Education.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Education Cost and Benefits Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mp\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Private v Non-Private cost and Pay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>School_Name</th>\n",
       "      <th>School_Type</th>\n",
       "      <th>Early_Career_Pay</th>\n",
       "      <th>Mid-Career_Pay</th>\n",
       "      <th>% High_Meaning</th>\n",
       "      <th>%_STEM_Degrees</th>\n",
       "      <th>Private</th>\n",
       "      <th>Rank2015</th>\n",
       "      <th>Rank2014</th>\n",
       "      <th>cost_2015</th>\n",
       "      <th>cost_2014</th>\n",
       "      <th>percent_change</th>\n",
       "      <th>2yrcost</th>\n",
       "      <th>totalcost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>415</td>\n",
       "      <td>416 (tie)</td>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>Private School, Research University</td>\n",
       "      <td>51500</td>\n",
       "      <td>90600</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.06</td>\n",
       "      <td>True</td>\n",
       "      <td>353.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>47110.0</td>\n",
       "      <td>45200.0</td>\n",
       "      <td>4.23%</td>\n",
       "      <td>92310.0</td>\n",
       "      <td>184620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>885</td>\n",
       "      <td>887 (tie)</td>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>Liberal Arts School, Private School, Religious...</td>\n",
       "      <td>42900</td>\n",
       "      <td>78300</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.21</td>\n",
       "      <td>True</td>\n",
       "      <td>342.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>47323.0</td>\n",
       "      <td>45691.0</td>\n",
       "      <td>3.57%</td>\n",
       "      <td>93014.0</td>\n",
       "      <td>186028.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>661</td>\n",
       "      <td>662 (tie)</td>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>Private School, Religious</td>\n",
       "      <td>48800</td>\n",
       "      <td>83600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07</td>\n",
       "      <td>True</td>\n",
       "      <td>497.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>43180.0</td>\n",
       "      <td>43180.0</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>86360.0</td>\n",
       "      <td>172720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>929</td>\n",
       "      <td>931 (tie)</td>\n",
       "      <td>Albertus Magnus College</td>\n",
       "      <td>Business, Private School, Religious</td>\n",
       "      <td>48500</td>\n",
       "      <td>77500</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.01</td>\n",
       "      <td>True</td>\n",
       "      <td>344.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>47302.0</td>\n",
       "      <td>46528.0</td>\n",
       "      <td>1.66%</td>\n",
       "      <td>93830.0</td>\n",
       "      <td>187660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>188</td>\n",
       "      <td>189 (tie)</td>\n",
       "      <td>Albion College</td>\n",
       "      <td>Liberal Arts School, Private School, Religious</td>\n",
       "      <td>48500</td>\n",
       "      <td>101700</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.24</td>\n",
       "      <td>True</td>\n",
       "      <td>317.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>48195.0</td>\n",
       "      <td>45884.0</td>\n",
       "      <td>5.04%</td>\n",
       "      <td>94079.0</td>\n",
       "      <td>188158.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       Rank                School_Name  \\\n",
       "0         415  416 (tie)         Adelphi University   \n",
       "1         885  887 (tie)        Agnes Scott College   \n",
       "2         661  662 (tie)  Alaska Pacific University   \n",
       "3         929  931 (tie)    Albertus Magnus College   \n",
       "4         188  189 (tie)             Albion College   \n",
       "\n",
       "                                         School_Type  Early_Career_Pay  \\\n",
       "0                Private School, Research University             51500   \n",
       "1  Liberal Arts School, Private School, Religious...             42900   \n",
       "2                          Private School, Religious             48800   \n",
       "3                Business, Private School, Religious             48500   \n",
       "4     Liberal Arts School, Private School, Religious             48500   \n",
       "\n",
       "   Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "0           90600            0.50            0.06     True     353.0   \n",
       "1           78300            0.55            0.21     True     342.0   \n",
       "2           83600             NaN            0.07     True     497.0   \n",
       "3           77500            0.60            0.01     True     344.0   \n",
       "4          101700            0.48            0.24     True     317.0   \n",
       "\n",
       "   Rank2014  cost_2015  cost_2014 percent_change  2yrcost  totalcost  \n",
       "0     360.0    47110.0    45200.0          4.23%  92310.0   184620.0  \n",
       "1     339.0    47323.0    45691.0          3.57%  93014.0   186028.0  \n",
       "2     429.0    43180.0    43180.0          0.00%  86360.0   172720.0  \n",
       "3     305.0    47302.0    46528.0          1.66%  93830.0   187660.0  \n",
       "4     334.0    48195.0    45884.0          5.04%  94079.0   188158.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dWV = pd.read_csv(\"Resources/Cost_And_Pay.csv\")\n",
    "dWV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>School_Name</th>\n",
       "      <th>School_Type</th>\n",
       "      <th>Early_Career_Pay</th>\n",
       "      <th>Mid-Career_Pay</th>\n",
       "      <th>% High_Meaning</th>\n",
       "      <th>%_STEM_Degrees</th>\n",
       "      <th>Private</th>\n",
       "      <th>Rank2015</th>\n",
       "      <th>Rank2014</th>\n",
       "      <th>cost_2015</th>\n",
       "      <th>cost_2014</th>\n",
       "      <th>percent_change</th>\n",
       "      <th>2yrcost</th>\n",
       "      <th>totalcost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>70</td>\n",
       "      <td>70 (tie)</td>\n",
       "      <td>New York University</td>\n",
       "      <td>Private School, Research University</td>\n",
       "      <td>58700</td>\n",
       "      <td>117000</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.17</td>\n",
       "      <td>True</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63540.0</td>\n",
       "      <td>61407.0</td>\n",
       "      <td>3.47%</td>\n",
       "      <td>124947.0</td>\n",
       "      <td>249894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Dartmouth College</td>\n",
       "      <td>Ivy League, Private School, Research Universit...</td>\n",
       "      <td>66300</td>\n",
       "      <td>140300</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.32</td>\n",
       "      <td>True</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63279.0</td>\n",
       "      <td>61398.0</td>\n",
       "      <td>3.06%</td>\n",
       "      <td>124677.0</td>\n",
       "      <td>249354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>105</td>\n",
       "      <td>106</td>\n",
       "      <td>Fordham University</td>\n",
       "      <td>Private School, Religious, Research University...</td>\n",
       "      <td>54400</td>\n",
       "      <td>111300</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.09</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>63438.0</td>\n",
       "      <td>61073.0</td>\n",
       "      <td>3.87%</td>\n",
       "      <td>124511.0</td>\n",
       "      <td>249022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>Engineering, Liberal Arts School, Private School</td>\n",
       "      <td>81000</td>\n",
       "      <td>155800</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.85</td>\n",
       "      <td>True</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63860.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>5.36%</td>\n",
       "      <td>124473.0</td>\n",
       "      <td>248946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>244</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>Private School, Research University, For Sport...</td>\n",
       "      <td>59500</td>\n",
       "      <td>113300</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.19</td>\n",
       "      <td>True</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63193.0</td>\n",
       "      <td>60840.0</td>\n",
       "      <td>3.87%</td>\n",
       "      <td>124033.0</td>\n",
       "      <td>248066.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>135</td>\n",
       "      <td>136 (tie)</td>\n",
       "      <td>Occidental College</td>\n",
       "      <td>Liberal Arts School, Private School</td>\n",
       "      <td>51800</td>\n",
       "      <td>107700</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.25</td>\n",
       "      <td>True</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>63363.0</td>\n",
       "      <td>60655.0</td>\n",
       "      <td>4.46%</td>\n",
       "      <td>124018.0</td>\n",
       "      <td>248036.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>429</td>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>Wesleyan University</td>\n",
       "      <td>Liberal Arts School, Private School</td>\n",
       "      <td>55900</td>\n",
       "      <td>117700</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.22</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>62849.0</td>\n",
       "      <td>61167.0</td>\n",
       "      <td>2.75%</td>\n",
       "      <td>124016.0</td>\n",
       "      <td>248032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>464</td>\n",
       "      <td>464 (tie)</td>\n",
       "      <td>Bard College</td>\n",
       "      <td>Liberal Arts School, Private School</td>\n",
       "      <td>48500</td>\n",
       "      <td>89000</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>True</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62572.0</td>\n",
       "      <td>60280.0</td>\n",
       "      <td>3.80%</td>\n",
       "      <td>122852.0</td>\n",
       "      <td>245704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>435</td>\n",
       "      <td>436 (tie)</td>\n",
       "      <td>Bennington College</td>\n",
       "      <td>Liberal Arts School, Private School</td>\n",
       "      <td>45800</td>\n",
       "      <td>90100</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>61920.0</td>\n",
       "      <td>60540.0</td>\n",
       "      <td>2.28%</td>\n",
       "      <td>122460.0</td>\n",
       "      <td>244920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>355</td>\n",
       "      <td>124</td>\n",
       "      <td>124 (tie)</td>\n",
       "      <td>Trinity College</td>\n",
       "      <td>Liberal Arts School, Private School</td>\n",
       "      <td>55600</td>\n",
       "      <td>108700</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.18</td>\n",
       "      <td>True</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>62310.0</td>\n",
       "      <td>59865.0</td>\n",
       "      <td>4.08%</td>\n",
       "      <td>122175.0</td>\n",
       "      <td>244350.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       Rank              School_Name  \\\n",
       "239          70   70 (tie)      New York University   \n",
       "93           10         11        Dartmouth College   \n",
       "124         105        106       Fordham University   \n",
       "149           0          1      Harvey Mudd College   \n",
       "244          94         95  Northwestern University   \n",
       "250         135  136 (tie)       Occidental College   \n",
       "429          66         67      Wesleyan University   \n",
       "20          464  464 (tie)             Bard College   \n",
       "28          435  436 (tie)       Bennington College   \n",
       "355         124  124 (tie)          Trinity College   \n",
       "\n",
       "                                           School_Type  Early_Career_Pay  \\\n",
       "239                Private School, Research University             58700   \n",
       "93   Ivy League, Private School, Research Universit...             66300   \n",
       "124  Private School, Religious, Research University...             54400   \n",
       "149   Engineering, Liberal Arts School, Private School             81000   \n",
       "244  Private School, Research University, For Sport...             59500   \n",
       "250                Liberal Arts School, Private School             51800   \n",
       "429                Liberal Arts School, Private School             55900   \n",
       "20                 Liberal Arts School, Private School             48500   \n",
       "28                 Liberal Arts School, Private School             45800   \n",
       "355                Liberal Arts School, Private School             55600   \n",
       "\n",
       "     Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "239          117000            0.42            0.17     True       8.0   \n",
       "93           140300            0.46            0.32     True      11.0   \n",
       "124          111300            0.38            0.09     True       9.0   \n",
       "149          155800            0.58            0.85     True       7.0   \n",
       "244          113300            0.48            0.19     True      12.0   \n",
       "250          107700            0.46            0.25     True      10.0   \n",
       "429          117700            0.59            0.22     True      13.0   \n",
       "20            89000            0.49            0.00     True      14.0   \n",
       "28            90100            0.37            0.05     True      23.0   \n",
       "355          108700            0.49            0.18     True      16.0   \n",
       "\n",
       "     Rank2014  cost_2015  cost_2014 percent_change   2yrcost  totalcost  \n",
       "239       7.0    63540.0    61407.0          3.47%  124947.0   249894.0  \n",
       "93        8.0    63279.0    61398.0          3.06%  124677.0   249354.0  \n",
       "124      10.0    63438.0    61073.0          3.87%  124511.0   249022.0  \n",
       "149      14.0    63860.0    60613.0          5.36%  124473.0   248946.0  \n",
       "244      11.0    63193.0    60840.0          3.87%  124033.0   248066.0  \n",
       "250      12.0    63363.0    60655.0          4.46%  124018.0   248036.0  \n",
       "429       9.0    62849.0    61167.0          2.75%  124016.0   248032.0  \n",
       "20       17.0    62572.0    60280.0          3.80%  122852.0   245704.0  \n",
       "28       15.0    61920.0    60540.0          2.28%  122460.0   244920.0  \n",
       "355      22.0    62310.0    59865.0          4.08%  122175.0   244350.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comparing top ten private vs.top ten non-private (incl.State) for Total Cost (step 1 and step 2)\n",
    "#Step 1: Top Ten Private colleges based on total cost\n",
    "pvt_college_List = dWV[dWV['Private']==True]                                  \n",
    "topTenPvtCollege_TotalCost = pvt_college_List.sort_values('totalcost', ascending = False)[:10]\n",
