day71/Day71.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"salaries_by_college_major.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>42200.0</td>\n",
       "      <td>152000.0</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aerospace Engineering</td>\n",
       "      <td>57700.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>64300.0</td>\n",
       "      <td>161000.0</td>\n",
       "      <td>STEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>42600.0</td>\n",
       "      <td>71900.0</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anthropology</td>\n",
       "      <td>36800.0</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>33800.0</td>\n",
       "      <td>138000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Architecture</td>\n",
       "      <td>41600.0</td>\n",
       "      <td>76800.0</td>\n",
       "      <td>50600.0</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Undergraduate Major  Starting Median Salary  Mid-Career Median Salary  \\\n",
       "0             Accounting                 46000.0                   77100.0   \n",
       "1  Aerospace Engineering                 57700.0                  101000.0   \n",
       "2            Agriculture                 42600.0                   71900.0   \n",
       "3           Anthropology                 36800.0                   61500.0   \n",
       "4           Architecture                 41600.0                   76800.0   \n",
       "\n",
       "   Mid-Career 10th Percentile Salary  Mid-Career 90th Percentile Salary  \\\n",
       "0                            42200.0                           152000.0   \n",
       "1                            64300.0                           161000.0   \n",
       "2                            36300.0                           150000.0   \n",
       "3                            33800.0                           138000.0   \n",
       "4                            50600.0                           136000.0   \n",
       "\n",
       "      Group  \n",
       "0  Business  \n",
       "1      STEM  \n",
       "2  Business  \n",
       "3      HASS  \n",
       "4  Business  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the top 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the number of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Undergraduate Major', 'Starting Median Salary',\n",
       "       'Mid-Career Median Salary', 'Mid-Career 10th Percentile Salary',\n",
       "       'Mid-Career 90th Percentile Salary', 'Group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display column names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Undergraduate Major  Starting Median Salary  Mid-Career Median Salary  \\\n",
       "0                 False                   False                     False   \n",
       "1                 False                   False                     False   \n",
       "2                 False                   False                     False   \n",
       "3                 False                   False                     False   \n",
       "4                 False                   False                     False   \n",
       "5                 False                   False                     False   \n",
       "6                 False                   False                     False   \n",
       "7                 False                   False                     False   \n",
       "8                 False                   False                     False   \n",
       "9                 False                   False                     False   \n",
       "10                False                   False                     False   \n",
       "11                False                   False                     False   \n",
       "12                False                   False                     False   \n",
       "13                False                   False                     False   \n",
       "14                False                   False                     False   \n",
       "15                False                   False                     False   \n",
       "16                False                   False                     False   \n",
       "17                False                   False                     False   \n",
       "18                False                   False                     False   \n",
       "19                False                   False                     False   \n",
       "20                False                   False                     False   \n",
       "21                False                   False                     False   \n",
       "22                False                   False                     False   \n",
       "23                False                   False                     False   \n",
       "24                False                   False                     False   \n",
       "25                False                   False                     False   \n",
       "26                False                   False                     False   \n",
       "27                False                   False                     False   \n",
       "28                False                   False                     False   \n",
       "29                False                   False                     False   \n",
       "30                False                   False                     False   \n",
       "31                False                   False                     False   \n",
       "32                False                   False                     False   \n",
       "33                False                   False                     False   \n",
       "34                False                   False                     False   \n",
       "35                False                   False                     False   \n",
       "36                False                   False                     False   \n",
       "37                False                   False                     False   \n",
       "38                False                   False                     False   \n",
       "39                False                   False                     False   \n",
       "40                False                   False                     False   \n",
       "41                False                   False                     False   \n",
       "42                False                   False                     False   \n",
       "43                False                   False                     False   \n",
       "44                False                   False                     False   \n",
       "45                False                   False                     False   \n",
       "46                False                   False                     False   \n",
       "47                False                   False                     False   \n",
