TeamProject1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring College Education and Salaries\n",
    "\n",
    "Hypothesis: There is a correlation between Salaries, academic major taken and type of college attended. \n",
    "\n",
    "1. Which degrees show the most growth in salary from starting to mid-career?\n",
    "2. What are the top 30 schools by mid career median salary?\n",
    "3. How do salaries differ by degree?\n",
    "4. Is there any correlation between starting salary and mid-career salary? If so, what is the    Pearson correlation coefficient? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy.stats import linregress\n",
    "%matplotlib inline\n",
    "from config import credentials_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Project Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data files and read into a pandas dataframe\n",
    "degrees_data = (\"Resources/degrees-that-pay-back .csv\")\n",
    "college_data = \"Resources/salaries-by-college-type.csv\"\n",
    "region_data = \"Resources/salaries-by-region.csv\"\n",
    "#with open(\"Resources/costOfAttendence.csv\", encoding=\"utf8\", errors='ignore') as cost_data:\n",
    "cost_data = \"Resources/costOfAttendence.csv\"\n",
    "# load colleges by state data\n",
    "\n",
    "# Read the mouse data and the study results into a pandas dataframe\n",
    "degrees_data = pd.read_csv(degrees_data)\n",
    "college_data = pd.read_csv(college_data)\n",
    "region_data = pd.read_csv(region_data)\n",
    "cost_data = pd.read_csv(cost_data)\n",
    "#degrees_data.head()\n",
    "#college_data.head\n",
    "#cost_data.head()\n",
    "#college_data.head()\n",
    "#region_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>School Name</th>\n",
       "      <th>Cost 2015</th>\n",
       "      <th>Cost 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>$47,110</td>\n",
       "      <td>$45,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aerosim Flight Academy</td>\n",
       "      <td>$82,824</td>\n",
       "      <td>$82,824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agnes Scott College</td>\n",
       "      <td>$47,323</td>\n",
       "      <td>$45,691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alaska Pacific University</td>\n",
       "      <td>$43,180</td>\n",
       "      <td>$43,180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albertus Magnus College</td>\n",
       "      <td>$47,302</td>\n",
       "      <td>$46,528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 School Name Cost 2015 Cost 2014\n",
       "0         Adelphi University  $47,110   $45,200 \n",
       "1     Aerosim Flight Academy  $82,824   $82,824 \n",
       "2        Agnes Scott College  $47,323   $45,691 \n",
       "3  Alaska Pacific University  $43,180   $43,180 \n",
       "4    Albertus Magnus College  $47,302   $46,528 "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete the extra column \n",
    "del cost_data['Unnamed: 3']\n",
    "cost_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</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 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Cost 2015</th>\n",
       "      <th>Cost 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>$40,600.00</td>\n",
       "      <td>$79,200.00</td>\n",
       "      <td>$44,200.00</td>\n",
       "      <td>$54,800.00</td>\n",
       "      <td>$114,000.00</td>\n",
       "      <td>$160,000.00</td>\n",
       "      <td>$47,110</td>\n",
       "      <td>$45,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American University, Washington D.C.</td>\n",
       "      <td>DC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>$45,300.00</td>\n",
       "      <td>$90,800.00</td>\n",
       "      <td>$45,200.00</td>\n",
       "      <td>$62,400.00</td>\n",
       "      <td>$134,000.00</td>\n",
       "      <td>$169,000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amherst College</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>$54,500.00</td>\n",
       "      <td>$107,000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$84,900.00</td>\n",
       "      <td>$162,000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$61,544</td>\n",
       "      <td>$59,060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Appalachian State University</td>\n",
       "      <td>NC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>$40,400.00</td>\n",
       "      <td>$69,100.00</td>\n",
       "      <td>$37,200.00</td>\n",
       "      <td>$50,400.00</td>\n",
       "      <td>$90,800.00</td>\n",
       "      <td>$115,000.00</td>\n",
       "      <td>$44,018</td>\n",
       "      <td>$43,457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Western</td>\n",
       "      <td>$47,400.00</td>\n",
       "      <td>$84,100.00</td>\n",
       "      <td>$44,600.00</td>\n",
       "      <td>$60,700.00</td>\n",
       "      <td>$114,000.00</td>\n",
       "      <td>$163,000.00</td>\n",
       "      <td>$42,363</td>\n",
       "      <td>$41,497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School Name State        Region  \\\n",
       "0                    Adelphi University    NY  Northeastern   \n",
       "1  American University, Washington D.C.    DC      Southern   \n",
       "2                       Amherst College    MA  Northeastern   \n",
       "3          Appalachian State University    NC      Southern   \n",
       "4             Arizona State University     AZ       Western   \n",
       "\n",
       "  Starting Median Salary Mid-Career Median Salary  \\\n",
       "0            $40,600.00               $79,200.00    \n",
       "1            $45,300.00               $90,800.00    \n",
       "2            $54,500.00              $107,000.00    \n",
       "3            $40,400.00               $69,100.00    \n",
