186960\n",
       "1   Prof          A   12        6    Male   93000\n",
       "2   Prof          A   23       20    Male  110515\n",
       "3   Prof          A   40       31    Male  131205\n",
       "4   Prof          B   20       18    Male  104800\n",
       "5   Prof          A   20       20    Male  122400\n",
       "7   Prof          A   18       18    Male  126300\n",
       "8   Prof          A   29       19    Male   94350\n",
       "9   Prof          A   51       51    Male   57800\n",
       "10  Prof          B   39       33    Male  128250\n",
       "11  Prof          B   23       23    Male  134778\n",
       "13  Prof          B   35       33    Male  162200\n",
       "14  Prof          B   25       19    Male  153750\n",
       "15  Prof          B   17        3    Male  150480\n",
       "18  Prof          A   19        7    Male  107300\n",
       "19  Prof          A   29       27    Male  150500\n",
       "21  Prof          A   33       30    Male  103106\n",
       "24  Prof          A   30       23    Male   91100\n",
       "25  Prof          B   35       31    Male   99418\n",
       "26  Prof          A   38       19    Male  148750\n",
       "27  Prof          A   45       43    Male  155865\n",
       "29  Prof          B   21       20    Male  123683\n",
       "31  Prof          B   22       21    Male  155750\n",
       "32  Prof          A   27       19    Male  103275\n",
       "33  Prof          B   18       18    Male  120000\n",
       "35  Prof          B   28       23    Male  126933\n",
       "36  Prof          B   45       45    Male  146856\n",
       "37  Prof          A   20        8    Male  102000\n",
       "39  Prof          B   18       18  Female  129000\n",
       "40  Prof          A   39       36  Female  137000\n",
       "44  Prof          B   23       19  Female  151768\n",
       "45  Prof          B   25       25  Female  140096\n",
       "48  Prof          B   17       17  Female  111512\n",
       "49  Prof          B   17       18  Female  122960\n",
       "51  Prof          B   20       14  Female  127512\n",
       "52  Prof          A   12        0  Female  105000\n",
       "58  Prof          B   36       26  Female  144651\n",
       "63  Prof          A   29       27  Female   91000\n",
       "65  Prof          A   36       19  Female  117555\n",
       "67  Prof          A   17       11  Female   90450\n",
       "69  Prof          A   28        7  Female  116450\n",
       "72  Prof          B   24       15  Female  161101\n",
       "73  Prof          B   18       10  Female  105450\n",
       "75  Prof          B   17       17  Female  124312\n",
       "76  Prof          A   28       14  Female  109954\n",
       "77  Prof          A   23       15  Female  109646"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_by.get_group('Prof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73f926ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "sexs=group_by.groupby(['rank','sex']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "404e20b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('AssocProf', 'Female')"
      ]
     },
     "metadata": {},
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       "      <td>AssocProf</td>\n",
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       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>74830</td>\n",
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       "      <th>47</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
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       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>103613</td>\n",
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       "      <th>54</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
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       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>77500</td>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
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       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>103994</td>\n",
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       "      <th>61</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>103750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>109650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>A</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>73300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>71065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "41  AssocProf          A   13        8  Female   74830\n",
       "47  AssocProf          B   11       11  Female  103613\n",
       "54  AssocProf          A   25       22  Female   62884\n",
       "56  AssocProf          A   10        8  Female   77500\n",
       "59  AssocProf          B   12       10  Female  103994\n",
       "61  AssocProf          B   13       10  Female  103750\n",
       "62  AssocProf          B   14        7  Female  109650\n",
       "64  AssocProf          A   26       24  Female   73300\n",
       "71  AssocProf          B   12        9  Female   71065\n",
       "74  AssocProf          B   19        6  Female  104542"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('AssocProf', 'Male')"
      ]
     },
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       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
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       "      <td>AssocProf</td>\n",
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       "         rank discipline  phd  service   sex  salary\n",
       "6   AssocProf          A   20       17  Male   81285\n",
       "30  AssocProf          B    9        7  Male  107008\n",
       "34  AssocProf          B   12        8  Male  119800"
      ]
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      "text/plain": [
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      ]
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       "      <th>46</th>\n",
       "      <td>AsstProf</td>\n",
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       "      <th>53</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
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       "      <th>55</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>63100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>77500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>78500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service     sex  salary\n",
       "42  AsstProf          B    4        2  Female   80225\n",
       "43  AsstProf          B    5        0  Female   77000\n",
       "46  AsstProf          B   11        3  Female   74692\n",
       "50  AsstProf          B   10        5  Female   97032\n",
       "53  AsstProf          A    5        3  Female   73500\n",
       "55  AsstProf          A    2        0  Female   72500\n",
       "57  AsstProf          A    3        1  Female   72500\n",
       "60  AsstProf          B    3        3  Female   92000\n",
       "66  AsstProf          A    7        6  Female   63100\n",
       "68  AsstProf          A    4        2  Female   77500\n",
       "70  AsstProf          A    8        3  Female   78500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('AsstProf', 'Male')"
      ]
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       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>91300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>91000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service   sex  salary\n",
       "12  AsstProf          B    1        0  Male   88000\n",
       "16  AsstProf          B    8        3  Male   75044\n",
       "17  AsstProf          B    4        0  Male   92000\n",
       "20  AsstProf          B    4        4  Male   92000\n",
       "22  AsstProf          A    4        2  Male   73000\n",
       "23  AsstProf          A    2        0  Male   85000\n",
       "28  AsstProf          B    7        2  Male   91300\n",
       "38  AsstProf          B    4        3  Male   91000"
      ]
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      "text/plain": [
       "('Prof', 'Female')"
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       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>B</td>\n",
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       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>140096</td>\n",