    "\n",
    "topTenPvtCollege_TotalCost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>School_Name</th>\n",
       "      <th>School_Type</th>\n",
       "      <th>Early_Career_Pay</th>\n",
       "      <th>Mid-Career_Pay</th>\n",
       "      <th>% High_Meaning</th>\n",
       "      <th>%_STEM_Degrees</th>\n",
       "      <th>Private</th>\n",
       "      <th>Rank2015</th>\n",
       "      <th>Rank2014</th>\n",
       "      <th>cost_2015</th>\n",
       "      <th>cost_2014</th>\n",
       "      <th>percent_change</th>\n",
       "      <th>2yrcost</th>\n",
       "      <th>totalcost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>368</td>\n",
       "      <td>167</td>\n",
       "      <td>168</td>\n",
       "      <td>University of California-Santa Cruz</td>\n",
       "      <td>Research University, State School</td>\n",
       "      <td>53500</td>\n",
       "      <td>103800</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.37</td>\n",
       "      <td>False</td>\n",
       "      <td>145.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>56031.0</td>\n",
       "      <td>56173.0</td>\n",
       "      <td>-0.25%</td>\n",
       "      <td>112204.0</td>\n",
       "      <td>224408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>University of California-Berkeley</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>65400</td>\n",
       "      <td>130100</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.34</td>\n",
       "      <td>False</td>\n",
       "      <td>142.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>56197.0</td>\n",
       "      <td>55766.0</td>\n",
       "      <td>0.77%</td>\n",
       "      <td>111963.0</td>\n",
       "      <td>223926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>367</td>\n",
       "      <td>60</td>\n",
       "      <td>60 (tie)</td>\n",
       "      <td>University of California-Santa Barbara</td>\n",
       "      <td>Party School, Research University, For Sports ...</td>\n",
       "      <td>54900</td>\n",
       "      <td>118800</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.28</td>\n",
       "      <td>False</td>\n",
       "      <td>133.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>56929.0</td>\n",
       "      <td>54945.0</td>\n",
       "      <td>3.61%</td>\n",
       "      <td>111874.0</td>\n",
       "      <td>223748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>107</td>\n",
       "      <td>108</td>\n",
       "      <td>University of California-Davis</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>56700</td>\n",
       "      <td>110900</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.37</td>\n",
       "      <td>False</td>\n",
       "      <td>147.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>55944.0</td>\n",
       "      <td>55046.0</td>\n",
       "      <td>1.63%</td>\n",
       "      <td>110990.0</td>\n",
       "      <td>221980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>University of California-Los Angeles</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>57500</td>\n",
       "      <td>114800</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "      <td>False</td>\n",
       "      <td>160.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>55294.0</td>\n",
       "      <td>54810.0</td>\n",
       "      <td>0.88%</td>\n",
       "      <td>110104.0</td>\n",
       "      <td>220208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>139</td>\n",
       "      <td>140 (tie)</td>\n",
       "      <td>University of California-Riverside</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>51600</td>\n",
       "      <td>107400</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
       "      <td>False</td>\n",
       "      <td>161.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>55116.0</td>\n",
       "      <td>54475.0</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>109591.0</td>\n",
       "      <td>219182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>59</td>\n",
       "      <td>60 (tie)</td>\n",
       "      <td>University of California-Irvine</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>54900</td>\n",
       "      <td>118800</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.32</td>\n",
       "      <td>False</td>\n",
       "      <td>192.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>53382.0</td>\n",
       "      <td>52906.0</td>\n",
       "      <td>0.90%</td>\n",
       "      <td>106288.0</td>\n",
       "      <td>212576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>University of California-San Diego</td>\n",
       "      <td>Engineering, Research University, State School</td>\n",
       "      <td>58600</td>\n",
       "      <td>124900</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>False</td>\n",
       "      <td>193.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>53348.0</td>\n",
       "      <td>52906.0</td>\n",
       "      <td>0.84%</td>\n",
       "      <td>106254.0</td>\n",
       "      <td>212508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>382</td>\n",
       "      <td>158</td>\n",
       "      <td>158 (tie)</td>\n",
       "      <td>University of Michigan-Ann Arbor</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>59300</td>\n",
       "      <td>104600</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.38</td>\n",
       "      <td>False</td>\n",
       "      <td>188.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>53490.0</td>\n",
       "      <td>51976.0</td>\n",
       "      <td>2.91%</td>\n",
       "      <td>105466.0</td>\n",
       "      <td>210932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>61</td>\n",
       "      <td>62 (tie)</td>\n",
       "      <td>University of Virginia-Main Campus</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>59500</td>\n",
       "      <td>118400</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.21</td>\n",
       "      <td>False</td>\n",
       "      <td>189.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>53461.0</td>\n",
       "      <td>51337.0</td>\n",
       "      <td>4.14%</td>\n",
       "      <td>104798.0</td>\n",
       "      <td>209596.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       Rank                             School_Name  \\\n",
       "368         167        168     University of California-Santa Cruz   \n",
       "361          29         30       University of California-Berkeley   \n",
       "367          60   60 (tie)  University of California-Santa Barbara   \n",
       "362         107        108          University of California-Davis   \n",
       "364          80         81    University of California-Los Angeles   \n",
       "365         139  140 (tie)      University of California-Riverside   \n",
       "363          59   60 (tie)         University of California-Irvine   \n",
       "366          41         42      University of California-San Diego   \n",
       "382         158  158 (tie)        University of Michigan-Ann Arbor   \n",
       "401          61   62 (tie)      University of Virginia-Main Campus   \n",
       "\n",
       "                                           School_Type  Early_Career_Pay  \\\n",
       "368                  Research University, State School             53500   \n",
       "361  Research University, For Sports Fans, State Sc...             65400   \n",
       "367  Party School, Research University, For Sports ...             54900   \n",
       "362  Research University, For Sports Fans, State Sc...             56700   \n",
       "364  Research University, For Sports Fans, State Sc...             57500   \n",
       "365  Research University, For Sports Fans, State Sc...             51600   \n",
       "363  Research University, For Sports Fans, State Sc...             54900   \n",
       "366     Engineering, Research University, State School             58600   \n",
       "382  Research University, For Sports Fans, State Sc...             59300   \n",
       "401  Research University, For Sports Fans, State Sc...             59500   \n",
       "\n",
       "     Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "368          103800            0.45            0.37    False     145.0   \n",
       "361          130100            0.49            0.34    False     142.0   \n",
       "367          118800            0.47            0.28    False     133.0   \n",
       "362          110900            0.53            0.37    False     147.0   \n",
       "364          114800            0.47            0.31    False     160.0   \n",
       "365          107400            0.44            0.33    False     161.0   \n",
       "363          118800            0.48            0.32    False     192.0   \n",
       "366          124900            0.53            0.54    False     193.0   \n",
       "382          104600            0.46            0.38    False     188.0   \n",
       "401          118400            0.48            0.21    False     189.0   \n",
       "\n",
       "     Rank2014  cost_2015  cost_2014 percent_change   2yrcost  totalcost  \n",
       "368     111.0    56031.0    56173.0         -0.25%  112204.0   224408.0  \n",
       "361     119.0    56197.0    55766.0          0.77%  111963.0   223926.0  \n",
       "367     136.0    56929.0    54945.0          3.61%  111874.0   223748.0  \n",
       "362     134.0    55944.0    55046.0          1.63%  110990.0   221980.0  \n",
       "364     140.0    55294.0    54810.0          0.88%  110104.0   220208.0  \n",
       "365     144.0    55116.0    54475.0          1.18%  109591.0   219182.0  \n",
       "363     171.0    53382.0    52906.0          0.90%  106288.0   212576.0  \n",
       "366     170.0    53348.0    52906.0          0.84%  106254.0   212508.0  \n",
       "382     183.0    53490.0    51976.0          2.91%  105466.0   210932.0  \n",
       "401     190.0    53461.0    51337.0          4.14%  104798.0   209596.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Step 2 : Top Ten Non- Private colleges based on total cost (step 2 of 2)\n",
    "NonPvt_college_List = dWV[dWV['Private']== False]\n",
    "topTenNonPvtCollege_TotalCost = NonPvt_college_List.sort_values('totalcost', ascending = False)[:10]\n",
    "topTenNonPvtCollege_TotalCost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>School_Name</th>\n",
       "      <th>School_Type</th>\n",
       "      <th>Early_Career_Pay</th>\n",
       "      <th>Mid-Career_Pay</th>\n",
       "      <th>% High_Meaning</th>\n",
       "      <th>%_STEM_Degrees</th>\n",
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       "      <th>Rank2015</th>\n",
       "      <th>Rank2014</th>\n",
       "      <th>cost_2015</th>\n",
       "      <th>cost_2014</th>\n",
       "      <th>percent_change</th>\n",
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       "      <th>totalcost</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>Engineering, Private School, Research University</td>\n",
       "      <td>81500</td>\n",
       "      <td>147000</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.69</td>\n",
       "      <td>True</td>\n",
       "      <td>93.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>59020.0</td>\n",
       "      <td>57010.0</td>\n",
       "      <td>3.53%</td>\n",
       "      <td>116030.0</td>\n",
       "      <td>232060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>Engineering, Liberal Arts School, Private School</td>\n",
       "      <td>81000</td>\n",
       "      <td>155800</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.85</td>\n",
       "      <td>True</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63860.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>5.36%</td>\n",
       "      <td>124473.0</td>\n",
       "      <td>248946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>Engineering, Private School, Research University</td>\n",
       "      <td>78800</td>\n",
       "      <td>142500</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.97</td>\n",
       "      <td>True</td>\n",
       "      <td>96.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>58755.0</td>\n",
       "      <td>56382.0</td>\n",
       "      <td>4.21%</td>\n",
       "      <td>115137.0</td>\n",
       "      <td>230274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>335</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>Private School, Research University, For Sport...</td>\n",
       "      <td>73300</td>\n",
       "      <td>140400</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.49</td>\n",
       "      <td>True</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>60749.0</td>\n",
       "      <td>58846.0</td>\n",
       "      <td>3.23%</td>\n",
       "      <td>119595.0</td>\n",
       "      <td>239190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>289</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>Rose-Hulman Institute of Technology</td>\n",
       "      <td>Engineering, Private School</td>\n",
       "      <td>71600</td>\n",
       "      <td>125900</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.98</td>\n",
       "      <td>True</td>\n",
       "      <td>128.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>57138.0</td>\n",
       "      <td>55413.0</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>112551.0</td>\n",
       "      <td>225102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>Engineering, Private School, Research University</td>\n",
       "      <td>70900</td>\n",
       "      <td>126400</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.64</td>\n",
       "      <td>True</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>62032.0</td>\n",
       "      <td>59710.0</td>\n",
       "      <td>3.89%</td>\n",
       "      <td>121742.0</td>\n",
       "      <td>243484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>425</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>Webb Institute</td>\n",
       "      <td>Engineering, Private School</td>\n",
       "      <td>70800</td>\n",
       "      <td>138200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>61820.0</td>\n",
       "      <td>18520.0</td>\n",
       "      <td>233.80%</td>\n",
       "      <td>80340.0</td>\n",
       "      <td>160680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>337</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>Stevens Institute of Technology</td>\n",
       "      <td>Engineering, Private School, Research University</td>\n",
       "      <td>70800</td>\n",
       "      <td>136900</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>True</td>\n",
       "      <td>59.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60554.0</td>\n",
       "      <td>59260.0</td>\n",
       "      <td>2.18%</td>\n",
       "      <td>119814.0</td>\n",
       "      <td>239628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>268</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Princeton University</td>\n",
       "      <td>Ivy League, Private School, Research Universit...</td>\n",
       "      <td>69800</td>\n",
       "      <td>147800</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>True</td>\n",
       "      <td>150.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>55832.0</td>\n",
       "      <td>54780.0</td>\n",
       "      <td>1.92%</td>\n",
       "      <td>110612.0</td>\n",