       "4            $47,400.00               $84,100.00    \n",
       "\n",
       "  Mid-Career 10th Percentile Salary Mid-Career 25th Percentile Salary  \\\n",
       "0                       $44,200.00                        $54,800.00    \n",
       "1                       $45,200.00                        $62,400.00    \n",
       "2                               NaN                       $84,900.00    \n",
       "3                       $37,200.00                        $50,400.00    \n",
       "4                       $44,600.00                        $60,700.00    \n",
       "\n",
       "  Mid-Career 75th Percentile Salary Mid-Career 90th Percentile Salary  \\\n",
       "0                      $114,000.00                       $160,000.00    \n",
       "1                      $134,000.00                       $169,000.00    \n",
       "2                      $162,000.00                                NaN   \n",
       "3                       $90,800.00                       $115,000.00    \n",
       "4                      $114,000.00                       $163,000.00    \n",
       "\n",
       "  Cost 2015 Cost 2014  \n",
       "0  $47,110   $45,200   \n",
       "1       NaN       NaN  \n",
       "2  $61,544   $59,060   \n",
       "3  $44,018   $43,457   \n",
       "4  $42,363   $41,497   "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Merge college data and region data dataframes\n",
    "merged_data = pd.merge(region_data,cost_data, how = 'left', left_on = 'School Name', right_on = 'School Name')\n",
    "#merged_data = pd.merge(region_data,cost_data, how='left', on=('School Name'))\n",
    "#                 'Starting Median Salary',\n",
    "#                 'Mid-Career Median Salary',\n",
    "#                 'Mid-Career 10th Percentile Salary',\n",
    "#                 'Mid-Career 25th Percentile Salary',\n",
    "#                 'Mid-Career 75th Percentile Salary',\n",
    "#                 'Mid-Career 90th Percentile Salary'])\n",
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(323, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the shape of the data, how many rows and columns\n",
    "merged_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 323 entries, 0 to 322\n",
      "Data columns (total 11 columns):\n",
      " #   Column                             Non-Null Count  Dtype \n",
      "---  ------                             --------------  ----- \n",
      " 0   School Name                        323 non-null    object\n",
      " 1   State                              323 non-null    object\n",
      " 2   Region                             323 non-null    object\n",
      " 3   Starting Median Salary             323 non-null    object\n",
      " 4   Mid-Career Median Salary           323 non-null    object\n",
      " 5   Mid-Career 10th Percentile Salary  276 non-null    object\n",
      " 6   Mid-Career 25th Percentile Salary  323 non-null    object\n",
      " 7   Mid-Career 75th Percentile Salary  323 non-null    object\n",
      " 8   Mid-Career 90th Percentile Salary  276 non-null    object\n",
      " 9   Cost 2015                          148 non-null    object\n",
      " 10  Cost 2014                          148 non-null    object\n",
      "dtypes: object(11)\n",
      "memory usage: 30.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# review data information to see the type of data, columns with missing values\n",
    "merged_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get an overview of the table statistics to see what needs to be cleaned\n",
    "#merged_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine which columns have missing values, method 1\n",
    "#merged_data.columns[merged_data.isnull().any()].tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine which columns have missing values, method 2\n",
    "#merged_data.loc[:, merged_data.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of missing values\n",
    "#combined_merged_data.isnull().sum()\n",
    "#combined_merged_data.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</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 25th Percentile Salary</th>\n",
       "      <th>Mid-Career 75th Percentile Salary</th>\n",
       "      <th>Mid-Career 90th Percentile Salary</th>\n",
       "      <th>Cost 2015</th>\n",
       "      <th>Cost 2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>40600.0</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>44200.0</td>\n",
       "      <td>54800.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>47110.0</td>\n",
       "      <td>45200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American University, Washington D.C.</td>\n",
       "      <td>DC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>45300.0</td>\n",
       "      <td>90800.0</td>\n",
       "      <td>45200.0</td>\n",
       "      <td>62400.0</td>\n",
       "      <td>134000.0</td>\n",
       "      <td>169000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amherst College</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>54500.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84900.0</td>\n",
       "      <td>162000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61544.0</td>\n",
       "      <td>59060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Appalachian State University</td>\n",
       "      <td>NC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>40400.0</td>\n",
       "      <td>69100.0</td>\n",
       "      <td>37200.0</td>\n",
       "      <td>50400.0</td>\n",
       "      <td>90800.0</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>44018.0</td>\n",
       "      <td>43457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Western</td>\n",
       "      <td>47400.0</td>\n",