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       "      <th>48</th>\n",
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       "      <td>Female</td>\n",
       "      <td>122960</td>\n",
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       "      <th>51</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>127512</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>105000</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
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       "      <td>Female</td>\n",
       "      <td>144651</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "      <td>91000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>117555</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>90450</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>116450</td>\n",
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       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>161101</td>\n",
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       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    rank discipline  phd  service     sex  salary\n",
       "39  Prof          B   18       18  Female  129000\n",
       "40  Prof          A   39       36  Female  137000\n",
       "44  Prof          B   23       19  Female  151768\n",
       "45  Prof          B   25       25  Female  140096\n",
       "48  Prof          B   17       17  Female  111512\n",
       "49  Prof          B   17       18  Female  122960\n",
       "51  Prof          B   20       14  Female  127512\n",
       "52  Prof          A   12        0  Female  105000\n",
       "58  Prof          B   36       26  Female  144651\n",
       "63  Prof          A   29       27  Female   91000\n",
       "65  Prof          A   36       19  Female  117555\n",
       "67  Prof          A   17       11  Female   90450\n",
       "69  Prof          A   28        7  Female  116450\n",
       "72  Prof          B   24       15  Female  161101\n",
       "73  Prof          B   18       10  Female  105450\n",
       "75  Prof          B   17       17  Female  124312\n",
       "76  Prof          A   28       14  Female  109954\n",
       "77  Prof          A   23       15  Female  109646"
      ]
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      "text/plain": [
       "('Prof', 'Male')"
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       "      <td>Male</td>\n",
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       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
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       "      <td>A</td>\n",
       "      <td>40</td>\n",
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       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
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       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>122400</td>\n",
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       "      <th>7</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>126300</td>\n",
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       "      <th>8</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>94350</td>\n",
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       "      <th>9</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>57800</td>\n",
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       "      <th>10</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>128250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>134778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>162200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>150480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>107300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>150500</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>103106</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>91100</td>\n",
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       "      <th>25</th>\n",
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       "      <td>B</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>99418</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>148750</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>155865</td>\n",
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       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>123683</td>\n",
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       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>155750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>103275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>126933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>146856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>102000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    rank discipline  phd  service   sex  salary\n",
       "0   Prof          B   56       49  Male  186960\n",
       "1   Prof          A   12        6  Male   93000\n",
       "2   Prof          A   23       20  Male  110515\n",
       "3   Prof          A   40       31  Male  131205\n",
       "4   Prof          B   20       18  Male  104800\n",
       "5   Prof          A   20       20  Male  122400\n",
       "7   Prof          A   18       18  Male  126300\n",
       "8   Prof          A   29       19  Male   94350\n",
       "9   Prof          A   51       51  Male   57800\n",
       "10  Prof          B   39       33  Male  128250\n",
       "11  Prof          B   23       23  Male  134778\n",
       "13  Prof          B   35       33  Male  162200\n",
       "14  Prof          B   25       19  Male  153750\n",
       "15  Prof          B   17        3  Male  150480\n",
       "18  Prof          A   19        7  Male  107300\n",
       "19  Prof          A   29       27  Male  150500\n",
       "21  Prof          A   33       30  Male  103106\n",
       "24  Prof          A   30       23  Male   91100\n",
       "25  Prof          B   35       31  Male   99418\n",
       "26  Prof          A   38       19  Male  148750\n",
       "27  Prof          A   45       43  Male  155865\n",
       "29  Prof          B   21       20  Male  123683\n",
       "31  Prof          B   22       21  Male  155750\n",
       "32  Prof          A   27       19  Male  103275\n",
       "33  Prof          B   18       18  Male  120000\n",
       "35  Prof          B   28       23  Male  126933\n",
       "36  Prof          B   45       45  Male  146856\n",
       "37  Prof          A   20        8  Male  102000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sex,group in sexs:\n",
    "    display(sex, group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "95ef7789",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>80225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>77000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>74692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>97032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>73500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>72500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>63100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>77500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>AsstProf</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>78500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank discipline  phd  service     sex  salary\n",
       "42  AsstProf          B    4        2  Female   80225\n",
       "43  AsstProf          B    5        0  Female   77000\n",
       "46  AsstProf          B   11        3  Female   74692\n",
       "50  AsstProf          B   10        5  Female   97032\n",
       "53  AsstProf          A    5        3  Female   73500\n",
       "55  AsstProf          A    2        0  Female   72500\n",
       "57  AsstProf          A    3        1  Female   72500\n",
       "60  AsstProf          B    3        3  Female   92000\n",
       "66  AsstProf          A    7        6  Female   63100\n",
       "68  AsstProf          A    4        2  Female   77500\n",
       "70  AsstProf          A    8        3  Female   78500"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sexs.get_group(('AsstProf','Female'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97a8eced",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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