       "48                False                   False                     False   \n",
       "49                False                   False                     False   \n",
       "50                False                    True                      True   \n",
       "\n",
       "    Mid-Career 10th Percentile Salary  Mid-Career 90th Percentile Salary  \\\n",
       "0                               False                              False   \n",
       "1                               False                              False   \n",
       "2                               False                              False   \n",
       "3                               False                              False   \n",
       "4                               False                              False   \n",
       "5                               False                              False   \n",
       "6                               False                              False   \n",
       "7                               False                              False   \n",
       "8                               False                              False   \n",
       "9                               False                              False   \n",
       "10                              False                              False   \n",
       "11                              False                              False   \n",
       "12                              False                              False   \n",
       "13                              False                              False   \n",
       "14                              False                              False   \n",
       "15                              False                              False   \n",
       "16                              False                              False   \n",
       "17                              False                              False   \n",
       "18                              False                              False   \n",
       "19                              False                              False   \n",
       "20                              False                              False   \n",
       "21                              False                              False   \n",
       "22                              False                              False   \n",
       "23                              False                              False   \n",
       "24                              False                              False   \n",
       "25                              False                              False   \n",
       "26                              False                              False   \n",
       "27                              False                              False   \n",
       "28                              False                              False   \n",
       "29                              False                              False   \n",
       "30                              False                              False   \n",
       "31                              False                              False   \n",
       "32                              False                              False   \n",
       "33                              False                              False   \n",
       "34                              False                              False   \n",
       "35                              False                              False   \n",
       "36                              False                              False   \n",
       "37                              False                              False   \n",
       "38                              False                              False   \n",
       "39                              False                              False   \n",
       "40                              False                              False   \n",
       "41                              False                              False   \n",
       "42                              False                              False   \n",
       "43                              False                              False   \n",
       "44                              False                              False   \n",
       "45                              False                              False   \n",
       "46                              False                              False   \n",
       "47                              False                              False   \n",
       "48                              False                              False   \n",
       "49                              False                              False   \n",
       "50                               True                               True   \n",
       "\n",
       "    Group  \n",
       "0   False  \n",
       "1   False  \n",
       "2   False  \n",
       "3   False  \n",
       "4   False  \n",
       "5   False  \n",
       "6   False  \n",
       "7   False  \n",
       "8   False  \n",
       "9   False  \n",
       "10  False  \n",
       "11  False  \n",
       "12  False  \n",
       "13  False  \n",
       "14  False  \n",
       "15  False  \n",
       "16  False  \n",
       "17  False  \n",
       "18  False  \n",
       "19  False  \n",
       "20  False  \n",
       "21  False  \n",
       "22  False  \n",
       "23  False  \n",
       "24  False  \n",
       "25  False  \n",
       "26  False  \n",
       "27  False  \n",
       "28  False  \n",
       "29  False  \n",
       "30  False  \n",
       "31  False  \n",
       "32  False  \n",
       "33  False  \n",
       "34  False  \n",
       "35  False  \n",
       "36  False  \n",
       "37  False  \n",
       "38  False  \n",
       "39  False  \n",
       "40  False  \n",
       "41  False  \n",
       "42  False  \n",
       "43  False  \n",
       "44  False  \n",
       "45  False  \n",
       "46  False  \n",
       "47  False  \n",
       "48  False  \n",
       "49  False  \n",
       "50   True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for NaN entries\n",
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Undergraduate Major</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Psychology</td>\n",
       "      <td>35900.0</td>\n",
       "      <td>60400.0</td>\n",
       "      <td>31600.0</td>\n",
       "      <td>127000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Religion</td>\n",
       "      <td>34100.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Sociology</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>58200.0</td>\n",
       "      <td>30700.0</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Source: PayScale Inc.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Undergraduate Major  Starting Median Salary  Mid-Career Median Salary  \\\n",
       "46             Psychology                 35900.0                   60400.0   \n",