       "      <td>84100.0</td>\n",
       "      <td>44600.0</td>\n",
       "      <td>60700.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>42363.0</td>\n",
       "      <td>41497.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School Name State        Region  \\\n",
       "0                    Adelphi University    NY  Northeastern   \n",
       "1  American University, Washington D.C.    DC      Southern   \n",
       "2                       Amherst College    MA  Northeastern   \n",
       "3          Appalachian State University    NC      Southern   \n",
       "4             Arizona State University     AZ       Western   \n",
       "\n",
       "   Starting Median Salary  Mid-Career Median Salary  \\\n",
       "0                 40600.0                   79200.0   \n",
       "1                 45300.0                   90800.0   \n",
       "2                 54500.0                  107000.0   \n",
       "3                 40400.0                   69100.0   \n",
       "4                 47400.0                   84100.0   \n",
       "\n",
       "   Mid-Career 10th Percentile Salary  Mid-Career 25th Percentile Salary  \\\n",
       "0                            44200.0                            54800.0   \n",
       "1                            45200.0                            62400.0   \n",
       "2                                NaN                            84900.0   \n",
       "3                            37200.0                            50400.0   \n",
       "4                            44600.0                            60700.0   \n",
       "\n",
       "   Mid-Career 75th Percentile Salary  Mid-Career 90th Percentile Salary  \\\n",
       "0                           114000.0                           160000.0   \n",
       "1                           134000.0                           169000.0   \n",
       "2                           162000.0                                NaN   \n",
       "3                            90800.0                           115000.0   \n",
       "4                           114000.0                           163000.0   \n",
       "\n",
       "   Cost 2015  Cost 2014  \n",
       "0    47110.0    45200.0  \n",
       "1        NaN        NaN  \n",
       "2    61544.0    59060.0  \n",
       "3    44018.0    43457.0  \n",
       "4    42363.0    41497.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##covert data columns to numerical values in order to perform analysis on the data\n",
    "# for multiple columns, put them in a list like so:\n",
    "#cols = ['col1', 'col2', ..., 'colN']\n",
    "\n",
    "cols = ['Starting Median Salary', \n",
    "        'Mid-Career Median Salary',\n",
    "        'Mid-Career 10th Percentile Salary',\n",
    "        'Mid-Career 25th Percentile Salary',\n",
    "        'Mid-Career 75th Percentile Salary',\n",
    "        'Mid-Career 90th Percentile Salary',\n",
    "        'Cost 2015', 'Cost 2014']\n",
    "merged_data[cols] = merged_data[cols].astype(str)\n",
    "\n",
    "# pass them to df.replace(), specifying each char and it's replacement:\n",
    "merged_data[cols] = merged_data[cols].replace({'\\$': '', ',': '', '%': ''}, regex=True)\n",
    "merged_data[cols] = merged_data[cols].astype(float)\n",
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Cost 2014</th>\n",
       "      <th>Cost 2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelphi University</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>40600.0</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>45200.0</td>\n",
       "      <td>47110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American University, Washington D.C.</td>\n",
       "      <td>DC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>45300.0</td>\n",
       "      <td>90800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amherst College</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>54500.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>59060.0</td>\n",
       "      <td>61544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Appalachian State University</td>\n",
       "      <td>NC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>40400.0</td>\n",
       "      <td>69100.0</td>\n",
       "      <td>43457.0</td>\n",
       "      <td>44018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona State University</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Western</td>\n",
       "      <td>47400.0</td>\n",
       "      <td>84100.0</td>\n",
       "      <td>41497.0</td>\n",
       "      <td>42363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arkansas State University</td>\n",
       "      <td>AR</td>\n",
       "      <td>Southern</td>\n",
       "      <td>38700.0</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>28068.0</td>\n",
       "      <td>29509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Auburn University</td>\n",
       "      <td>AL</td>\n",
       "      <td>Southern</td>\n",
       "      <td>45400.0</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>40458.0</td>\n",
       "      <td>40458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Austin Peay State University</td>\n",
       "      <td>TN</td>\n",
       "      <td>Southern</td>\n",
       "      <td>37700.0</td>\n",
       "      <td>59200.0</td>\n",
       "      <td>46097.0</td>\n",
       "      <td>47625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ball State University</td>\n",
       "      <td>IN</td>\n",
       "      <td>Midwestern</td>\n",
       "      <td>39100.0</td>\n",
       "      <td>64500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bates College</td>\n",
       "      <td>ME</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>59285.0</td>\n",
