Sai_vinay_Nandigam_Homework_01.ipynb

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   "source": [
    "<div class='heading'>\n",
    "    <div style='float:left;'><h1>CPSC 4300/6300: Applied Data Science</h1></div>\n",
    "    <img style=\"float: right; padding-right: 10px; width: 65px\" src=\"https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/images/clemson_paw.png\"> </div>\n",
    "\n",
    "\n",
    "## Homework: EDA and Visualization\n",
    "\n",
    "**Clemson University**<br>\n",
    "**Fall 2021**<br>\n",
    "**Instructor(s):** Nina Hubig <br>\n",
    "**Author(s):** Brandon Walker\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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       "\n",
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       "    border-color: #E9967A; \t\n",
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   ],
   "source": [
    "\"\"\" RUN THIS CELL TO GET THE RIGHT FORMATTING \"\"\"\n",
    "import requests\n",
    "from IPython.core.display import HTML\n",
    "css_file = 'https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/css/cpsc6300.css'\n",
    "styles = requests.get(css_file).text\n",
    "HTML(styles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from pandas.plotting import scatter_matrix \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INSTRUCTIONS\n",
    "\n",
    "- To submit your assignment follow the instructions given in Canvas.\n",
    "- Restart the kernel and run the whole notebook again before you submit. \n",
    "- As much as possible, try and stick to the hints and functions we import at the top of the homework, as those are the ideas and tools the class supports and is aiming to teach. And if a problem specifies a particular library you're required to use that library, and possibly others from the import list.\n",
    "- Please use .head() when viewing data. Do not submit a notebook that is excessively long because output was not suppressed or otherwise limited. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "Using Pandas, load the `College.csv` file into a DataFrame named `college`. Take a look at the data loaded. You will notice that the first column is named 'Unnamed: 0' and contains the name of each university. Rename the first column as `Name` and show the first few rows of the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise relates to the College data set, which can be found in the file [College.csv](http://faculty.marshall.usc.edu/gareth-james/ISL/College.csv). It contains a number of variables for 777 different universities and colleges in the US. The variables are\n",
    "\n",
    "- Private: Public/private indicator\n",
    "- Apps: Number of applications received\n",
    "- Accept: Number of applicants accepted\n",
    "- Enroll: Number of new students enrolled\n",
    "- Top 10 percent: New students from top10% of high school class\n",
    "- Top 25 percent: New students from top 25% of high school class\n",
    "- F.Undergrad: Number of full-time. undergraduates\n",
    "- P.Undergrad: Number of part-time undergraduates\n",
    "- Outstate: Out-of-state tuition\n",
    "- Room.Board: Room and board costs\n",
    "- Books: Estimated book costs\n",
    "- Personal: Estimated personal spending\n",
    "- PhD: Percent of faculty with Ph.D.’s\n",
    "- Terminal: Percent of faculty with terminal degree • S.F.Ratio: Student/faculty ratio\n",
    "- perc alumni: Percent of alumni who donate\n",
    "- expend: Instructional expenditure per student\n",
    "- grad.Rate: Graduation rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Private</th>\n",
       "      <th>Apps</th>\n",
       "      <th>Accept</th>\n",
       "      <th>Enroll</th>\n",
       "      <th>Top10perc</th>\n",
       "      <th>Top25perc</th>\n",
       "      <th>F.Undergrad</th>\n",
       "      <th>P.Undergrad</th>\n",
       "      <th>Outstate</th>\n",
       "      <th>Room.Board</th>\n",
       "      <th>Books</th>\n",
       "      <th>Personal</th>\n",
       "      <th>PhD</th>\n",
       "      <th>Terminal</th>\n",
       "      <th>S.F.Ratio</th>\n",
       "      <th>perc.alumni</th>\n",
       "      <th>Expend</th>\n",
       "      <th>Grad.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
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      "text/plain": [
       "                           Name Private  Apps  Accept  Enroll  Top10perc  \\\n",
       "0  Abilene Christian University     Yes  1660    1232     721         23   \n",
       "1            Adelphi University     Yes  2186    1924     512         16   \n",
       "2                Adrian College     Yes  1428    1097     336         22   \n",
       "3           Agnes Scott College     Yes   417     349     137         60   \n",
       "4     Alaska Pacific University     Yes   193     146      55         16   \n",
       "\n",
       "   Top25perc  F.Undergrad  P.Undergrad  Outstate  Room.Board  Books  Personal  \\\n",
       "0         52         2885          537      7440        3300    450      2200   \n",
       "1         29         2683         1227     12280        6450    750      1500   \n",
       "2         50         1036           99     11250        3750    400      1165   \n",
       "3         89          510           63     12960        5450    450       875   \n",
       "4         44          249          869      7560        4120    800      1500   \n",
       "\n",
       "   PhD  Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  \n",
       "0   70        78       18.1           12    7041         60  \n",
       "1   29        30       12.2           16   10527         56  \n",
       "2   53        66       12.9           30    8735         54  \n",
       "3   92        97        7.7           37   19016         59  \n",
       "4   76        72       11.9            2   10922         15  "
      ]
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     "metadata": {},
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   ],
   "source": [
    "# %load solutions/load_data.py\n",
    "# Your code here\n",
    "df_college = pd.read_csv(\"College.csv\")\n",
    "df_college.columns.values[0] = 'Name'\n",
    "df_college.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n",