       "      <td>221224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Harvard University</td>\n",
       "      <td>Ivy League, Private School, Research Universit...</td>\n",
       "      <td>69200</td>\n",
       "      <td>140700</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>True</td>\n",
       "      <td>72.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>59950.0</td>\n",
       "      <td>57050.0</td>\n",
       "      <td>5.08%</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>234000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 Rank                            School_Name  \\\n",
       "210           2    3  Massachusetts Institute of Technology   \n",
       "149           0    1                    Harvey Mudd College   \n",
       "45            6    7     California Institute of Technology   \n",
       "335           9   10                    Stanford University   \n",
       "289          39   40    Rose-Hulman Institute of Technology   \n",
       "51           37   38             Carnegie Mellon University   \n",
       "425          13   14                         Webb Institute   \n",
       "337          16   17        Stevens Institute of Technology   \n",
       "268           1    2                   Princeton University   \n",
       "148           8    9                     Harvard University   \n",
       "\n",
       "                                           School_Type  Early_Career_Pay  \\\n",
       "210   Engineering, Private School, Research University             81500   \n",
       "149   Engineering, Liberal Arts School, Private School             81000   \n",
       "45    Engineering, Private School, Research University             78800   \n",
       "335  Private School, Research University, For Sport...             73300   \n",
       "289                        Engineering, Private School             71600   \n",
       "51    Engineering, Private School, Research University             70900   \n",
       "425                        Engineering, Private School             70800   \n",
       "337   Engineering, Private School, Research University             70800   \n",
       "268  Ivy League, Private School, Research Universit...             69800   \n",
       "148  Ivy League, Private School, Research Universit...             69200   \n",
       "\n",
       "     Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "210          147000            0.53            0.69     True      93.0   \n",
       "149          155800            0.58            0.85     True       7.0   \n",
       "45           142500            0.52            0.97     True      96.0   \n",
       "335          140400            0.56            0.49     True      56.0   \n",
       "289          125900            0.51            0.98     True     128.0   \n",
       "51           126400            0.46            0.64     True      20.0   \n",
       "425          138200             NaN            1.00     True      26.0   \n",
       "337          136900            0.50            0.80     True      59.0   \n",
       "268          147800            0.44            0.47     True     150.0   \n",
       "148          140700            0.56            0.19     True      72.0   \n",
       "\n",
       "     Rank2014  cost_2015  cost_2014 percent_change   2yrcost  totalcost  \n",
       "210      95.0    59020.0    57010.0          3.53%  116030.0   232060.0  \n",
       "149      14.0    63860.0    60613.0          5.36%  124473.0   248946.0  \n",
       "45      109.0    58755.0    56382.0          4.21%  115137.0   230274.0  \n",
       "335      51.0    60749.0    58846.0          3.23%  119595.0   239190.0  \n",
       "289     128.0    57138.0    55413.0          3.11%  112551.0   225102.0  \n",
       "51       26.0    62032.0    59710.0          3.89%  121742.0   243484.0  \n",
       "425    1959.0    61820.0    18520.0        233.80%   80340.0   160680.0  \n",
       "337      40.0    60554.0    59260.0          2.18%  119814.0   239628.0  \n",
       "268     141.0    55832.0    54780.0          1.92%  110612.0   221224.0  \n",
       "148      92.0    59950.0    57050.0          5.08%  117000.0   234000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparing top ten private vs. top ten non-private (incl. State) for Early-career_Pay (step 1 of 2)\n",
    "# step 1\n",
    "\n",
    "pvt_college_List = dWV[dWV['Private']==True]  \n",
    "\n",
    "topTenPvtCollege_EaryCareerPay = pvt_college_List.sort_values('Early_Career_Pay', ascending = False)[:10]\n",
    "\n",
    "topTenPvtCollege_EaryCareerPay\n",
    "#topTenPvtCollege_Early_CareerPay\n",
    "\n",
    "#comparing top ten private vs. top ten non-private (incl. state) for Mid-Career_pay\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>School_Name</th>\n",
       "      <th>School_Type</th>\n",
       "      <th>Early_Career_Pay</th>\n",
       "      <th>Mid-Career_Pay</th>\n",
       "      <th>% High_Meaning</th>\n",
       "      <th>%_STEM_Degrees</th>\n",
       "      <th>Private</th>\n",
       "      <th>Rank2015</th>\n",
       "      <th>Rank2014</th>\n",
       "      <th>cost_2015</th>\n",
       "      <th>cost_2014</th>\n",
       "      <th>percent_change</th>\n",
       "      <th>2yrcost</th>\n",
       "      <th>totalcost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Colorado School of Mines</td>\n",
       "      <td>Engineering, Research University, State School</td>\n",
       "      <td>71900</td>\n",
       "      <td>136100</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.94</td>\n",
       "      <td>False</td>\n",
       "      <td>371.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>46533.0</td>\n",
       "      <td>44531.0</td>\n",
       "      <td>4.50%</td>\n",
       "      <td>91064.0</td>\n",
       "      <td>182128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>University of California-Berkeley</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>65400</td>\n",
       "      <td>130100</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.34</td>\n",
       "      <td>False</td>\n",
       "      <td>142.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>56197.0</td>\n",
       "      <td>55766.0</td>\n",
       "      <td>0.77%</td>\n",
       "      <td>111963.0</td>\n",
       "      <td>223926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>415</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>Virginia Military Institute</td>\n",
       "      <td>Liberal Arts School, For Sports Fans, State Sc...</td>\n",
       "      <td>62200</td>\n",
       "      <td>124600</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.43</td>\n",
       "      <td>False</td>\n",
       "      <td>366.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>46630.0</td>\n",
       "      <td>44694.0</td>\n",
       "      <td>4.33%</td>\n",
       "      <td>91324.0</td>\n",
       "      <td>182648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>54</td>\n",
       "      <td>54 (tie)</td>\n",
       "      <td>New Jersey Institute of Technology</td>\n",
       "      <td>Engineering, Research University, For Sports F...</td>\n",
       "      <td>60800</td>\n",
       "      <td>119700</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.71</td>\n",
       "      <td>False</td>\n",
       "      <td>440.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>44540.0</td>\n",
       "      <td>43590.0</td>\n",
       "      <td>2.18%</td>\n",
       "      <td>88130.0</td>\n",
       "      <td>176260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>61</td>\n",
       "      <td>62 (tie)</td>\n",
       "      <td>University of Virginia-Main Campus</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>59500</td>\n",
       "      <td>118400</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.21</td>\n",
       "      <td>False</td>\n",
       "      <td>189.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>53461.0</td>\n",
       "      <td>51337.0</td>\n",
       "      <td>4.14%</td>\n",
       "      <td>104798.0</td>\n",
       "      <td>209596.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>382</td>\n",
       "      <td>158</td>\n",
       "      <td>158 (tie)</td>\n",
       "      <td>University of Michigan-Ann Arbor</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>59300</td>\n",
       "      <td>104600</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.38</td>\n",
       "      <td>False</td>\n",
       "      <td>188.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>53490.0</td>\n",
       "      <td>51976.0</td>\n",
       "      <td>2.91%</td>\n",
       "      <td>105466.0</td>\n",
       "      <td>210932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>379</td>\n",
       "      <td>117</td>\n",
       "      <td>117 (tie)</td>\n",
       "      <td>University of Illinois at Urbana-Champaign</td>\n",
       "      <td>Party School, Research University, For Sports ...</td>\n",
       "      <td>58600</td>\n",
       "      <td>109600</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>False</td>\n",
       "      <td>484.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>43468.0</td>\n",
       "      <td>42706.0</td>\n",
       "      <td>1.78%</td>\n",
       "      <td>86174.0</td>\n",
       "      <td>172348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>University of California-San Diego</td>\n",
       "      <td>Engineering, Research University, State School</td>\n",
       "      <td>58600</td>\n",
       "      <td>124900</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>False</td>\n",
       "      <td>193.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>53348.0</td>\n",
       "      <td>52906.0</td>\n",
       "      <td>0.84%</td>\n",
       "      <td>106254.0</td>\n",
       "      <td>212508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>University of California-Los Angeles</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>57500</td>\n",
       "      <td>114800</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "      <td>False</td>\n",
       "      <td>160.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>55294.0</td>\n",
       "      <td>54810.0</td>\n",
       "      <td>0.88%</td>\n",
       "      <td>110104.0</td>\n",
       "      <td>220208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>403</td>\n",
       "      <td>122</td>\n",
       "      <td>122 (tie)</td>\n",
       "      <td>University of Washington-Seattle Campus</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>57300</td>\n",
       "      <td>108800</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.32</td>\n",
       "      <td>False</td>\n",
       "      <td>369.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>46608.0</td>\n",
       "      <td>43621.0</td>\n",
       "      <td>6.85%</td>\n",
       "      <td>90229.0</td>\n",
       "      <td>180458.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       Rank                                 School_Name  \\\n",
       "83           17         18                    Colorado School of Mines   \n",
       "361          29         30           University of California-Berkeley   \n",
       "415          44         45                 Virginia Military Institute   \n",
       "236          54   54 (tie)          New Jersey Institute of Technology   \n",
       "401          61   62 (tie)          University of Virginia-Main Campus   \n",
       "382         158  158 (tie)            University of Michigan-Ann Arbor   \n",
       "379         117  117 (tie)  University of Illinois at Urbana-Champaign   \n",
       "366          41         42          University of California-San Diego   \n",
       "364          80         81        University of California-Los Angeles   \n",
       "403         122  122 (tie)     University of Washington-Seattle Campus   \n",
       "\n",
       "                                           School_Type  Early_Career_Pay  \\\n",
       "83      Engineering, Research University, State School             71900   \n",
       "361  Research University, For Sports Fans, State Sc...             65400   \n",
       "415  Liberal Arts School, For Sports Fans, State Sc...             62200   \n",
       "236  Engineering, Research University, For Sports F...             60800   \n",
       "401  Research University, For Sports Fans, State Sc...             59500   \n",
       "382  Research University, For Sports Fans, State Sc...             59300   \n",
       "379  Party School, Research University, For Sports ...             58600   \n",
       "366     Engineering, Research University, State School             58600   \n",
       "364  Research University, For Sports Fans, State Sc...             57500   \n",
       "403  Research University, For Sports Fans, State Sc...             57300   \n",
       "\n",
       "     Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "83           136100            0.60            0.94    False     371.0   \n",
       "361          130100            0.49            0.34    False     142.0   \n",
       "415          124600            0.66            0.43    False     366.0   \n",
       "236          119700            0.52            0.71    False     440.0   \n",
       "401          118400            0.48            0.21    False     189.0   \n",
       "382          104600            0.46            0.38    False     188.0   \n",
       "379          109600            0.46            0.37    False     484.0   \n",
       "366          124900            0.53            0.54    False     193.0   \n",
       "364          114800            0.47            0.31    False     160.0   \n",
       "403          108800            0.53            0.32    False     369.0   \n",
       "\n",
       "     Rank2014  cost_2015  cost_2014 percent_change   2yrcost  totalcost  \n",
       "83      384.0    46533.0    44531.0          4.50%   91064.0   182128.0  \n",
       "361     119.0    56197.0    55766.0          0.77%  111963.0   223926.0  \n",
       "415     375.0    46630.0    44694.0          4.33%   91324.0   182648.0  \n",
       "236     415.0    44540.0    43590.0          2.18%   88130.0   176260.0  \n",
       "401     190.0    53461.0    51337.0          4.14%  104798.0   209596.0  \n",
       "382     183.0    53490.0    51976.0          2.91%  105466.0   210932.0  \n",
       "379     452.0    43468.0    42706.0          1.78%   86174.0   172348.0  \n",
       "366     170.0    53348.0    52906.0          0.84%  106254.0   212508.0  \n",
       "364     140.0    55294.0    54810.0          0.88%  110104.0   220208.0  \n",
       "403     414.0    46608.0    43621.0          6.85%   90229.0   180458.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#topTenNon-PvtCollege_Early_CareerPay (step 2 of 2)\n",
    "NonPvt_college_List = dWV[dWV['Private']== False]\n",
    "topTenNonPvtCollege_EaryCareerPay = NonPvt_college_List.sort_values('Early_Career_Pay', ascending = False)[:10]\n",
    "topTenNonPvtCollege_EaryCareerPay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>Engineering, Liberal Arts School, Private School</td>\n",
       "      <td>81000</td>\n",
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       "      <td>0.58</td>\n",
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       "      <td>True</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63860.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>5.36%</td>\n",