       "      <td>61000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            School Name State        Region  \\\n",
       "0                    Adelphi University    NY  Northeastern   \n",
       "1  American University, Washington D.C.    DC      Southern   \n",
       "2                       Amherst College    MA  Northeastern   \n",
       "3          Appalachian State University    NC      Southern   \n",
       "4             Arizona State University     AZ       Western   \n",
       "5             Arkansas State University    AR      Southern   \n",
       "6                     Auburn University    AL      Southern   \n",
       "7          Austin Peay State University    TN      Southern   \n",
       "8                Ball State University     IN    Midwestern   \n",
       "9                         Bates College    ME  Northeastern   \n",
       "\n",
       "   Starting Median Salary  Mid-Career Median Salary  Cost 2014  Cost 2015  \n",
       "0                 40600.0                   79200.0    45200.0    47110.0  \n",
       "1                 45300.0                   90800.0        NaN        NaN  \n",
       "2                 54500.0                  107000.0    59060.0    61544.0  \n",
       "3                 40400.0                   69100.0    43457.0    44018.0  \n",
       "4                 47400.0                   84100.0    41497.0    42363.0  \n",
       "5                 38700.0                   63300.0    28068.0    29509.0  \n",
       "6                 45400.0                   84700.0    40458.0    40458.0  \n",
       "7                 37700.0                   59200.0    46097.0    47625.0  \n",
       "8                 39100.0                   64500.0        NaN        NaN  \n",
       "9                 47300.0                   96500.0    59285.0    61000.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a dataframe for the data i want to analyze\n",
    "regionSalary_df = merged_data.loc[:,['School Name','State','Region','Starting Median Salary',\n",
    "                'Mid-Career Median Salary','Cost 2014','Cost 2015']] \n",
    "regionSalary_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# cols = ['Starting Median Salary', \n",
    "#         'Mid-Career Median Salary',\n",
    "#         'Mid-Career 10th Percentile Salary',\n",
    "#         'Mid-Career 25th Percentile Salary',\n",
    "#         'Mid-Career 75th Percentile Salary',\n",
    "#         'Mid-Career 90th Percentile Salary',\n",
    "#         ]\n",
    "# college_data[cols] = college_data[cols].replace({'\\$': '', ',': '', '%': ''}, regex=True)\n",
    "# college_data[cols] = college_data[cols].astype(float)\n",
    "\n",
    "# college_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # What are the resulting distributions for starting and mid-career salaries by college?\n",
    "\n",
    "# figure1 = sns.distplot(college_data[('Starting Median Salary')], bins=20, color='green', \\\n",
    "#              label='Starting Median Salary')\n",
    "\n",
    "# sns.distplot(college_data[('Mid-Career Median Salary')], bins=20, color='red', \\\n",
    "#              label='Mid-Career Median Salary')\n",
    "# plt.legend()\n",
    "# sns.despine()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#regionSalary_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis: Starting Median Salary for college graduates is around  $46,000, \n",
    "#  by mid career avarage is about $84,000\n",
    "#  average cost of college in 2015 was $55,000. \n",
    "# Assumption all other factors being equal- it is true these numbers will vary depending on college major and other factors.. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Cost 2014</th>\n",
       "      <th>Cost 2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>75500.0</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>56382.0</td>\n",
       "      <td>58755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>72200.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>71800.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>63860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>70400.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>58846.0</td>\n",
       "      <td>60749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Princeton University</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>66500.0</td>\n",
       "      <td>131000.0</td>\n",
       "      <td>54780.0</td>\n",
       "      <td>55832.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                School Name State        Region  \\\n",
       "23       California Institute of Technology    CA    California   \n",
       "108  Massachusetts Institute of Technology     MA  Northeastern   \n",
       "83                      Harvey Mudd College    CA    California   \n",
       "178                     Stanford University    CA    California   \n",
       "147                    Princeton University    NJ  Northeastern   \n",
       "\n",
       "     Starting Median Salary  Mid-Career Median Salary  Cost 2014  Cost 2015  \n",
       "23                  75500.0                  123000.0    56382.0    58755.0  \n",
       "108                 72200.0                  126000.0        NaN        NaN  \n",
       "83                  71800.0                  122000.0    60613.0    63860.0  \n",
       "178                 70400.0                  129000.0    58846.0    60749.0  \n",
       "147                 66500.0                  131000.0    54780.0    55832.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort values by starting median salary, so that our graph will be in order\n",
    "regionSalary_df.sort_values(by = 'Starting Median Salary', ascending = False, inplace=True)\n",
    "regionSalary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Cost 2014</th>\n",