    "Now that you are familiar with the College dataset, answer the following questions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 1 - Elite Institutions: </b> Create a new qualitative variable, called Elite, by binning the Top 10 percent variable. We are going to divide universities into two groups based on whether or not the proportion of students coming from the top 10% of their high school classes exceeds 50%.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load solutions/question01.py\n",
    "# Your code here\n",
    "df_college[\"Elite\"] = \"No\"\n",
    "df_college.loc[df[\"Top10perc\"] > 50, \"Elite\"] = \"Yes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 2 - Acceptance Rates: </b> Create a new column called AcceptRate with contains the acceptance rate for each university.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>Abilene Christian University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
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       "      <td>No</td>\n",
       "      <td>74.216867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>...</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "      <td>No</td>\n",
       "      <td>88.014639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adrian College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "      <td>76.820728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>...</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "      <td>Yes</td>\n",
       "      <td>83.693046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>...</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "      <td>No</td>\n",
       "      <td>75.647668</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Worcester State College</td>\n",
       "      <td>No</td>\n",
       "      <td>2197</td>\n",
       "      <td>1515</td>\n",
       "      <td>543</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>3089</td>\n",
       "      <td>2029</td>\n",
       "      <td>6797</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>1200</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14</td>\n",
       "      <td>4469</td>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "      <td>68.957670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>Xavier University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1959</td>\n",
       "      <td>1805</td>\n",
       "      <td>695</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "      <td>2849</td>\n",
       "      <td>1107</td>\n",
       "      <td>11520</td>\n",
       "      <td>...</td>\n",
       "      <td>600</td>\n",
       "      <td>1250</td>\n",
       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>13.3</td>\n",
       "      <td>31</td>\n",
       "      <td>9189</td>\n",
       "      <td>83</td>\n",
       "      <td>No</td>\n",
       "      <td>92.138846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>Xavier University of Louisiana</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2097</td>\n",
       "      <td>1915</td>\n",
       "      <td>695</td>\n",
       "      <td>34</td>\n",
       "      <td>61</td>\n",
       "      <td>2793</td>\n",
       "      <td>166</td>\n",
       "      <td>6900</td>\n",
       "      <td>...</td>\n",
       "      <td>617</td>\n",
       "      <td>781</td>\n",
       "      <td>67</td>\n",
       "      <td>75</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20</td>\n",
       "      <td>8323</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>91.320935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>Yale University</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10705</td>\n",
       "      <td>2453</td>\n",
       "      <td>1317</td>\n",
       "      <td>95</td>\n",
       "      <td>99</td>\n",
       "      <td>5217</td>\n",
       "      <td>83</td>\n",
       "      <td>19840</td>\n",
       "      <td>...</td>\n",
       "      <td>630</td>\n",
       "      <td>2115</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>5.8</td>\n",
       "      <td>49</td>\n",
       "      <td>40386</td>\n",
       "      <td>99</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22.914526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>York College of Pennsylvania</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2989</td>\n",
       "      <td>1855</td>\n",
       "      <td>691</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>2988</td>\n",
       "      <td>1726</td>\n",
       "      <td>4990</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>1250</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>18.1</td>\n",
       "      <td>28</td>\n",
       "      <td>4509</td>\n",
       "      <td>99</td>\n",
       "      <td>No</td>\n",
       "      <td>62.060890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>777 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name Private   Apps  Accept  Enroll  Top10perc  \\\n",
       "0      Abilene Christian University     Yes   1660    1232     721         23   \n",
       "1                Adelphi University     Yes   2186    1924     512         16   \n",
       "2                    Adrian College     Yes   1428    1097     336         22   \n",
       "3               Agnes Scott College     Yes    417     349     137         60   \n",
       "4         Alaska Pacific University     Yes    193     146      55         16   \n",
       "..                              ...     ...    ...     ...     ...        ...   \n",
       "772         Worcester State College      No   2197    1515     543          4   \n",
       "773               Xavier University     Yes   1959    1805     695         24   \n",
       "774  Xavier University of Louisiana     Yes   2097    1915     695         34   \n",
       "775                 Yale University     Yes  10705    2453    1317         95   \n",
       "776    York College of Pennsylvania     Yes   2989    1855     691         28   \n",
       "\n",
       "     Top25perc  F.Undergrad  P.Undergrad  Outstate  ...  Books  Personal  PhD  \\\n",
       "0           52         2885          537      7440  ...    450      2200   70   \n",