       "47               Religion                 34100.0                   52000.0   \n",
       "48              Sociology                 36500.0                   58200.0   \n",
       "49                Spanish                 34000.0                   53100.0   \n",
       "50  Source: PayScale Inc.                     NaN                       NaN   \n",
       "\n",
       "    Mid-Career 10th Percentile Salary  Mid-Career 90th Percentile Salary Group  \n",
       "46                            31600.0                           127000.0  HASS  \n",
       "47                            29700.0                            96400.0  HASS  \n",
       "48                            30700.0                           118000.0  HASS  \n",
       "49                            31000.0                            96400.0  HASS  \n",
       "50                                NaN                                NaN   NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete rows with NaN entries\n",
    "clean_df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Political Science</td>\n",
       "      <td>40800.0</td>\n",
       "      <td>78200.0</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>168000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Psychology</td>\n",
       "      <td>35900.0</td>\n",
       "      <td>60400.0</td>\n",
       "      <td>31600.0</td>\n",
       "      <td>127000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Religion</td>\n",
       "      <td>34100.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Sociology</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>58200.0</td>\n",
       "      <td>30700.0</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Undergraduate Major  Starting Median Salary  Mid-Career Median Salary  \\\n",
       "45   Political Science                 40800.0                   78200.0   \n",
       "46          Psychology                 35900.0                   60400.0   \n",
       "47            Religion                 34100.0                   52000.0   \n",
       "48           Sociology                 36500.0                   58200.0   \n",
       "49             Spanish                 34000.0                   53100.0   \n",
       "\n",
       "    Mid-Career 10th Percentile Salary  Mid-Career 90th Percentile Salary Group  \n",
       "45                            41200.0                           168000.0  HASS  \n",
       "46                            31600.0                           127000.0  HASS  \n",
       "47                            29700.0                            96400.0  HASS  \n",
       "48                            30700.0                           118000.0  HASS  \n",
       "49                            31000.0                            96400.0  HASS  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the last 5 rows again\n",
    "clean_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     46000.0\n",
       "1     57700.0\n",
       "2     42600.0\n",
       "3     36800.0\n",
       "4     41600.0\n",
       "5     35800.0\n",
       "6     38800.0\n",
       "7     43000.0\n",
       "8     63200.0\n",
       "9     42600.0\n",
       "10    53900.0\n",
       "11    38100.0\n",
       "12    61400.0\n",
       "13    55900.0\n",
       "14    53700.0\n",
       "15    35000.0\n",
       "16    35900.0\n",
       "17    50100.0\n",
       "18    34900.0\n",
       "19    60900.0\n",
       "20    38000.0\n",
       "21    37900.0\n",
       "22    47900.0\n",
       "23    39100.0\n",
       "24    41200.0\n",
       "25    43500.0\n",
       "26    35700.0\n",
       "27    38800.0\n",
       "28    39200.0\n",
       "29    37800.0\n",
       "30    57700.0\n",
       "31    49100.0\n",
       "32    36100.0\n",
       "33    40900.0\n",
       "34    35600.0\n",
       "35    49200.0\n",
       "36    40800.0\n",
       "37    45400.0\n",
       "38    57900.0\n",
       "39    35900.0\n",
       "40    54200.0\n",
       "41    39900.0\n",
       "42    39900.0\n",
       "43    74300.0\n",
       "44    50300.0\n",
       "45    40800.0\n",
       "46    35900.0\n",
       "47    34100.0\n",
       "48    36500.0\n",
       "49    34000.0\n",
       "Name: Starting Median Salary, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access a particular column\n",
    "clean_df[\"Starting Median Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74300.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the maximum value\n",
    "clean_df[\"Starting Median Salary\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the id for rown containing the maximum value\n",
    "clean_df[\"Starting Median Salary\"].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Physician Assistant'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the matching entry in a different column\n",
    "clean_df[\"Undergraduate Major\"].loc[43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Physician Assistant'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternatively\n",
    "clean_df[\"Undergraduate Major\"][43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Undergraduate Major                  Physician Assistant\n",
       "Starting Median Salary                             74300\n",
       "Mid-Career Median Salary                           91700\n",
       "Mid-Career 10th Percentile Salary                  66400\n",
       "Mid-Career 90th Percentile Salary                 124000\n",
       "Group                                               STEM\n",
       "Name: 43, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the whole row with a particular ID\n",
    "clean_df.loc[43]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What college major has the highest mid-career salary? How much do graduates with this major earn?\n",
    "# (Mid-career is defined as having 10+ years of experience)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107000.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# highest mid-carreer salary\n",
    "clean_df[\"Mid-Career Median Salary\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Chemical Engineering'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matching what college major\n",
    "mid_max_id = clean_df[\"Mid-Career Median Salary\"].idxmax()\n",
    "clean_df[\"Undergraduate Major\"][mid_max_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Which college major has the lowest starting salary and how much do graduates earn after university?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34000.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lowest starting salary\n",
    "clean_df[\"Starting Median Salary\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Spanish'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matching what college major\n",
    "start_min_id = clean_df[\"Starting Median Salary\"].idxmin()\n",