       "      <th>Cost 2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>75500.0</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>56382.0</td>\n",
       "      <td>58755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>72200.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>71800.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>63860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>70400.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>58846.0</td>\n",
       "      <td>60749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Princeton University</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>66500.0</td>\n",
       "      <td>131000.0</td>\n",
       "      <td>54780.0</td>\n",
       "      <td>55832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rice University</td>\n",
       "      <td>TN</td>\n",
       "      <td>Southern</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>52242.0</td>\n",
       "      <td>54291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Harvard University</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>63400.0</td>\n",
       "      <td>124000.0</td>\n",
       "      <td>57050.0</td>\n",
       "      <td>59950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Polytechnic University of New York -Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>62400.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cooper Union</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>62200.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>PA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>61800.0</td>\n",
       "      <td>111000.0</td>\n",
       "      <td>59710.0</td>\n",
       "      <td>62032.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    School Name State        Region  \\\n",
       "0            California Institute of Technology    CA    California   \n",
       "1        Massachusetts Institute of Technology     MA  Northeastern   \n",
       "2                           Harvey Mudd College    CA    California   \n",
       "3                           Stanford University    CA    California   \n",
       "4                          Princeton University    NJ  Northeastern   \n",
       "5                               Rice University    TN      Southern   \n",
       "6                            Harvard University    MA  Northeastern   \n",
       "7  Polytechnic University of New York -Brooklyn    NY  Northeastern   \n",
       "8                                  Cooper Union    NY  Northeastern   \n",
       "9                    Carnegie Mellon University    PA  Northeastern   \n",
       "\n",
       "   Starting Median Salary  Mid-Career Median Salary  Cost 2014  Cost 2015  \n",
       "0                 75500.0                  123000.0    56382.0    58755.0  \n",
       "1                 72200.0                  126000.0        NaN        NaN  \n",
       "2                 71800.0                  122000.0    60613.0    63860.0  \n",
       "3                 70400.0                  129000.0    58846.0    60749.0  \n",
       "4                 66500.0                  131000.0    54780.0    55832.0  \n",
       "5                 64000.0                  110000.0    52242.0    54291.0  \n",
       "6                 63400.0                  124000.0    57050.0    59950.0  \n",
       "7                 62400.0                  114000.0        NaN        NaN  \n",
       "8                 62200.0                  114000.0        NaN        NaN  \n",
       "9                 61800.0                  111000.0    59710.0    62032.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new index\n",
    "regionSalary_df.reset_index(drop=True, inplace=True)\n",
    "regionSalary_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Cost 2014</th>\n",
       "      <th>Cost 2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>75500.0</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>56382.0</td>\n",
       "      <td>58755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>72200.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>71800.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>63860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>70400.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>58846.0</td>\n",
       "      <td>60749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Princeton University</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>66500.0</td>\n",
       "      <td>131000.0</td>\n",
       "      <td>54780.0</td>\n",
       "      <td>55832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rice University</td>\n",
       "      <td>TN</td>\n",
       "      <td>Southern</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>52242.0</td>\n",
       "      <td>54291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Harvard University</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>63400.0</td>\n",
       "      <td>124000.0</td>\n",
       "      <td>57050.0</td>\n",
       "      <td>59950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Polytechnic University of New York -Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>62400.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cooper Union</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>62200.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Carnegie Mellon University</td>\n",
       "      <td>PA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>61800.0</td>\n",
       "      <td>111000.0</td>\n",
       "      <td>59710.0</td>\n",
       "      <td>62032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rensselaer Polytechnic Institute</td>\n",
       "      <td>CT</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>61100.0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Worcester Polytechnic Institute</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>University of Pennsylvania</td>\n",