       "      <td>124473.0</td>\n",
       "      <td>248946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>268</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Princeton University</td>\n",
       "      <td>Ivy League, Private School, Research Universit...</td>\n",
       "      <td>69800</td>\n",
       "      <td>147800</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>True</td>\n",
       "      <td>150.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>55832.0</td>\n",
       "      <td>54780.0</td>\n",
       "      <td>1.92%</td>\n",
       "      <td>110612.0</td>\n",
       "      <td>221224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>Engineering, Private School, Research University</td>\n",
       "      <td>81500</td>\n",
       "      <td>147000</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.69</td>\n",
       "      <td>True</td>\n",
       "      <td>93.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>59020.0</td>\n",
       "      <td>57010.0</td>\n",
       "      <td>3.53%</td>\n",
       "      <td>116030.0</td>\n",
       "      <td>232060.0</td>\n",
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       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>Engineering, Private School, Research University</td>\n",
       "      <td>78800</td>\n",
       "      <td>142500</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.97</td>\n",
       "      <td>True</td>\n",
       "      <td>96.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>58755.0</td>\n",
       "      <td>56382.0</td>\n",
       "      <td>4.21%</td>\n",
       "      <td>115137.0</td>\n",
       "      <td>230274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Babson College</td>\n",
       "      <td>Business, Private School</td>\n",
       "      <td>66600</td>\n",
       "      <td>141700</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>True</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60504.0</td>\n",
       "      <td>58450.0</td>\n",
       "      <td>3.51%</td>\n",
       "      <td>118954.0</td>\n",
       "      <td>237908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Harvard University</td>\n",
       "      <td>Ivy League, Private School, Research Universit...</td>\n",
       "      <td>69200</td>\n",
       "      <td>140700</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>True</td>\n",
       "      <td>72.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>59950.0</td>\n",
       "      <td>57050.0</td>\n",
       "      <td>5.08%</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>234000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>335</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>Private School, Research University, For Sport...</td>\n",
       "      <td>73300</td>\n",
       "      <td>140400</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.49</td>\n",
       "      <td>True</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>60749.0</td>\n",
       "      <td>58846.0</td>\n",
       "      <td>3.23%</td>\n",
       "      <td>119595.0</td>\n",
       "      <td>239190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>Dartmouth College</td>\n",
       "      <td>Ivy League, Private School, Research Universit...</td>\n",
       "      <td>66300</td>\n",
       "      <td>140300</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.32</td>\n",
       "      <td>True</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>63279.0</td>\n",
       "      <td>61398.0</td>\n",
       "      <td>3.06%</td>\n",
       "      <td>124677.0</td>\n",
       "      <td>249354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>439</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>Williams College</td>\n",
       "      <td>Liberal Arts School, Private School</td>\n",
       "      <td>62000</td>\n",
       "      <td>138400</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>61850.0</td>\n",
       "      <td>59412.0</td>\n",
       "      <td>4.10%</td>\n",
       "      <td>121262.0</td>\n",
       "      <td>242524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>425</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>Webb Institute</td>\n",
       "      <td>Engineering, Private School</td>\n",
       "      <td>70800</td>\n",
       "      <td>138200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>61820.0</td>\n",
       "      <td>18520.0</td>\n",
       "      <td>233.80%</td>\n",
       "      <td>80340.0</td>\n",
       "      <td>160680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 Rank                            School_Name  \\\n",
       "149           0    1                    Harvey Mudd College   \n",
       "268           1    2                   Princeton University   \n",
       "210           2    3  Massachusetts Institute of Technology   \n",
       "45            6    7     California Institute of Technology   \n",
       "19            7    8                         Babson College   \n",
       "148           8    9                     Harvard University   \n",
       "335           9   10                    Stanford University   \n",
       "93           10   11                      Dartmouth College   \n",
       "439          11   12                       Williams College   \n",
       "425          13   14                         Webb Institute   \n",
       "\n",
       "                                           School_Type  Early_Career_Pay  \\\n",
       "149   Engineering, Liberal Arts School, Private School             81000   \n",
       "268  Ivy League, Private School, Research Universit...             69800   \n",
       "210   Engineering, Private School, Research University             81500   \n",
       "45    Engineering, Private School, Research University             78800   \n",
       "19                            Business, Private School             66600   \n",
       "148  Ivy League, Private School, Research Universit...             69200   \n",
       "335  Private School, Research University, For Sport...             73300   \n",
       "93   Ivy League, Private School, Research Universit...             66300   \n",
       "439                Liberal Arts School, Private School             62000   \n",
       "425                        Engineering, Private School             70800   \n",
       "\n",
       "     Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "149          155800            0.58            0.85     True       7.0   \n",
       "268          147800            0.44            0.47     True     150.0   \n",
       "210          147000            0.53            0.69     True      93.0   \n",
       "45           142500            0.52            0.97     True      96.0   \n",
       "19           141700            0.34            0.00     True      60.0   \n",
       "148          140700            0.56            0.19     True      72.0   \n",
       "335          140400            0.56            0.49     True      56.0   \n",
       "93           140300            0.46            0.32     True      11.0   \n",
       "439          138400            0.44            0.33     True      25.0   \n",
       "425          138200             NaN            1.00     True      26.0   \n",
       "\n",
       "     Rank2014  cost_2015  cost_2014 percent_change   2yrcost  totalcost  \n",
       "149      14.0    63860.0    60613.0          5.36%  124473.0   248946.0  \n",
       "268     141.0    55832.0    54780.0          1.92%  110612.0   221224.0  \n",
       "210      95.0    59020.0    57010.0          3.53%  116030.0   232060.0  \n",
       "45      109.0    58755.0    56382.0          4.21%  115137.0   230274.0  \n",
       "19       61.0    60504.0    58450.0          3.51%  118954.0   237908.0  \n",
       "148      92.0    59950.0    57050.0          5.08%  117000.0   234000.0  \n",
       "335      51.0    60749.0    58846.0          3.23%  119595.0   239190.0  \n",
       "93        8.0    63279.0    61398.0          3.06%  124677.0   249354.0  \n",
       "439      33.0    61850.0    59412.0          4.10%  121262.0   242524.0  \n",
       "425    1959.0    61820.0    18520.0        233.80%   80340.0   160680.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comparing top ten private vs. top ten non-private (incl. state) for Mid-Career_pay(step 1 of 2)\n",
    "#Step 1 of 2\n",
    "pvt_college_List = dWV[dWV['Private']==True]  \n",
    "\n",
    "topTenPvtCollege_MidCareerPay = pvt_college_List.sort_values('Mid-Career_Pay', ascending = False)[:10]\n",
    "\n",
    "topTenPvtCollege_MidCareerPay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Rank</th>\n",
       "      <th>School_Name</th>\n",
       "      <th>School_Type</th>\n",
       "      <th>Early_Career_Pay</th>\n",
       "      <th>Mid-Career_Pay</th>\n",
       "      <th>% High_Meaning</th>\n",
       "      <th>%_STEM_Degrees</th>\n",
       "      <th>Private</th>\n",
       "      <th>Rank2015</th>\n",
       "      <th>Rank2014</th>\n",
       "      <th>cost_2015</th>\n",
       "      <th>cost_2014</th>\n",
       "      <th>percent_change</th>\n",
       "      <th>2yrcost</th>\n",
       "      <th>totalcost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>Colorado School of Mines</td>\n",
       "      <td>Engineering, Research University, State School</td>\n",
       "      <td>71900</td>\n",
       "      <td>136100</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.94</td>\n",
       "      <td>False</td>\n",
       "      <td>371.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>46533.0</td>\n",
       "      <td>44531.0</td>\n",
       "      <td>4.50%</td>\n",
       "      <td>91064.0</td>\n",
       "      <td>182128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>University of California-Berkeley</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>65400</td>\n",
       "      <td>130100</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.34</td>\n",
       "      <td>False</td>\n",
       "      <td>142.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>56197.0</td>\n",
       "      <td>55766.0</td>\n",
       "      <td>0.77%</td>\n",
       "      <td>111963.0</td>\n",
       "      <td>223926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>University of California-San Diego</td>\n",
       "      <td>Engineering, Research University, State School</td>\n",
       "      <td>58600</td>\n",
       "      <td>124900</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>False</td>\n",
       "      <td>193.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>53348.0</td>\n",
       "      <td>52906.0</td>\n",
       "      <td>0.84%</td>\n",
       "      <td>106254.0</td>\n",
       "      <td>212508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>415</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>Virginia Military Institute</td>\n",
       "      <td>Liberal Arts School, For Sports Fans, State Sc...</td>\n",
       "      <td>62200</td>\n",
       "      <td>124600</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.43</td>\n",
       "      <td>False</td>\n",
       "      <td>366.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>46630.0</td>\n",
       "      <td>44694.0</td>\n",
       "      <td>4.33%</td>\n",
       "      <td>91324.0</td>\n",
       "      <td>182648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>236</td>\n",
       "      <td>54</td>\n",
       "      <td>54 (tie)</td>\n",
       "      <td>New Jersey Institute of Technology</td>\n",
       "      <td>Engineering, Research University, For Sports F...</td>\n",
       "      <td>60800</td>\n",
       "      <td>119700</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.71</td>\n",
       "      <td>False</td>\n",
       "      <td>440.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>44540.0</td>\n",
       "      <td>43590.0</td>\n",
       "      <td>2.18%</td>\n",
       "      <td>88130.0</td>\n",
       "      <td>176260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>367</td>\n",
       "      <td>60</td>\n",
       "      <td>60 (tie)</td>\n",
       "      <td>University of California-Santa Barbara</td>\n",
       "      <td>Party School, Research University, For Sports ...</td>\n",
       "      <td>54900</td>\n",
       "      <td>118800</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.28</td>\n",
       "      <td>False</td>\n",
       "      <td>133.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>56929.0</td>\n",
       "      <td>54945.0</td>\n",
       "      <td>3.61%</td>\n",
       "      <td>111874.0</td>\n",
       "      <td>223748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>59</td>\n",
       "      <td>60 (tie)</td>\n",
       "      <td>University of California-Irvine</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>54900</td>\n",
       "      <td>118800</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.32</td>\n",
       "      <td>False</td>\n",
       "      <td>192.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>53382.0</td>\n",
       "      <td>52906.0</td>\n",
       "      <td>0.90%</td>\n",
       "      <td>106288.0</td>\n",
       "      <td>212576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>61</td>\n",
       "      <td>62 (tie)</td>\n",
       "      <td>University of Virginia-Main Campus</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>59500</td>\n",
       "      <td>118400</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.21</td>\n",
       "      <td>False</td>\n",
       "      <td>189.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>53461.0</td>\n",
       "      <td>51337.0</td>\n",
       "      <td>4.14%</td>\n",
       "      <td>104798.0</td>\n",
       "      <td>209596.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>University of California-Los Angeles</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>57500</td>\n",
       "      <td>114800</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "      <td>False</td>\n",
       "      <td>160.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>55294.0</td>\n",
       "      <td>54810.0</td>\n",
       "      <td>0.88%</td>\n",
       "      <td>110104.0</td>\n",
       "      <td>220208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>101</td>\n",
       "      <td>102</td>\n",
       "      <td>College of William and Mary</td>\n",
       "      <td>Research University, For Sports Fans, State Sc...</td>\n",
       "      <td>54400</td>\n",
       "      <td>111900</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.20</td>\n",
       "      <td>False</td>\n",
       "      <td>228.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>51306.0</td>\n",
       "      <td>49712.0</td>\n",
       "      <td>3.21%</td>\n",
       "      <td>101018.0</td>\n",
       "      <td>202036.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0      Rank                             School_Name  \\\n",
       "83           17        18                Colorado School of Mines   \n",
       "361          29        30       University of California-Berkeley   \n",
       "366          41        42      University of California-San Diego   \n",
       "415          44        45             Virginia Military Institute   \n",
       "236          54  54 (tie)      New Jersey Institute of Technology   \n",
       "367          60  60 (tie)  University of California-Santa Barbara   \n",
       "363          59  60 (tie)         University of California-Irvine   \n",