    "clean_df[\"Undergraduate Major\"][start_min_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Which college major has the lowest mid-career salary and how much can people expect to earn with this degree? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52000.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lowest mid_career salary\n",
    "clean_df[\"Mid-Career Median Salary\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Education'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matching what college major\n",
    "mid_min_id = clean_df[\"Mid-Career Median Salary\"].idxmin()\n",
    "clean_df[\"Undergraduate Major\"][mid_min_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Undergraduate Major                  Education\n",
       "Starting Median Salary                   34900\n",
       "Mid-Career Median Salary                 52000\n",
       "Mid-Career 10th Percentile Salary        29300\n",
       "Mid-Career 90th Percentile Salary       102000\n",
       "Group                                     HASS\n",
       "Name: 18, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.loc[mid_min_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the difference between two columns\n",
    "spread = clean_df[\"Mid-Career 90th Percentile Salary\"] - clean_df[\"Mid-Career 10th Percentile Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Spread</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accounting</td>\n",
       "      <td>109800.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>42200.0</td>\n",
       "      <td>152000.0</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aerospace Engineering</td>\n",
       "      <td>96700.0</td>\n",
       "      <td>57700.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>64300.0</td>\n",
       "      <td>161000.0</td>\n",
       "      <td>STEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>113700.0</td>\n",
       "      <td>42600.0</td>\n",
       "      <td>71900.0</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anthropology</td>\n",
       "      <td>104200.0</td>\n",
       "      <td>36800.0</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>33800.0</td>\n",
       "      <td>138000.0</td>\n",
       "      <td>HASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Architecture</td>\n",
       "      <td>85400.0</td>\n",
       "      <td>41600.0</td>\n",
       "      <td>76800.0</td>\n",
       "      <td>50600.0</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Undergraduate Major    Spread  Starting Median Salary  \\\n",
       "0             Accounting  109800.0                 46000.0   \n",
       "1  Aerospace Engineering   96700.0                 57700.0   \n",
       "2            Agriculture  113700.0                 42600.0   \n",
       "3           Anthropology  104200.0                 36800.0   \n",
       "4           Architecture   85400.0                 41600.0   \n",
       "\n",
       "   Mid-Career Median Salary  Mid-Career 10th Percentile Salary  \\\n",
       "0                   77100.0                            42200.0   \n",
       "1                  101000.0                            64300.0   \n",
       "2                   71900.0                            36300.0   \n",
       "3                   61500.0                            33800.0   \n",
       "4                   76800.0                            50600.0   \n",
       "\n",
       "   Mid-Career 90th Percentile Salary     Group  \n",
       "0                           152000.0  Business  \n",
       "1                           161000.0      STEM  \n",
       "2                           150000.0  Business  \n",
       "3                           138000.0      HASS  \n",
       "4                           136000.0  Business  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add to the dataframe as a new column, at position 1\n",
    "clean_df.insert(1, \"Spread\", spread)\n",
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Nursing</td>\n",
       "      <td>50700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Physician Assistant</td>\n",
       "      <td>57600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Nutrition</td>\n",
       "      <td>65300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>65400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Health Care Administration</td>\n",
       "      <td>66400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Undergraduate Major   Spread\n",
       "40                     Nursing  50700.0\n",
       "43         Physician Assistant  57600.0\n",
       "41                   Nutrition  65300.0\n",
       "49                     Spanish  65400.0\n",
       "27  Health Care Administration  66400.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by a particular column\n",
    "low_risk = clean_df.sort_values(\"Spread\", ascending=True)\n",
    "low_risk[[\"Undergraduate Major\", \"Spread\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the top 5 degrees with the highest values in the 90th percentile. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Mid-Career 90th Percentile Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Economics</td>\n",
       "      <td>210000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Finance</td>\n",
       "      <td>195000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chemical Engineering</td>\n",
       "      <td>194000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Math</td>\n",
       "      <td>183000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Physics</td>\n",
       "      <td>178000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Undergraduate Major  Mid-Career 90th Percentile Salary\n",
       "17             Economics                           210000.0\n",
       "22               Finance                           195000.0\n",
       "8   Chemical Engineering                           194000.0\n",
       "37                  Math                           183000.0\n",
       "44               Physics                           178000.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_five = clean_df.sort_values(\"Mid-Career 90th Percentile Salary\", ascending=False)\n",
    "top_five[[\"Undergraduate Major\", \"Mid-Career 90th Percentile Salary\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Economics</td>\n",
       "      <td>159400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Finance</td>\n",
       "      <td>147800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Math</td>\n",
       "      <td>137800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Marketing</td>\n",
       "      <td>132900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Philosophy</td>\n",
       "      <td>132500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Undergraduate Major    Spread\n",
       "17           Economics  159400.0\n",
       "22             Finance  147800.0\n",
       "37                Math  137800.0\n",
       "36           Marketing  132900.0\n",