       "      <td>PA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>60900.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>59600.0</td>\n",
       "      <td>61800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Stevens Institute of Technology</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>60600.0</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>59260.0</td>\n",
       "      <td>60554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cornell University</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>60300.0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>59591.0</td>\n",
       "      <td>61618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>University of California-Berkeley</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>59900.0</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>55766.0</td>\n",
       "      <td>56197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Columbia University</td>\n",
       "      <td>NY</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lehigh University</td>\n",
       "      <td>PA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>59200.0</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>55515.0</td>\n",
       "      <td>57145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Yale University</td>\n",
       "      <td>CT</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>59100.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>59320.0</td>\n",
       "      <td>61620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Duke University</td>\n",
       "      <td>NC</td>\n",
       "      <td>Southern</td>\n",
       "      <td>58900.0</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>59528.0</td>\n",
       "      <td>61748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Georgia Institute of Technology</td>\n",
       "      <td>GA</td>\n",
       "      <td>Southern</td>\n",
       "      <td>58300.0</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Colorado School of Mines</td>\n",
       "      <td>CO</td>\n",
       "      <td>Western</td>\n",
       "      <td>58100.0</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>44531.0</td>\n",
       "      <td>46533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dartmouth College</td>\n",
       "      <td>NH</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>134000.0</td>\n",
       "      <td>61398.0</td>\n",
       "      <td>63279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cal Poly San Luis Obispo</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>57200.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>47236.0</td>\n",
       "      <td>47236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>University of Missouri - Rolla</td>\n",
       "      <td>MO</td>\n",
       "      <td>Midwestern</td>\n",
       "      <td>57100.0</td>\n",
       "      <td>95800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>University of Notre Dame</td>\n",
       "      <td>IN</td>\n",
       "      <td>Midwestern</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>116000.0</td>\n",
       "      <td>57805.0</td>\n",
       "      <td>60117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brown University</td>\n",
       "      <td>RI</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>109000.0</td>\n",
       "      <td>58140.0</td>\n",
       "      <td>60460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Case Western Reserve University</td>\n",
       "      <td>OH</td>\n",
       "      <td>Midwestern</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>56026.0</td>\n",
       "      <td>57798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Illinois Institute of Technology</td>\n",
       "      <td>IL</td>\n",
       "      <td>Midwestern</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>97800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Villanova University</td>\n",
       "      <td>PA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>55800.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>56583.0</td>\n",
       "      <td>58736.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     School Name State        Region  \\\n",
       "0             California Institute of Technology    CA    California   \n",
       "1         Massachusetts Institute of Technology     MA  Northeastern   \n",
       "2                            Harvey Mudd College    CA    California   \n",
       "3                            Stanford University    CA    California   \n",
       "4                           Princeton University    NJ  Northeastern   \n",
       "5                                Rice University    TN      Southern   \n",
       "6                             Harvard University    MA  Northeastern   \n",
       "7   Polytechnic University of New York -Brooklyn    NY  Northeastern   \n",
       "8                                   Cooper Union    NY  Northeastern   \n",
       "9                     Carnegie Mellon University    PA  Northeastern   \n",
       "10             Rensselaer Polytechnic Institute     CT  Northeastern   \n",
       "11              Worcester Polytechnic Institute     MA  Northeastern   \n",
       "12                    University of Pennsylvania    PA  Northeastern   \n",
       "13               Stevens Institute of Technology    NJ  Northeastern   \n",
       "14                            Cornell University    NY  Northeastern   \n",
       "15             University of California-Berkeley    CA    California   \n",
       "16                           Columbia University    NY  Northeastern   \n",
       "17                             Lehigh University    PA  Northeastern   \n",