       "401          61  62 (tie)      University of Virginia-Main Campus   \n",
       "364          80        81    University of California-Los Angeles   \n",
       "80          101       102             College of William and Mary   \n",
       "\n",
       "                                           School_Type  Early_Career_Pay  \\\n",
       "83      Engineering, Research University, State School             71900   \n",
       "361  Research University, For Sports Fans, State Sc...             65400   \n",
       "366     Engineering, Research University, State School             58600   \n",
       "415  Liberal Arts School, For Sports Fans, State Sc...             62200   \n",
       "236  Engineering, Research University, For Sports F...             60800   \n",
       "367  Party School, Research University, For Sports ...             54900   \n",
       "363  Research University, For Sports Fans, State Sc...             54900   \n",
       "401  Research University, For Sports Fans, State Sc...             59500   \n",
       "364  Research University, For Sports Fans, State Sc...             57500   \n",
       "80   Research University, For Sports Fans, State Sc...             54400   \n",
       "\n",
       "     Mid-Career_Pay  % High_Meaning  %_STEM_Degrees  Private  Rank2015  \\\n",
       "83           136100            0.60            0.94    False     371.0   \n",
       "361          130100            0.49            0.34    False     142.0   \n",
       "366          124900            0.53            0.54    False     193.0   \n",
       "415          124600            0.66            0.43    False     366.0   \n",
       "236          119700            0.52            0.71    False     440.0   \n",
       "367          118800            0.47            0.28    False     133.0   \n",
       "363          118800            0.48            0.32    False     192.0   \n",
       "401          118400            0.48            0.21    False     189.0   \n",
       "364          114800            0.47            0.31    False     160.0   \n",
       "80           111900            0.44            0.20    False     228.0   \n",
       "\n",
       "     Rank2014  cost_2015  cost_2014 percent_change   2yrcost  totalcost  \n",
       "83      384.0    46533.0    44531.0          4.50%   91064.0   182128.0  \n",
       "361     119.0    56197.0    55766.0          0.77%  111963.0   223926.0  \n",
       "366     170.0    53348.0    52906.0          0.84%  106254.0   212508.0  \n",
       "415     375.0    46630.0    44694.0          4.33%   91324.0   182648.0  \n",
       "236     415.0    44540.0    43590.0          2.18%   88130.0   176260.0  \n",
       "367     136.0    56929.0    54945.0          3.61%  111874.0   223748.0  \n",
       "363     171.0    53382.0    52906.0          0.90%  106288.0   212576.0  \n",
       "401     190.0    53461.0    51337.0          4.14%  104798.0   209596.0  \n",
       "364     140.0    55294.0    54810.0          0.88%  110104.0   220208.0  \n",
       "80      223.0    51306.0    49712.0          3.21%  101018.0   202036.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#topTenNon-PvtCollege_Early_CareerPay (step 2 of 2)\n",
    "NonPvt_college_List = dWV[dWV['Private']== False]\n",
    "topTenNonPvtCollege_MidCareerPay = NonPvt_college_List.sort_values('Mid-Career_Pay', ascending = False)[:10]\n",
    "topTenNonPvtCollege_MidCareerPay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot based on cost_2015 vs. early-career pay (all private college and all public colleges)\n",
    "#Rationale is since early career pay is early we are trying to see recent available annual cost,which is 2015 cost\n",
    "#3 steps\n",
    "#step 1- Pvt colleges cost vs. early career pay (x= cost_2015, y= Early_Career_Pay )\n",
    "\n",
    "\n",
    "pvt_college_List = dWV[dWV['Private']==True]  \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.scatter(pvt_college_List.cost_2015, pvt_college_List.Early_Career_Pay , \n",
    "            facecolor= \"red\", \n",
    "            edgecolors=\"black\", \n",
    "            label='',\n",
    "            marker='o')\n",
    "          \n",
    "plt.title(\"Annualcost vs. early-career pay- Private Colleges\")\n",
    "plt.xlabel(\"cost_2015\")\n",
    "plt.ylabel(\"Early-Career-Pay\")\n",
    "ax.grid(True)\n",
    "\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig('Images/pvtCostVsEarlycareerPay.png', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 2- non- Pvt colleges cost vs. early career pay (x= cost_2015, y= Early_Career_Pay )NonPvt_college_List = dWV[dWV['Private']== False]\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.scatter(NonPvt_college_List.cost_2015, NonPvt_college_List.Early_Career_Pay , \n",
    "            facecolor= \"Blue\", \n",
    "            edgecolors=\"black\", \n",
    "            label='',\n",
    "            marker='o')\n",
    "            \n",
    "plt.title(\"Annualcost vs. early-career pay- Public Colleges\")\n",
    "plt.xlabel(\"cost_2015\")\n",
    "plt.ylabel(\"Early-Career-Pay\")\n",
    "ax.grid(True)\n",
    "\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig('Images/non_pvtCostVsEarlycareerPay.png', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 3 _ Combined of step 1 and step2 -plotting both in one graph (pvt and non-private annual cost vs. early carrer pay)\n",
    "x = range(40000,70000,2000)\n",
    "y = range(40000,90000,5000)\n",
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(pvt_college_List.cost_2015, pvt_college_List.Early_Career_Pay , \n",
    "            facecolor= \"red\", \n",
    "            edgecolors=\"black\", \n",
    "            label='pvt_college',\n",
    "            marker='o')\n",
    "ax1.scatter(NonPvt_college_List.cost_2015, NonPvt_college_List.Early_Career_Pay , \n",
    "            facecolor= \"Blue\", \n",
    "            edgecolors=\"black\", \n",
    "            label='non_Pvt_college',\n",
    "            marker='o')\n",
    "plt.title(\"Annualcost vs. early-career pay-  Colleges\")\n",
    "plt.xlabel(\"cost_2015\")\n",
    "plt.ylabel(\"Early-Career-Pay\")\n",
    "plt.legend(loc= 'upper center');\n",
    "plt.grid()\n",
    "plt.savefig('Images/combo_CostVsEarlycareerPay.png', bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations:\n",
    "    \n",
    "Private colleges- There seems to be a correlation between what you pay and what you earn as early-career pay.\n",
    "The majority of the data points cluster around in the range of early 40,000s to mid\n",
    "50,000s for both Cost and Pay.\n",
    "Cost of non- private schools are comparatively lesser than the private schools.\n",
    "For Public schools, there seems to be no real trend and data is less evenly distributed to draw conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot based on cost_2015 vs. mid-career pay (all private college and all public colleges)\n",
    "#Rationale is since early career pay is early we are trying to see recent available annual cost,which is 2015 cost\n",
    "#3 steps\n",
    "#step 1- Pvt colleges cost vs. mid career pay (x= cost_2015, y= Mid-Career_Pay )\n",
    "\n",
    "pvt_college_List = dWV[dWV['Private']==True]  \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# first convert Mid-career_Pay to readable here, we are putting at as list, then for uniformity doing it on cost 2015 also.\n",
    "plt.scatter(pvt_college_List[\"cost_2015\"].tolist(), pvt_college_List[\"Mid-Career_Pay\"].tolist(),\n",
    "            facecolor= \"orange\", \n",
    "            edgecolors=\"Red\", \n",
    "            label='',\n",
    "            marker='o')\n",
    "           \n",
    "plt.title(\"Annualcost vs. Mid-Career_Pay Private Colleges\")\n",
    "plt.xlabel(\"cost_2015\")\n",
    "plt.ylabel(\"Mid-Career_Pay\")\n",
    "ax.grid(True)\n",
    "\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig('Images/pvtCostVsMidCareerPay.png', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 2-Non Pvt colleges cost vs. Mid-career-pay (x= cost_2015, y= Mid-Career_Pay )\n",
    "\n",
    "NonPvt_college_List = dWV[dWV['Private']== False]\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.scatter(NonPvt_college_List[\"cost_2015\"].tolist(), NonPvt_college_List[\"Mid-Career_Pay\"].tolist(),\n",
    "            facecolor= \"lightgreen\", \n",
    "            edgecolors=\"darkgreen\", \n",
    "            label='',\n",
    "            marker='o')\n",
    "            \n",
    "plt.title(\"Annualcost vs. Mid-Career_Pay Public Colleges\")\n",
    "plt.xlabel(\"cost_2015\")\n",
    "plt.ylabel(\"Mid-Career_Pay\")\n",
    "ax.grid(True)\n",
    "\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig('Images/NonpvtCostVsMidCareerPay.png', bbox_inches = \"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 3 _ Combined of step 1 and step2 -plotting both in one graph (pvt and non-private annual cost vs. Mid career pay)\n",
    "x = range(40000,70000,2000)\n",
    "y = range(70000,140000,10000)\n",
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(pvt_college_List[\"cost_2015\"].tolist(), pvt_college_List[\"Mid-Career_Pay\"].tolist(),\n",
    "            facecolor= \"orange\", \n",
    "            edgecolors=\"Red\", \n",
    "            label='Pvt_college',\n",
    "            marker='o')\n",
    "ax1.scatter(NonPvt_college_List[\"cost_2015\"].tolist(), NonPvt_college_List[\"Mid-Career_Pay\"].tolist(),\n",
    "            facecolor= \"lightgreen\", \n",
    "            edgecolors=\"darkgreen\",\n",
    "            label='Non Pvt_college',\n",
    "            marker='o')\n",
    "            \n",
    "plt.title(\"Annualcost vs. mid-career pay-  Colleges\")\n",
    "plt.xlabel(\"cost_2015\")\n",
    "plt.ylabel(\"Mid-Career-Pay\")\n",
    "plt.legend(loc='best');\n",
    "plt.grid()\n",
    "\n",
    "plt.savefig('Images/combo_CostVsMidcareerPay.png', bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations\n",
    " Mid career pay is the same trend as the early career pay for private colleges. The scale seems to follow a linear trend.\n",
    " The trend for public colleges between early pay and mid career pay seems to be similar. No real trend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# What are the best/worst majors for ROI?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                     \n",
    "import matplotlib.pyplot as plt         \n",
    "import numpy as np                       \n",
    "from scipy.sparse import csr_matrix     \n",
    "%matplotlib inline\n",
    "# Read in data\n",
    "DegreesDF = pd.read_csv('Resources/degrees-that-pay-back.csv',encoding ='cp1252')\n",
    "earlyMidPayDF= pd.read_csv('Resources/early_mid_pay.csv',encoding ='cp1252')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Undergraduate Major</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Percent change from Starting to Mid-Career Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>$46,000.00</td>\n",
       "      <td>$77,100.00</td>\n",
       "      <td>67.6</td>\n",
       "      <td>$42,200.00</td>\n",
       "      <td>$56,100.00</td>\n",
       "      <td>$108,000.00</td>\n",
       "      <td>$152,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Aerospace Engineering</td>\n",
       "      <td>$57,700.00</td>\n",
       "      <td>$101,000.00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>$64,300.00</td>\n",
       "      <td>$82,100.00</td>\n",
       "      <td>$127,000.00</td>\n",
       "      <td>$161,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>$42,600.00</td>\n",
       "      <td>$71,900.00</td>\n",
       "      <td>68.8</td>\n",
       "      <td>$36,300.00</td>\n",
       "      <td>$52,100.00</td>\n",
       "      <td>$96,300.00</td>\n",
       "      <td>$150,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Anthropology</td>\n",
       "      <td>$36,800.00</td>\n",
       "      <td>$61,500.00</td>\n",
       "      <td>67.1</td>\n",
       "      <td>$33,800.00</td>\n",
       "      <td>$45,500.00</td>\n",
       "      <td>$89,300.00</td>\n",
       "      <td>$138,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Architecture</td>\n",
       "      <td>$41,600.00</td>\n",
       "      <td>$76,800.00</td>\n",
       "      <td>84.6</td>\n",
       "      <td>$50,600.00</td>\n",
       "      <td>$62,200.00</td>\n",
       "      <td>$97,000.00</td>\n",
       "      <td>$136,000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Undergraduate Major Starting Median Salary Mid-Career Median Salary  \\\n",
       "0             Accounting            $46,000.00               $77,100.00    \n",
       "1  Aerospace Engineering            $57,700.00              $101,000.00    \n",
       "2            Agriculture            $42,600.00               $71,900.00    \n",
       "3           Anthropology            $36,800.00               $61,500.00    \n",
       "4           Architecture            $41,600.00               $76,800.00    \n",
       "\n",
       "   Percent change from Starting to Mid-Career Salary  \\\n",
       "0                                               67.6   \n",
       "1                                               75.0   \n",
       "2                                               68.8   \n",
       "3                                               67.1   \n",
       "4                                               84.6   \n",
       "\n",
       "  Mid-Career 10th Percentile Salary Mid-Career 25th Percentile Salary  \\\n",
       "0                       $42,200.00                        $56,100.00    \n",
       "1                       $64,300.00                        $82,100.00    \n",
       "2                       $36,300.00                        $52,100.00    \n",
       "3                       $33,800.00                        $45,500.00    \n",
       "4                       $50,600.00                        $62,200.00    \n",
       "\n",
       "  Mid-Career 75th Percentile Salary Mid-Career 90th Percentile Salary  \n",
       "0                      $108,000.00                       $152,000.00   \n",
       "1                      $127,000.00                       $161,000.00   \n",
       "2                       $96,300.00                       $150,000.00   \n",
       "3                       $89,300.00                       $138,000.00   \n",
       "4                       $97,000.00                       $136,000.00   "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check data from each file\n",
    "DegreesDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Major</th>\n",
       "      <th>Degree Type</th>\n",
       "      <th>Early Career Pay</th>\n",
       "      <th>Mid Career Pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Petroleum Engineering</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>$94,600</td>\n",
       "      <td>$175,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Actuarial Mathematics</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>$56,400</td>\n",
       "      <td>$131,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Actuarial Science</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>$61,200</td>\n",
       "      <td>$130,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Nuclear Engineering</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>$69,000</td>\n",
       "      <td>$127,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Chemical Engineering</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>$70,300</td>\n",