       "42          Philosophy  132500.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also, find the degrees with the greatest spread in salaries. \n",
    "# Which majors have the largest difference between high and low earners after graduation.\n",
    "spread = clean_df[\"Mid-Career 90th Percentile Salary\"] - clean_df[\"Mid-Career 10th Percentile Salary\"]\n",
    "high_risk = clean_df.sort_values(\"Spread\", ascending=False)\n",
    "high_risk[[\"Undergraduate Major\", \"Spread\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Mid-Career Median Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chemical Engineering</td>\n",
       "      <td>107000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Computer Engineering</td>\n",
       "      <td>105000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>103000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aerospace Engineering</td>\n",
       "      <td>101000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Economics</td>\n",
       "      <td>98600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Undergraduate Major  Mid-Career Median Salary\n",
       "8     Chemical Engineering                  107000.0\n",
       "12    Computer Engineering                  105000.0\n",
       "19  Electrical Engineering                  103000.0\n",
       "1    Aerospace Engineering                  101000.0\n",
       "17               Economics                   98600.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare highest spread vs. highest median salary\n",
    "high_median = clean_df.sort_values(\"Mid-Career Median Salary\", ascending=False)\n",
    "high_median[[\"Undergraduate Major\", \"Mid-Career Median Salary\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Spread</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HASS</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STEM</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Undergraduate Major  Spread  Starting Median Salary  \\\n",
       "Group                                                           \n",
       "Business                   12      12                      12   \n",
       "HASS                       22      22                      22   \n",
       "STEM                       16      16                      16   \n",
       "\n",
       "          Mid-Career Median Salary  Mid-Career 10th Percentile Salary  \\\n",
       "Group                                                                   \n",
       "Business                        12                                 12   \n",
       "HASS                            22                                 22   \n",
       "STEM                            16                                 16   \n",
       "\n",
       "          Mid-Career 90th Percentile Salary  \n",
       "Group                                        \n",
       "Business                                 12  \n",
       "HASS                                     22  \n",
       "STEM                                     16  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by values in a certain column and count the matching rows\n",
    "clean_df.groupby(\"Group\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Spread</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <td>103958.333333</td>\n",
       "      <td>44633.333333</td>\n",
       "      <td>75083.333333</td>\n",
       "      <td>43566.666667</td>\n",
       "      <td>147525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HASS</th>\n",
       "      <td>95218.181818</td>\n",
       "      <td>37186.363636</td>\n",
       "      <td>62968.181818</td>\n",
       "      <td>34145.454545</td>\n",
       "      <td>129363.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STEM</th>\n",
       "      <td>101600.000000</td>\n",
       "      <td>53862.500000</td>\n",
       "      <td>90812.500000</td>\n",
       "      <td>56025.000000</td>\n",
       "      <td>157625.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Spread  Starting Median Salary  Mid-Career Median Salary  \\\n",
       "Group                                                                       \n",
       "Business  103958.333333            44633.333333              75083.333333   \n",
       "HASS       95218.181818            37186.363636              62968.181818   \n",
       "STEM      101600.000000            53862.500000              90812.500000   \n",
       "\n",
       "          Mid-Career 10th Percentile Salary  Mid-Career 90th Percentile Salary  \n",
       "Group                                                                           \n",
       "Business                       43566.666667                      147525.000000  \n",
       "HASS                           34145.454545                      129363.636364  \n",
       "STEM                           56025.000000                      157625.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean salary by group\n",
    "clean_df.groupby(\"Group\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Spread</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Mid-Career 10th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <td>103,958.33</td>\n",
       "      <td>44,633.33</td>\n",
       "      <td>75,083.33</td>\n",
       "      <td>43,566.67</td>\n",
       "      <td>147,525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HASS</th>\n",
       "      <td>95,218.18</td>\n",
       "      <td>37,186.36</td>\n",
       "      <td>62,968.18</td>\n",
       "      <td>34,145.45</td>\n",
       "      <td>129,363.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STEM</th>\n",
       "      <td>101,600.00</td>\n",
       "      <td>53,862.50</td>\n",
       "      <td>90,812.50</td>\n",
       "      <td>56,025.00</td>\n",
       "      <td>157,625.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Spread  Starting Median Salary  Mid-Career Median Salary  \\\n",
       "Group                                                                   \n",
       "Business 103,958.33               44,633.33                 75,083.33   \n",
       "HASS      95,218.18               37,186.36                 62,968.18   \n",
       "STEM     101,600.00               53,862.50                 90,812.50   \n",
       "\n",
       "          Mid-Career 10th Percentile Salary  Mid-Career 90th Percentile Salary  \n",
       "Group                                                                           \n",
       "Business                          43,566.67                         147,525.00  \n",
       "HASS                              34,145.45                         129,363.64  \n",
       "STEM                              56,025.00                         157,625.00  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change display format\n",
    "pd.options.display.float_format = \"{:,.2f}\".format \n",
    "clean_df.groupby(\"Group\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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