       "18                               Yale University    CT  Northeastern   \n",
       "19                               Duke University    NC      Southern   \n",
       "20               Georgia Institute of Technology    GA      Southern   \n",
       "21                      Colorado School of Mines    CO       Western   \n",
       "22                             Dartmouth College    NH  Northeastern   \n",
       "23                      Cal Poly San Luis Obispo    CA    California   \n",
       "24               University of Missouri - Rolla     MO    Midwestern   \n",
       "25                      University of Notre Dame    IN    Midwestern   \n",
       "26                              Brown University    RI  Northeastern   \n",
       "27               Case Western Reserve University    OH    Midwestern   \n",
       "28             Illinois Institute of Technology     IL    Midwestern   \n",
       "29                          Villanova University    PA  Northeastern   \n",
       "\n",
       "    Starting Median Salary  Mid-Career Median Salary  Cost 2014  Cost 2015  \n",
       "0                  75500.0                  123000.0    56382.0    58755.0  \n",
       "1                  72200.0                  126000.0        NaN        NaN  \n",
       "2                  71800.0                  122000.0    60613.0    63860.0  \n",
       "3                  70400.0                  129000.0    58846.0    60749.0  \n",
       "4                  66500.0                  131000.0    54780.0    55832.0  \n",
       "5                  64000.0                  110000.0    52242.0    54291.0  \n",
       "6                  63400.0                  124000.0    57050.0    59950.0  \n",
       "7                  62400.0                  114000.0        NaN        NaN  \n",
       "8                  62200.0                  114000.0        NaN        NaN  \n",
       "9                  61800.0                  111000.0    59710.0    62032.0  \n",
       "10                 61100.0                  110000.0        NaN        NaN  \n",
       "11                 61000.0                  114000.0        NaN        NaN  \n",
       "12                 60900.0                  120000.0    59600.0    61800.0  \n",
       "13                 60600.0                  105000.0    59260.0    60554.0  \n",
       "14                 60300.0                  110000.0    59591.0    61618.0  \n",
       "15                 59900.0                  112000.0    55766.0    56197.0  \n",
       "16                 59400.0                  107000.0        NaN        NaN  \n",
       "17                 59200.0                  105000.0    55515.0    57145.0  \n",
       "18                 59100.0                  126000.0    59320.0    61620.0  \n",
       "19                 58900.0                  106000.0    59528.0    61748.0  \n",
       "20                 58300.0                  106000.0        NaN        NaN  \n",
       "21                 58100.0                  106000.0    44531.0    46533.0  \n",
       "22                 58000.0                  134000.0    61398.0    63279.0  \n",
       "23                 57200.0                  101000.0    47236.0    47236.0  \n",
       "24                 57100.0                   95800.0        NaN        NaN  \n",
       "25                 56300.0                  116000.0    57805.0    60117.0  \n",
       "26                 56200.0                  109000.0    58140.0    60460.0  \n",
       "27                 56200.0                   96500.0    56026.0    57798.0  \n",
       "28                 56000.0                   97800.0        NaN        NaN  \n",
       "29                 55800.0                  101000.0    56583.0    58736.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median salaries of the Top 30 schools\n",
    "TOPschoolsSalary_df=regionSalary_df.loc[0:29,:] \n",
    "TOPschoolsSalary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create graph to show the median starting salary of the top 30 Colleges\n",
    "# flip the graph to accomodate the school names along the Y axis\n",
    "\n",
    "fig = plt.figure(figsize=(8,12))\n",
    "x = TOPschoolsSalary_df['Starting Median Salary'] #switch x and y labels\n",
    "y = TOPschoolsSalary_df.index\n",
    "# invert the y axis to show from lowest to highest.\n",
    "y=[len(y)-i for i in TOPschoolsSalary_df.index]\n",
    "labels = TOPschoolsSalary_df['School Name']\n",
    "\n",
    "\n",
    "plt.scatter(x, y, color='g', label = 'Starting Median Salary') \n",
    "plt.yticks(y, labels)\n",
    "\n",
    "plt.xlabel('US $')\n",
    "plt.ylabel('') #hide label\n",
    "plt.title('Starting Median Salary by College')\n",
    "plt.legend(loc=2)\n",
    "plt.grid()\n",
    "plt.savefig('Figures/Starting Median vs salary.png')\n",
    "            \n",
    "plt.show()           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the Mid Career salaries\n",
    "\n",
    "fig = plt.figure(figsize=(10,14))\n",
    "x = TOPschoolsSalary_df['Starting Median Salary'] #switch x and y labels\n",
    "y=[len(y)-i for i in TOPschoolsSalary_df.index]\n",
    "#y=len(y)-TOPschoolsSalary_df.index\n",
    "\n",
    "labels = TOPschoolsSalary_df['School Name']\n",
    "\n",
    "plt.scatter(x, y, color='g', label = 'Starting Median Salary') \n",
    "plt.yticks(y, labels)\n",
    "\n",
    "x2 = TOPschoolsSalary_df['Mid-Career Median Salary']\n",
    "plt.scatter(x2, y, color='b', label = 'Median Mid Career Salary')\n",
    "\n",
    "plt.xlabel('US $')\n",
    "plt.ylabel('')\n",
    "plt.title('Salary vs College Attended')\n",
    "plt.legend(loc=4) #move the legend\n",
    "\n",
    "# Turn on the grid\n",
    "plt.grid()\n",
    "\n",
    "# x3 = TOPschoolsSalary_df['Cost 2015']\n",