       "      <td>$124,500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Rank                  Major Degree Type Early Career Pay Mid Career Pay\n",
       "0    1  Petroleum Engineering  Bachelor's         $94,600       $175,500 \n",
       "1    2  Actuarial Mathematics  Bachelor's         $56,400       $131,700 \n",
       "2    3      Actuarial Science  Bachelor's         $61,200       $130,800 \n",
       "3    4    Nuclear Engineering  Bachelor's         $69,000       $127,500 \n",
       "4    5   Chemical Engineering  Bachelor's         $70,300       $124,500 "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earlyMidPayDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Major</th>\n",
       "      <th>Mid Career Pay</th>\n",
       "      <th>Mid_Career_Pay_Numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Petroleum Engineering</td>\n",
       "      <td>$175,500</td>\n",
       "      <td>175500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Actuarial Mathematics</td>\n",
       "      <td>$131,700</td>\n",
       "      <td>131700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Actuarial Science</td>\n",
       "      <td>$130,800</td>\n",
       "      <td>130800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nuclear Engineering</td>\n",
       "      <td>$127,500</td>\n",
       "      <td>127500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Chemical Engineering</td>\n",
       "      <td>$124,500</td>\n",
       "      <td>124500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Major Mid Career Pay  Mid_Career_Pay_Numeric\n",
       "0  Petroleum Engineering      $175,500                 175500.0\n",
       "1  Actuarial Mathematics      $131,700                 131700.0\n",
       "2      Actuarial Science      $130,800                 130800.0\n",
       "3    Nuclear Engineering      $127,500                 127500.0\n",
       "4   Chemical Engineering      $124,500                 124500.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# UGMajor = \"Petroleum Engineering\", \"Actuarial Mathematics\", \"Actuarial Science\", \"Nuclear Engineering\", \"Chemical Engineering \"\n",
    "# MidCareerPay = 175000, 131700, 130800, 127500, 124500\n",
    "# x_axis = np.arange(len(UGMajor))\n",
    "new_df = pd.DataFrame({\"Major\": earlyMidPayDF[\"Major\"], \"Mid Career Pay\": earlyMidPayDF[\"Mid Career Pay\"]})\n",
    "new_df['Mid_Career_Pay_Numeric'] = new_df['Mid Career Pay'].replace('[\\$,]', '', regex=True).astype(float)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the ticks for our bar chart's x axis\n",
    "# Give the chart a title, x label, and y label\n",
    "#Create a new DF \n",
    "\n",
    "Top10_df = new_df.sort_values('Mid_Career_Pay_Numeric', ascending = False)[:10]\n",
    "Top10_df\n",
    "x_axis = np.arange(0, len(Top10_df.Major))\n",
    "tick_locations = []\n",
    "for x in x_axis:\n",
    "    tick_locations.append(x)\n",
    "plt.title(\"Top 10 College Majors with Greatest Return on Investment\")\n",
    "plt.xlabel(\"Under Graduate Major\")\n",
    "plt.ylabel(\"Salary\")\n",
    "tick_locations = [value for value in x_axis]\n",
    "plt.xticks(tick_locations, Top10_df.Major, rotation = 45, horizontalalignment=\"right\")\n",
    "plt.bar(x_axis, Top10_df.Mid_Career_Pay_Numeric, color=\"b\", align=\"center\")\n",
    "\n",
    "plt.savefig('Images/GreatestRoi.png', bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations:\n",
    "based on the available data the top 10 college majors \n",
    "with the greatest Return on investment are from Petroleum Engineering \n",
    "Actuarial Math, Actuarial Science, Nucler Engineering, Chemical Engineering, Marine Engineering Economics and Mathematics,Geophysics cognitive Science, and Electrical Power Engineering. \n",
    "One thing we noticed was that the majors \n",
    "apart from Petroluim Engineering, has a relatively even distribution. They all range around the salary of $125,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Major</th>\n",
       "      <th>Mid Career Pay</th>\n",
       "      <th>Mid_Career_Pay_Numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>488</td>\n",
       "      <td>Early Childhood Education</td>\n",
       "      <td>$40,400</td>\n",
       "      <td>40400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>487</td>\n",
       "      <td>Child &amp; Family Studies</td>\n",
       "      <td>$42,100</td>\n",
       "      <td>42100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486</td>\n",
       "      <td>Veterinary Technology</td>\n",
       "      <td>$43,100</td>\n",
       "      <td>43100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>485</td>\n",
       "      <td>Early Childhood &amp; Elementary Education</td>\n",
       "      <td>$43,600</td>\n",
       "      <td>43600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>484</td>\n",
       "      <td>Child Development</td>\n",
       "      <td>$44,000</td>\n",
       "      <td>44000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>483</td>\n",
       "      <td>Family Studies</td>\n",
       "      <td>$44,500</td>\n",
       "      <td>44500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>482</td>\n",
       "      <td>Human Services (HS)</td>\n",
       "      <td>$46,500</td>\n",
       "      <td>46500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481</td>\n",
       "      <td>Photojournalism</td>\n",
       "      <td>$47,200</td>\n",
       "      <td>47200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>480</td>\n",
       "      <td>Biblical Studies</td>\n",
       "      <td>$47,800</td>\n",
       "      <td>47800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>479</td>\n",
       "      <td>Social Work (SW)</td>\n",
       "      <td>$48,300</td>\n",
       "      <td>48300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Major Mid Career Pay  \\\n",
       "488               Early Childhood Education       $40,400    \n",
       "487                  Child & Family Studies       $42,100    \n",
       "486                   Veterinary Technology       $43,100    \n",
       "485  Early Childhood & Elementary Education       $43,600    \n",
       "484                       Child Development       $44,000    \n",
       "483                          Family Studies       $44,500    \n",
       "482                     Human Services (HS)       $46,500    \n",
       "481                         Photojournalism       $47,200    \n",
       "480                        Biblical Studies       $47,800    \n",
       "479                        Social Work (SW)       $48,300    \n",
       "\n",
       "     Mid_Career_Pay_Numeric  \n",
       "488                 40400.0  \n",
       "487                 42100.0  \n",
       "486                 43100.0  \n",
       "485                 43600.0  \n",
       "484                 44000.0  \n",
       "483                 44500.0  \n",
       "482                 46500.0  \n",
       "481                 47200.0  \n",
       "480                 47800.0  \n",
       "479                 48300.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df1 = pd.DataFrame({\"Major\": earlyMidPayDF[\"Major\"], \"Mid Career Pay\": earlyMidPayDF[\"Mid Career Pay\"]})\n",
    "new_df1['Mid_Career_Pay_Numeric'] = new_df1['Mid Career Pay'].replace('[\\$,]', '', regex=True).astype(float)\n",
    "#new_df1\n",
    "new_df2 = new_df1.sort_values('Mid_Career_Pay_Numeric', ascending = True)[:10]\n",
    "new_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the ticks for our bar chart's x axis\n",
    "# Give the chart a title, x label, and y label\n",
    "Bottom10_df = new_df2.sort_values('Mid_Career_Pay_Numeric')\n",
    "Bottom10_df \n",
    "x_axis1 = np.arange(0, len(Bottom10_df.Major))\n",
    "tick_locations = []\n",
    "for x in x_axis1:\n",
    "    tick_locations.append(x)\n",
    "plt.title(\"Bottom 10 College Majors with Lowest Return on Investment\")\n",
    "plt.xlabel(\"Under Graduate Major\")\n",
    "plt.ylabel(\"Salary\")\n",
    "tick_locations = [value for value in x_axis1]\n",
    "tick_locations\n",
    "plt.xticks(tick_locations, Bottom10_df.Major, rotation = 45, horizontalalignment=\"right\")\n",
    "plt.bar(x_axis1, Bottom10_df.Mid_Career_Pay_Numeric, color=\"g\", align=\"center\")\n",
    "\n",
    "plt.savefig('Images/LeastRoi.png', bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observations\n",
    "The college majors with the lowest return on investment are..\n",
    "The bottom 5 are very similar in terms of the field in that they \n",
    "are all having to do with earyl childhood education/ development with the exception of veterinary technology.\n",
    "There is a significant difference between the pay scales of the top 10 and bottom 10 majors (175,000 𝑡𝑜 125,000) \n",
    "All bottom five seem to be very close in pay, ranging around $40,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Undergraduate Major</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Percent change from Starting to Mid-Career Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Math</td>\n",
       "      <td>$45,400.00</td>\n",
       "      <td>$92,400.00</td>\n",
       "      <td>103.5</td>\n",
       "      <td>$45,200.00</td>\n",
       "      <td>$64,200.00</td>\n",
       "      <td>$128,000.00</td>\n",
       "      <td>$183,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Philosophy</td>\n",
       "      <td>$39,900.00</td>\n",
       "      <td>$81,200.00</td>\n",
       "      <td>103.5</td>\n",
       "      <td>$35,500.00</td>\n",
       "      <td>$52,800.00</td>\n",
       "      <td>$127,000.00</td>\n",
       "      <td>$168,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>International Relations</td>\n",
       "      <td>$40,900.00</td>\n",
       "      <td>$80,900.00</td>\n",
       "      <td>97.8</td>\n",
       "      <td>$38,200.00</td>\n",
       "      <td>$56,000.00</td>\n",
       "      <td>$111,000.00</td>\n",
       "      <td>$157,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Economics</td>\n",
       "      <td>$50,100.00</td>\n",
       "      <td>$98,600.00</td>\n",
       "      <td>96.8</td>\n",
       "      <td>$50,600.00</td>\n",
       "      <td>$70,600.00</td>\n",
       "      <td>$145,000.00</td>\n",
       "      <td>$210,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>$40,800.00</td>\n",
       "      <td>$79,600.00</td>\n",
       "      <td>95.1</td>\n",
       "      <td>$42,100.00</td>\n",
       "      <td>$55,600.00</td>\n",
       "      <td>$119,000.00</td>\n",
       "      <td>$175,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Physics</td>\n",
       "      <td>$50,300.00</td>\n",
       "      <td>$97,300.00</td>\n",
       "      <td>93.4</td>\n",
       "      <td>$56,000.00</td>\n",
       "      <td>$74,200.00</td>\n",
       "      <td>$132,000.00</td>\n",
       "      <td>$178,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Political Science</td>\n",
       "      <td>$40,800.00</td>\n",
       "      <td>$78,200.00</td>\n",
       "      <td>91.7</td>\n",
       "      <td>$41,200.00</td>\n",
       "      <td>$55,300.00</td>\n",
       "      <td>$114,000.00</td>\n",
       "      <td>$168,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>$42,600.00</td>\n",
       "      <td>$79,900.00</td>\n",
       "      <td>87.6</td>\n",
       "      <td>$45,300.00</td>\n",
       "      <td>$60,700.00</td>\n",
       "      <td>$108,000.00</td>\n",
       "      <td>$148,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>Journalism</td>\n",
       "      <td>$35,600.00</td>\n",
       "      <td>$66,700.00</td>\n",
       "      <td>87.4</td>\n",
       "      <td>$38,400.00</td>\n",
       "      <td>$48,300.00</td>\n",
       "      <td>$97,700.00</td>\n",
       "      <td>$145,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Architecture</td>\n",
       "      <td>$41,600.00</td>\n",
       "      <td>$76,800.00</td>\n",
       "      <td>84.6</td>\n",
       "      <td>$50,600.00</td>\n",
       "      <td>$62,200.00</td>\n",
       "      <td>$97,000.00</td>\n",
       "      <td>$136,000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Undergraduate Major Starting Median Salary Mid-Career Median Salary  \\\n",
       "37                     Math            $45,400.00               $92,400.00    \n",
       "42               Philosophy            $39,900.00               $81,200.00    \n",
       "33  International Relations            $40,900.00               $80,900.00    \n",
       "17                Economics            $50,100.00               $98,600.00    \n",
       "36                Marketing            $40,800.00               $79,600.00    \n",
       "44                  Physics            $50,300.00               $97,300.00    \n",
       "45        Political Science            $40,800.00               $78,200.00    \n",
       "9                 Chemistry            $42,600.00               $79,900.00    \n",
       "34               Journalism            $35,600.00               $66,700.00    \n",
       "4              Architecture            $41,600.00               $76,800.00    \n",
       "\n",
       "    Percent change from Starting to Mid-Career Salary  \\\n",
       "37                                              103.5   \n",
       "42                                              103.5   \n",
       "33                                               97.8   \n",
       "17                                               96.8   \n",
       "36                                               95.1   \n",
       "44                                               93.4   \n",
       "45                                               91.7   \n",
       "9                                                87.6   \n",
       "34                                               87.4   \n",
       "4                                                84.6   \n",
       "\n",
       "   Mid-Career 10th Percentile Salary Mid-Career 25th Percentile Salary  \\\n",
       "37                       $45,200.00                        $64,200.00    \n",
       "42                       $35,500.00                        $52,800.00    \n",
       "33                       $38,200.00                        $56,000.00    \n",
       "17                       $50,600.00                        $70,600.00    \n",
       "36                       $42,100.00                        $55,600.00    \n",
       "44                       $56,000.00                        $74,200.00    \n",
       "45                       $41,200.00                        $55,300.00    \n",
       "9                        $45,300.00                        $60,700.00    \n",
       "34                       $38,400.00                        $48,300.00    \n",
       "4                        $50,600.00                        $62,200.00    \n",
       "\n",
       "   Mid-Career 75th Percentile Salary Mid-Career 90th Percentile Salary  \n",
       "37                      $128,000.00                       $183,000.00   \n",
       "42                      $127,000.00                       $168,000.00   \n",
       "33                      $111,000.00                       $157,000.00   \n",
       "17                      $145,000.00                       $210,000.00   \n",
       "36                      $119,000.00                       $175,000.00   \n",