       "1           29         2683         1227     12280  ...    750      1500   29   \n",
       "2           50         1036           99     11250  ...    400      1165   53   \n",
       "3           89          510           63     12960  ...    450       875   92   \n",
       "4           44          249          869      7560  ...    800      1500   76   \n",
       "..         ...          ...          ...       ...  ...    ...       ...  ...   \n",
       "772         26         3089         2029      6797  ...    500      1200   60   \n",
       "773         47         2849         1107     11520  ...    600      1250   73   \n",
       "774         61         2793          166      6900  ...    617       781   67   \n",
       "775         99         5217           83     19840  ...    630      2115   96   \n",
       "776         63         2988         1726      4990  ...    500      1250   75   \n",
       "\n",
       "     Terminal  S.F.Ratio  perc.alumni  Expend  Grad.Rate  Elite AcceptRate  \n",
       "0          78       18.1           12    7041         60     No  74.216867  \n",
       "1          30       12.2           16   10527         56     No  88.014639  \n",
       "2          66       12.9           30    8735         54     No  76.820728  \n",
       "3          97        7.7           37   19016         59    Yes  83.693046  \n",
       "4          72       11.9            2   10922         15     No  75.647668  \n",
       "..        ...        ...          ...     ...        ...    ...        ...  \n",
       "772        60       21.0           14    4469         40     No  68.957670  \n",
       "773        75       13.3           31    9189         83     No  92.138846  \n",
       "774        75       14.4           20    8323         49     No  91.320935  \n",
       "775        96        5.8           49   40386         99    Yes  22.914526  \n",
       "776        75       18.1           28    4509         99     No  62.060890  \n",
       "\n",
       "[777 rows x 21 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/question02.py\n",
    "# Your code here\n",
    "df_college[\"AcceptRate\"] = (df_college[\"Accept\"] / df[\"Apps\"]) * 100\n",
    "df_college\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 3:</b> How many elite schools are there?</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/question03.py\n",
    "# Your code here\n",
    "df_college.Elite.value_counts()[\"Yes\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 4 - Acceptance Rate Comparison</b> Create a boxplot comparing the acceptance rates of elite and non-elite universities. Make sure your plot has the appropriate labels and a title.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load solutions/question04.py\n",
    "# Your code here\n",
    "ar = sns.catplot(x = \"Elite\", y = \"AcceptRate\", kind = \"box\", order = [\"Yes\", \"No\"], data = df_college)\n",
    "ar.set(ylabel = \"Percent of applicants accepted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 5 - Cost Comparisons:</b> Create two side-by-side histograms (using subplots) showing the distribution of out of state tuition for elite and non-elite institutions. Make sure each plot is labeled appropriately.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'cost'), Text(0.5, 1.0, 'non-Elite institutio')]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## %load solutions/question05.py\n",
    "# Your code here\n",
    "fig, axes = plt.subplots(nrows = 2, ncols = 2, figsize = (15, 10))\n",
    "\n",
    "sns.histplot(df_college.loc[df_college[\"Elite\"] == \"Yes\", \"Outstate\"], ax = axes[0,0]).set(xlabel = \"cost\", title = \"elite institutions\")\n",
    "\n",
    "sns.histplot(df_college.loc[df_college[\"Elite\"] == \"No\", \"Outstate\"], ax = axes[0,1]).set(xlabel = \"cost\", title = \"non-Elite institutio\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 6:</b> What is the university with the most students in the top 10% of class?</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Massachusetts Institute of Technology'"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/question06.py\n",
    "# Your code here\n",
    "df_college.iloc[df_college[\"Top10perc\"].idxmax()][\"Name\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 7:</b> What university has the smallest acceptance rate?</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Princeton University'"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/question07.py\n",
    "# Your code here\n",
    "df_college.iloc[df_college[\"AcceptRate\"].idxmin()][\"Name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 8:</b> What university has the most liberal acceptance rate?</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Emporia State University'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/question08.py\n",
    "# Your code here\n",
    "df_college.iloc[df_college[\"AcceptRate\"].idxmax()][\"Name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 9:</b> What is the correlation between out-of-state tuition and graduation rate?</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cor:\t\t 0.5712899282482009\n"
     ]
    }
   ],
   "source": [
    "# %load solutions/question09.py\n",
    "# Your code here\n",
    "from scipy import stats\n",
    "\n",
    "corr = stats.pearsonr(df_college[\"Outstate\"], df_college[\"Grad.Rate\"])\n",
    "print(\"cor:\\t\\t\", corr[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 10:</b> Based on the data provided, what is Clemson's acceptance rate?</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[65.1828890266584]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/question10.py\n",
    "# Your code here\n",
    "list(df_college[df_college[\"Name\"]==\"Clemson University\"][\"AcceptRate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                                                \n",
    "                                                                                \n",
    "                                                                                Submitted by Sai Vinay Nandigam"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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   "file_extension": ".py",
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