    "# plt.scatter(x3, y, color='r', label = 'Cost 2015')\n",
    "\n",
    "# plt.xlabel('US $')\n",
    "# plt.ylabel('')\n",
    "# plt.title('Salary Information by College')\n",
    "# plt.legend(loc=2) #move the legend\n",
    "\n",
    "plt.savefig('Figures/Starting&Mid carreer vs salary.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up to use Plotly API within Jupyter notebook\n",
    "# #Step1.  import required libraries:\n",
    "# import numpy as np\n",
    "# import pandas as pd\n",
    "\n",
    "# # #Std plotly tools\n",
    "# import chart_studio.plotly\n",
    "# #import plotly.plotly as py -this module created -ImportError: The plotly.plotly module is deprecated, advised\n",
    "# # to use chart_studio.plotly instead\n",
    "# import plotly as py\n",
    "# import plotly.graph_objs as go\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step2. Set credentials using the set credential function\n",
    "#plotly.tools.set_credentials_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>School Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Starting Median Salary</th>\n",
       "      <th>Mid-Career Median Salary</th>\n",
       "      <th>Cost 2014</th>\n",
       "      <th>Cost 2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>75500.0</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>56382.0</td>\n",
       "      <td>58755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>MA</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>72200.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harvey Mudd College</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>71800.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>60613.0</td>\n",
       "      <td>63860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>70400.0</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>58846.0</td>\n",
       "      <td>60749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Princeton University</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>66500.0</td>\n",
       "      <td>131000.0</td>\n",
       "      <td>54780.0</td>\n",
       "      <td>55832.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              School Name State        Region  \\\n",
       "0      California Institute of Technology    CA    California   \n",
       "1  Massachusetts Institute of Technology     MA  Northeastern   \n",
       "2                     Harvey Mudd College    CA    California   \n",
       "3                     Stanford University    CA    California   \n",
       "4                    Princeton University    NJ  Northeastern   \n",
       "\n",
       "   Starting Median Salary  Mid-Career Median Salary  Cost 2014  Cost 2015  \n",
       "0                 75500.0                  123000.0    56382.0    58755.0  \n",
       "1                 72200.0                  126000.0        NaN        NaN  \n",
       "2                 71800.0                  122000.0    60613.0    63860.0  \n",
       "3                 70400.0                  129000.0    58846.0    60749.0  \n",
       "4                 66500.0                  131000.0    54780.0    55832.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regionSalary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from  plotly.offline import plot\n",
    "from config import credentials_file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"525px\"\n",
       "            src=\"https://plotly.com/~smwanzi/1.embed\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x242a209c708>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import pandas as pd\n",
    "import config \n",
    "\n",
    "username = 'username'\n",
    "api_key = 'api_key' \n",
    "\n",
    "for col in regionSalary_df.columns:\n",
    "    regionSalary_df[col] = regionSalary_df[col].astype(str)\n",
    "\n",
    "scl = [\n",
    "    [0.0, 'rgb(242,240,247)'],\n",
    "    [0.2, 'rgb(218,218,235)'],\n",
    "    [0.4, 'rgb(188,189,220)'],\n",
    "    [0.6, 'rgb(158,154,200)'],\n",
    "    [0.8, 'rgb(117,107,177)'],\n",
    "    [1.0, 'rgb(84,39,143)']\n",
    "]\n",
    "\n",
    "regionSalary_df['text'] ='States'+ regionSalary_df['State'] + '<br>' + \\\n",
    "    ' Starting Median Salary ' + regionSalary_df['Starting Median Salary'] +'<br>' + \\\n",
    "    ' Mid-Career Median Salary ' + regionSalary_df['Mid-Career Median Salary'] #+ '<br>' + \\\n",
    "\n",
    "data = [go.Choropleth(\n",
    "    colorscale = scl,\n",
    "    autocolorscale = False,\n",
    "    locations = regionSalary_df['State'],\n",
    "    z = regionSalary_df['Cost 2015'].astype(float),\n",
    "    locationmode = 'USA-states',\n",
    "    text = regionSalary_df['text'],\n",
    "    marker = go.choropleth.Marker(\n",
    "        line = go.choropleth.marker.Line(\n",
    "            color = 'rgb(255,255,255)',\n",
    "            width = 2\n",
    "        )),\n",
    "    colorbar = go.choropleth.ColorBar(\n",
    "        title = \"USD\")\n",
    ")]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = go.layout.Title(\n",
    "        text = '2015 Cost of Attending College by States vs Salary<br>(Hover for breakdown)'\n",
    "    ),\n",
    "    geo = go.layout.Geo(\n",
    "        scope = 'usa',\n",
    "        projection = go.layout.geo.Projection(type = 'albers usa'),\n",
    "        showlakes = True,\n",
    "        lakecolor = 'rgb(255, 255, 255)'),\n",
    ")\n",
    "# plotly.tools.set_credentials_file(username=’YourAccountName’, api_key=’YourAPIKey’)\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "py.sign_in(username='username', api_key='api_key')\n",
    "py.iplot(fig, filename = 'd3-cloropleth-map')\n",
    "plt.savefig('Figures/d3-cloropleth-map.png')\n",
    "py.iplot(fig, filename = 'd3-cloropleth-map')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}