       "44                      $132,000.00                       $178,000.00   \n",
       "45                      $114,000.00                       $168,000.00   \n",
       "9                       $108,000.00                       $148,000.00   \n",
       "34                       $97,700.00                       $145,000.00   \n",
       "4                        $97,000.00                       $136,000.00   "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the undergraduate majors with best potential for increase\n",
    "# track percent change of majors with biggest percent change\n",
    "# Rank top 10 jumps in salary\n",
    "top10CareerJumps = DegreesDF.sort_values('Percent change from Starting to Mid-Career Salary', ascending = False)\n",
    "top10CareerJumps[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Undergraduate Major</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Percent change from Starting to Mid-Career Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>MidCareer10thNumber</th>\n",
       "      <th>MidCareer90thNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>$74,300.00</td>\n",
       "      <td>$91,700.00</td>\n",
       "      <td>23.4</td>\n",
       "      <td>$66,400.00</td>\n",
       "      <td>$75,200.00</td>\n",
       "      <td>$108,000.00</td>\n",
       "      <td>$124,000.00</td>\n",
       "      <td>66400.0</td>\n",
       "      <td>124000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>$54,200.00</td>\n",
       "      <td>$67,000.00</td>\n",
       "      <td>23.6</td>\n",
       "      <td>$47,600.00</td>\n",
       "      <td>$56,400.00</td>\n",
       "      <td>$80,900.00</td>\n",
       "      <td>$98,300.00</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>98300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Nutrition</td>\n",
       "      <td>$39,900.00</td>\n",
       "      <td>$55,300.00</td>\n",
       "      <td>38.6</td>\n",
       "      <td>$33,900.00</td>\n",
       "      <td>$44,500.00</td>\n",
       "      <td>$70,500.00</td>\n",
       "      <td>$99,200.00</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>99200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Interior Design</td>\n",
       "      <td>$36,100.00</td>\n",
       "      <td>$53,200.00</td>\n",
       "      <td>47.4</td>\n",
       "      <td>$35,700.00</td>\n",
       "      <td>$42,600.00</td>\n",
       "      <td>$72,500.00</td>\n",
       "      <td>$107,000.00</td>\n",
       "      <td>35700.0</td>\n",
       "      <td>107000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Education</td>\n",
       "      <td>$34,900.00</td>\n",
       "      <td>$52,000.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>$29,300.00</td>\n",
       "      <td>$37,900.00</td>\n",
       "      <td>$73,400.00</td>\n",
       "      <td>$102,000.00</td>\n",
       "      <td>29300.0</td>\n",
       "      <td>102000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Hospitality &amp; Tourism</td>\n",
       "      <td>$37,800.00</td>\n",
       "      <td>$57,500.00</td>\n",
       "      <td>52.1</td>\n",
       "      <td>$35,500.00</td>\n",
       "      <td>$43,600.00</td>\n",
       "      <td>$81,900.00</td>\n",
       "      <td>$124,000.00</td>\n",
       "      <td>35500.0</td>\n",
       "      <td>124000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Information Technology (IT)</td>\n",
       "      <td>$49,100.00</td>\n",
       "      <td>$74,800.00</td>\n",
       "      <td>52.3</td>\n",
       "      <td>$44,500.00</td>\n",
       "      <td>$56,700.00</td>\n",
       "      <td>$96,700.00</td>\n",
       "      <td>$129,000.00</td>\n",
       "      <td>44500.0</td>\n",
       "      <td>129000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Religion</td>\n",
       "      <td>$34,100.00</td>\n",
       "      <td>$52,000.00</td>\n",
       "      <td>52.5</td>\n",
       "      <td>$29,700.00</td>\n",
       "      <td>$36,500.00</td>\n",
       "      <td>$70,900.00</td>\n",
       "      <td>$96,400.00</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>96400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Music</td>\n",
       "      <td>$35,900.00</td>\n",
       "      <td>$55,000.00</td>\n",
       "      <td>53.2</td>\n",
       "      <td>$26,700.00</td>\n",
       "      <td>$40,200.00</td>\n",
       "      <td>$88,000.00</td>\n",
       "      <td>$134,000.00</td>\n",
       "      <td>26700.0</td>\n",
       "      <td>134000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>$34,000.00</td>\n",
       "      <td>$53,100.00</td>\n",
       "      <td>56.2</td>\n",
       "      <td>$31,000.00</td>\n",
       "      <td>$40,000.00</td>\n",
       "      <td>$76,800.00</td>\n",
       "      <td>$96,400.00</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>96400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Undergraduate Major Starting Median Salary  \\\n",
       "43          Physician Assistant            $74,300.00    \n",
       "40                      Nursing            $54,200.00    \n",
       "41                    Nutrition            $39,900.00    \n",
       "32              Interior Design            $36,100.00    \n",
       "18                    Education            $34,900.00    \n",
       "29        Hospitality & Tourism            $37,800.00    \n",
       "31  Information Technology (IT)            $49,100.00    \n",
       "47                     Religion            $34,100.00    \n",
       "39                        Music            $35,900.00    \n",
       "49                      Spanish            $34,000.00    \n",
       "\n",
       "   Mid-Career Median Salary  \\\n",
       "43              $91,700.00    \n",
       "40              $67,000.00    \n",
       "41              $55,300.00    \n",
       "32              $53,200.00    \n",
       "18              $52,000.00    \n",
       "29              $57,500.00    \n",
       "31              $74,800.00    \n",
       "47              $52,000.00    \n",
       "39              $55,000.00    \n",
       "49              $53,100.00    \n",
       "\n",
       "    Percent change from Starting to Mid-Career Salary  \\\n",
       "43                                               23.4   \n",
       "40                                               23.6   \n",
       "41                                               38.6   \n",
       "32                                               47.4   \n",
       "18                                               49.0   \n",
       "29                                               52.1   \n",
       "31                                               52.3   \n",
       "47                                               52.5   \n",
       "39                                               53.2   \n",
       "49                                               56.2   \n",
       "\n",
       "   Mid-Career 10th Percentile Salary Mid-Career 25th Percentile Salary  \\\n",
       "43                       $66,400.00                        $75,200.00    \n",
       "40                       $47,600.00                        $56,400.00    \n",
       "41                       $33,900.00                        $44,500.00    \n",
       "32                       $35,700.00                        $42,600.00    \n",
       "18                       $29,300.00                        $37,900.00    \n",
       "29                       $35,500.00                        $43,600.00    \n",
       "31                       $44,500.00                        $56,700.00    \n",
       "47                       $29,700.00                        $36,500.00    \n",
       "39                       $26,700.00                        $40,200.00    \n",
       "49                       $31,000.00                        $40,000.00    \n",
       "\n",
       "   Mid-Career 75th Percentile Salary Mid-Career 90th Percentile Salary  \\\n",
       "43                      $108,000.00                       $124,000.00    \n",
       "40                       $80,900.00                        $98,300.00    \n",
       "41                       $70,500.00                        $99,200.00    \n",
       "32                       $72,500.00                       $107,000.00    \n",
       "18                       $73,400.00                       $102,000.00    \n",
       "29                       $81,900.00                       $124,000.00    \n",
       "31                       $96,700.00                       $129,000.00    \n",
       "47                       $70,900.00                        $96,400.00    \n",
       "39                       $88,000.00                       $134,000.00    \n",
       "49                       $76,800.00                        $96,400.00    \n",
       "\n",
       "    MidCareer10thNumber  MidCareer90thNumber  \n",
       "43              66400.0             124000.0  \n",
       "40              47600.0              98300.0  \n",
       "41              33900.0              99200.0  \n",
       "32              35700.0             107000.0  \n",
       "18              29300.0             102000.0  \n",
       "29              35500.0             124000.0  \n",
       "31              44500.0             129000.0  \n",
       "47              29700.0              96400.0  \n",
       "39              26700.0             134000.0  \n",
       "49              31000.0              96400.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the undergraduate majors with least potential for increase\n",
    "# track percent change of majors with least percent change\n",
    "# Rank bottom 10 jumps in salary\n",
    "bottom10CareerJumps = DegreesDF.sort_values('Percent change from Starting to Mid-Career Salary', ascending = True)\n",
    "bottom10CareerJumps[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Undergraduate Major</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Percent change from Starting to Mid-Career Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>MidCareer10thNumber</th>\n",
       "      <th>MidCareer90thNumber</th>\n",
       "      <th>Mid-CareerMeanNumber</th>\n",
       "      <th>10th90thDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Economics</td>\n",
       "      <td>$50,100.00</td>\n",
       "      <td>$98,600.00</td>\n",
       "      <td>96.8</td>\n",
       "      <td>$50,600.00</td>\n",
       "      <td>$70,600.00</td>\n",
       "      <td>$145,000.00</td>\n",
       "      <td>$210,000.00</td>\n",
       "      <td>50600.0</td>\n",
       "      <td>210000.0</td>\n",
       "      <td>98600.0</td>\n",
       "      <td>159400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Finance</td>\n",
       "      <td>$47,900.00</td>\n",
       "      <td>$88,300.00</td>\n",
       "      <td>84.3</td>\n",
       "      <td>$47,200.00</td>\n",
       "      <td>$62,100.00</td>\n",
       "      <td>$128,000.00</td>\n",
       "      <td>$195,000.00</td>\n",
       "      <td>47200.0</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>88300.0</td>\n",
       "      <td>147800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Math</td>\n",
       "      <td>$45,400.00</td>\n",
       "      <td>$92,400.00</td>\n",
       "      <td>103.5</td>\n",
       "      <td>$45,200.00</td>\n",
       "      <td>$64,200.00</td>\n",
       "      <td>$128,000.00</td>\n",
       "      <td>$183,000.00</td>\n",
       "      <td>45200.0</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>92400.0</td>\n",
       "      <td>137800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>$40,800.00</td>\n",
       "      <td>$79,600.00</td>\n",
       "      <td>95.1</td>\n",
       "      <td>$42,100.00</td>\n",
       "      <td>$55,600.00</td>\n",
       "      <td>$119,000.00</td>\n",
       "      <td>$175,000.00</td>\n",
       "      <td>42100.0</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>79600.0</td>\n",
       "      <td>132900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Philosophy</td>\n",
       "      <td>$39,900.00</td>\n",
       "      <td>$81,200.00</td>\n",
       "      <td>103.5</td>\n",
       "      <td>$35,500.00</td>\n",
       "      <td>$52,800.00</td>\n",
       "      <td>$127,000.00</td>\n",
       "      <td>$168,000.00</td>\n",
       "      <td>35500.0</td>\n",
       "      <td>168000.0</td>\n",
       "      <td>81200.0</td>\n",
       "      <td>132500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Political Science</td>\n",
       "      <td>$40,800.00</td>\n",
       "      <td>$78,200.00</td>\n",
       "      <td>91.7</td>\n",
       "      <td>$41,200.00</td>\n",
       "      <td>$55,300.00</td>\n",
       "      <td>$114,000.00</td>\n",
       "      <td>$168,000.00</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>168000.0</td>\n",
       "      <td>78200.0</td>\n",
       "      <td>126800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Chemical Engineering</td>\n",
       "      <td>$63,200.00</td>\n",
       "      <td>$107,000.00</td>\n",
       "      <td>69.3</td>\n",
       "      <td>$71,900.00</td>\n",
       "      <td>$87,300.00</td>\n",
       "      <td>$143,000.00</td>\n",
       "      <td>$194,000.00</td>\n",
       "      <td>71900.0</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>122100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Physics</td>\n",
       "      <td>$50,300.00</td>\n",
       "      <td>$97,300.00</td>\n",
       "      <td>93.4</td>\n",
       "      <td>$56,000.00</td>\n",
       "      <td>$74,200.00</td>\n",
       "      <td>$132,000.00</td>\n",
       "      <td>$178,000.00</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>178000.0</td>\n",
       "      <td>97300.0</td>\n",
       "      <td>122000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>International Relations</td>\n",
       "      <td>$40,900.00</td>\n",
       "      <td>$80,900.00</td>\n",
       "      <td>97.8</td>\n",
       "      <td>$38,200.00</td>\n",
       "      <td>$56,000.00</td>\n",
       "      <td>$111,000.00</td>\n",
       "      <td>$157,000.00</td>\n",
       "      <td>38200.0</td>\n",
       "      <td>157000.0</td>\n",
       "      <td>80900.0</td>\n",
       "      <td>118800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Drama</td>\n",
       "      <td>$35,900.00</td>\n",
       "      <td>$56,900.00</td>\n",
       "      <td>58.5</td>\n",
       "      <td>$36,700.00</td>\n",
       "      <td>$41,300.00</td>\n",
       "      <td>$79,100.00</td>\n",
       "      <td>$153,000.00</td>\n",
       "      <td>36700.0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>56900.0</td>\n",
       "      <td>116300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Undergraduate Major Starting Median Salary Mid-Career Median Salary  \\\n",
       "17                Economics            $50,100.00               $98,600.00    \n",
       "22                  Finance            $47,900.00               $88,300.00    \n",
       "37                     Math            $45,400.00               $92,400.00    \n",
       "36                Marketing            $40,800.00               $79,600.00    \n",
       "42               Philosophy            $39,900.00               $81,200.00    \n",
       "45        Political Science            $40,800.00               $78,200.00    \n",
       "8      Chemical Engineering            $63,200.00              $107,000.00    \n",
       "44                  Physics            $50,300.00               $97,300.00    \n",
       "33  International Relations            $40,900.00               $80,900.00    \n",
       "16                    Drama            $35,900.00               $56,900.00    \n",
       "\n",
       "    Percent change from Starting to Mid-Career Salary  \\\n",
       "17                                               96.8   \n",
       "22                                               84.3   \n",
       "37                                              103.5   \n",
       "36                                               95.1   \n",
       "42                                              103.5   \n",
       "45                                               91.7   \n",
       "8                                                69.3   \n",
       "44                                               93.4   \n",
       "33                                               97.8   \n",
       "16                                               58.5   \n",
       "\n",
       "   Mid-Career 10th Percentile Salary Mid-Career 25th Percentile Salary  \\\n",
       "17                       $50,600.00                        $70,600.00    \n",
       "22                       $47,200.00                        $62,100.00    \n",
       "37                       $45,200.00                        $64,200.00    \n",
       "36                       $42,100.00                        $55,600.00    \n",
       "42                       $35,500.00                        $52,800.00    \n",
       "45                       $41,200.00                        $55,300.00    \n",
       "8                        $71,900.00                        $87,300.00    \n",
       "44                       $56,000.00                        $74,200.00    \n",
       "33                       $38,200.00                        $56,000.00    \n",
       "16                       $36,700.00                        $41,300.00    \n",
       "\n",
       "   Mid-Career 75th Percentile Salary Mid-Career 90th Percentile Salary  \\\n",
       "17                      $145,000.00                       $210,000.00    \n",
       "22                      $128,000.00                       $195,000.00    \n",
       "37                      $128,000.00                       $183,000.00    \n",
       "36                      $119,000.00                       $175,000.00    \n",
       "42                      $127,000.00                       $168,000.00    \n",
       "45                      $114,000.00                       $168,000.00    \n",
       "8                       $143,000.00                       $194,000.00    \n",
       "44                      $132,000.00                       $178,000.00    \n",
       "33                      $111,000.00                       $157,000.00    \n",
       "16                       $79,100.00                       $153,000.00    \n",
       "\n",
       "    MidCareer10thNumber  MidCareer90thNumber  Mid-CareerMeanNumber  \\\n",
       "17              50600.0             210000.0               98600.0   \n",
       "22              47200.0             195000.0               88300.0   \n",
       "37              45200.0             183000.0               92400.0   \n",
       "36              42100.0             175000.0               79600.0   \n",
       "42              35500.0             168000.0               81200.0   \n",
       "45              41200.0             168000.0               78200.0   \n",
       "8               71900.0             194000.0              107000.0   \n",
       "44              56000.0             178000.0               97300.0   \n",
       "33              38200.0             157000.0               80900.0   \n",
       "16              36700.0             153000.0               56900.0   \n",
       "\n",
       "    10th90thDiff  \n",
       "17      159400.0  \n",
       "22      147800.0  \n",
       "37      137800.0  \n",
       "36      132900.0  \n",
       "42      132500.0  \n",
       "45      126800.0  \n",
       "8       122100.0  \n",
       "44      122000.0  \n",
       "33      118800.0  \n",
       "16      116300.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# biggest difference on top and bottom half\n",
    "# difference between 10th nd 90th percentiles\n",
    "# add 2 columns to represent number values for percentiles\n",
    "DegreesDF['MidCareer10thNumber'] = DegreesDF['Mid-Career 10th Percentile Salary'].replace('[\\$,]', '', regex=True).astype(float)\n",
    "DegreesDF['MidCareer90thNumber'] = DegreesDF['Mid-Career 90th Percentile Salary'].replace('[\\$,]', '', regex=True).astype(float)\n",
    "DegreesDF['10th90thDiff'] = DegreesDF['MidCareer90thNumber'] - DegreesDF['MidCareer10thNumber']\n",
    "DegreesDF.sort_values('10th90thDiff', ascending = False)[:10]\n",
    "#DegreesDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "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>Undergraduate Major</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Percent change from Starting to Mid-Career Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>MidCareer10thNumber</th>\n",
       "      <th>MidCareer90thNumber</th>\n",
       "      <th>Mid-CareerMeanNumber</th>\n",
       "      <th>10th90thDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>$54,200.00</td>\n",
       "      <td>$67,000.00</td>\n",
       "      <td>23.6</td>\n",
       "      <td>$47,600.00</td>\n",
       "      <td>$56,400.00</td>\n",
       "      <td>$80,900.00</td>\n",
       "      <td>$98,300.00</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>98300.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>50700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>$74,300.00</td>\n",
       "      <td>$91,700.00</td>\n",
       "      <td>23.4</td>\n",
       "      <td>$66,400.00</td>\n",
       "      <td>$75,200.00</td>\n",
       "      <td>$108,000.00</td>\n",
       "      <td>$124,000.00</td>\n",
       "      <td>66400.0</td>\n",
       "      <td>124000.0</td>\n",
       "      <td>91700.0</td>\n",
       "      <td>57600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Nutrition</td>\n",
       "      <td>$39,900.00</td>\n",
       "      <td>$55,300.00</td>\n",
       "      <td>38.6</td>\n",
       "      <td>$33,900.00</td>\n",
       "      <td>$44,500.00</td>\n",
       "      <td>$70,500.00</td>\n",
       "      <td>$99,200.00</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>99200.0</td>\n",
       "      <td>55300.0</td>\n",
       "      <td>65300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>$34,000.00</td>\n",
       "      <td>$53,100.00</td>\n",
       "      <td>56.2</td>\n",
       "      <td>$31,000.00</td>\n",
       "      <td>$40,000.00</td>\n",
       "      <td>$76,800.00</td>\n",
       "      <td>$96,400.00</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>65400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Health Care Administration</td>\n",
       "      <td>$38,800.00</td>\n",
       "      <td>$60,600.00</td>\n",
       "      <td>56.2</td>\n",
       "      <td>$34,600.00</td>\n",
       "      <td>$45,600.00</td>\n",
       "      <td>$78,800.00</td>\n",
       "      <td>$101,000.00</td>\n",
       "      <td>34600.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>60600.0</td>\n",
       "      <td>66400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Religion</td>\n",
       "      <td>$34,100.00</td>\n",
       "      <td>$52,000.00</td>\n",
       "      <td>52.5</td>\n",
       "      <td>$29,700.00</td>\n",
       "      <td>$36,500.00</td>\n",
       "      <td>$70,900.00</td>\n",
       "      <td>$96,400.00</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>66700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Forestry</td>\n",
       "      <td>$39,100.00</td>\n",
       "      <td>$62,600.00</td>\n",
       "      <td>60.1</td>\n",
       "      <td>$41,000.00</td>\n",
       "      <td>$49,300.00</td>\n",
       "      <td>$78,200.00</td>\n",
       "      <td>$111,000.00</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>111000.0</td>\n",
       "      <td>62600.0</td>\n",
       "      <td>70000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Interior Design</td>\n",
       "      <td>$36,100.00</td>\n",
       "      <td>$53,200.00</td>\n",
       "      <td>47.4</td>\n",
       "      <td>$35,700.00</td>\n",
       "      <td>$42,600.00</td>\n",
       "      <td>$72,500.00</td>\n",
       "      <td>$107,000.00</td>\n",
       "      <td>35700.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>53200.0</td>\n",
       "      <td>71300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Education</td>\n",
       "      <td>$34,900.00</td>\n",
       "      <td>$52,000.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>$29,300.00</td>\n",
       "      <td>$37,900.00</td>\n",
       "      <td>$73,400.00</td>\n",
       "      <td>$102,000.00</td>\n",
       "      <td>29300.0</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>72700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Criminal Justice</td>\n",
       "      <td>$35,000.00</td>\n",
       "      <td>$56,300.00</td>\n",
       "      <td>60.9</td>\n",
       "      <td>$32,200.00</td>\n",
       "      <td>$41,600.00</td>\n",
       "      <td>$80,700.00</td>\n",
       "      <td>$107,000.00</td>\n",
       "      <td>32200.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>74800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Undergraduate Major Starting Median Salary  \\\n",
       "40                     Nursing            $54,200.00    \n",
       "43         Physician Assistant            $74,300.00    \n",
       "41                   Nutrition            $39,900.00    \n",
       "49                     Spanish            $34,000.00    \n",
       "27  Health Care Administration            $38,800.00    \n",
       "47                    Religion            $34,100.00    \n",
       "23                    Forestry            $39,100.00    \n",
       "32             Interior Design            $36,100.00    \n",
       "18                   Education            $34,900.00    \n",
       "15            Criminal Justice            $35,000.00    \n",
       "\n",
       "   Mid-Career Median Salary  \\\n",
       "40              $67,000.00    \n",
       "43              $91,700.00    \n",
       "41              $55,300.00    \n",
       "49              $53,100.00    \n",
       "27              $60,600.00    \n",
       "47              $52,000.00    \n",
       "23              $62,600.00    \n",
       "32              $53,200.00    \n",
       "18              $52,000.00    \n",
       "15              $56,300.00    \n",
       "\n",
       "    Percent change from Starting to Mid-Career Salary  \\\n",
       "40                                               23.6   \n",
       "43                                               23.4   \n",
       "41                                               38.6   \n",
       "49                                               56.2   \n",
       "27                                               56.2   \n",
       "47                                               52.5   \n",
       "23                                               60.1   \n",
       "32                                               47.4   \n",
       "18                                               49.0   \n",
       "15                                               60.9   \n",
       "\n",
       "   Mid-Career 10th Percentile Salary Mid-Career 25th Percentile Salary  \\\n",
       "40                       $47,600.00                        $56,400.00    \n",
       "43                       $66,400.00                        $75,200.00    \n",
       "41                       $33,900.00                        $44,500.00    \n",
       "49                       $31,000.00                        $40,000.00    \n",
       "27                       $34,600.00                        $45,600.00    \n",
       "47                       $29,700.00                        $36,500.00    \n",
       "23                       $41,000.00                        $49,300.00    \n",
       "32                       $35,700.00                        $42,600.00    \n",
       "18                       $29,300.00                        $37,900.00    \n",
       "15                       $32,200.00                        $41,600.00    \n",
       "\n",
       "   Mid-Career 75th Percentile Salary Mid-Career 90th Percentile Salary  \\\n",
       "40                       $80,900.00                        $98,300.00    \n",
       "43                      $108,000.00                       $124,000.00    \n",
       "41                       $70,500.00                        $99,200.00    \n",
       "49                       $76,800.00                        $96,400.00    \n",
       "27                       $78,800.00                       $101,000.00    \n",
       "47                       $70,900.00                        $96,400.00    \n",
       "23                       $78,200.00                       $111,000.00    \n",
       "32                       $72,500.00                       $107,000.00    \n",
       "18                       $73,400.00                       $102,000.00    \n",
       "15                       $80,700.00                       $107,000.00    \n",
       "\n",
       "    MidCareer10thNumber  MidCareer90thNumber  Mid-CareerMeanNumber  \\\n",
       "40              47600.0              98300.0               67000.0   \n",
       "43              66400.0             124000.0               91700.0   \n",
       "41              33900.0              99200.0               55300.0   \n",
       "49              31000.0              96400.0               53100.0   \n",
       "27              34600.0             101000.0               60600.0   \n",
       "47              29700.0              96400.0               52000.0   \n",
       "23              41000.0             111000.0               62600.0   \n",
       "32              35700.0             107000.0               53200.0   \n",
       "18              29300.0             102000.0               52000.0   \n",
       "15              32200.0             107000.0               56300.0   \n",
       "\n",
       "    10th90thDiff  \n",
       "40       50700.0  \n",
       "43       57600.0  \n",
       "41       65300.0  \n",
       "49       65400.0  \n",
       "27       66400.0  \n",
       "47       66700.0  \n",
       "23       70000.0  \n",
       "32       71300.0  \n",
       "18       72700.0  \n",
       "15       74800.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#careers with least variance between top and bottom\n",
    "#careers with least variance between top and bottom\n",
    "DegreesDF.sort_values('10th90thDiff')[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert median salary to number\n",
    "DegreesDF['Mid-CareerMeanNumber'] = DegreesDF['Mid-Career Median Salary'].replace('[\\$,]', '', regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "258836738.7755102"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics\n",
    "#variance\n",
    "DegreesDF['Mid-CareerMeanNumber'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16088.403860405488"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standard deviation\n",
    "DegreesDF['Mid-CareerMeanNumber'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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