WomenSTEMSolutions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2419</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2339</td>\n",
       "      <td>2057</td>\n",
       "      <td>282</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>756</td>\n",
       "      <td>679</td>\n",
       "      <td>77</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>856</td>\n",
       "      <td>725</td>\n",
       "      <td>131</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2417</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>1258</td>\n",
       "      <td>1123</td>\n",
       "      <td>135</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2418</td>\n",
       "      <td>NUCLEAR ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2573</td>\n",
       "      <td>2200</td>\n",
       "      <td>373</td>\n",
       "      <td>0.144967</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Major_code                                      Major Major_category  \\\n",
       "0     1        2419                      PETROLEUM ENGINEERING    Engineering   \n",
       "1     2        2416             MINING AND MINERAL ENGINEERING    Engineering   \n",
       "2     3        2415                  METALLURGICAL ENGINEERING    Engineering   \n",
       "3     4        2417  NAVAL ARCHITECTURE AND MARINE ENGINEERING    Engineering   \n",
       "4     5        2418                        NUCLEAR ENGINEERING    Engineering   \n",
       "\n",
       "   Total   Men  Women  ShareWomen  Median  \n",
       "0   2339  2057    282    0.120564  110000  \n",
       "1    756   679     77    0.101852   75000  \n",
       "2    856   725    131    0.153037   73000  \n",
       "3   1258  1123    135    0.107313   70000  \n",
       "4   2573  2200    373    0.144967   65000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_WomenSTEM = pd.read_csv(\"women-stem.csv\") #load in csv file\n",
    "\n",
    "df_WomenSTEM.head() #displays first five lines; can specify number of lines inside parenthese (ex: df.head(10) shows first 10 lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76 entries, 0 to 75\n",
      "Data columns (total 9 columns):\n",
      "Rank              76 non-null int64\n",
      "Major_code        76 non-null int64\n",
      "Major             76 non-null object\n",
      "Major_category    76 non-null object\n",
      "Total             76 non-null int64\n",
      "Men               76 non-null int64\n",
      "Women             76 non-null int64\n",
      "ShareWomen        76 non-null float64\n",
      "Median            76 non-null int64\n",
      "dtypes: float64(1), int64(6), object(2)\n",
      "memory usage: 5.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#get general info about data: names of columns, number of entries per column, data type (int, string, etc)\n",
    "df_WomenSTEM.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engineering                29\n",
       "Biology & Life Science     14\n",
       "Health                     12\n",
       "Computers & Mathematics    11\n",
       "Physical Sciences          10\n",
       "Name: Major_category, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counts of unique values in \"Major_category\" column, including NaN values\n",
    "df_WomenSTEM[\"Major_category\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank              0\n",
       "Major_code        0\n",
       "Major             0\n",
       "Major_category    0\n",
       "Total             0\n",
       "Men               0\n",
       "Women             0\n",
       "ShareWomen        0\n",
       "Median            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sum of null values per column; in this case the data set is clean, we don't need to do anything\n",
    "pd.isnull(df_WomenSTEM).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find dimensions of dataframe\n",
    "df_WomenSTEM.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                int64\n",
       "Major_code          int64\n",
       "Major              object\n",
       "Major_category     object\n",
       "Total               int64\n",
       "Men                 int64\n",
       "Women               int64\n",
       "ShareWomen        float64\n",
       "Median              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find value type for each column\n",
    "df_WomenSTEM.dtypes "
   ]
  },
  {
   "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>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Major</th>\n",
       "      <th>Major_category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2419</td>\n",
       "      <td>PETROLEUM ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2339</td>\n",
       "      <td>2057</td>\n",
       "      <td>282</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2416</td>\n",
       "      <td>MINING AND MINERAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>756</td>\n",
       "      <td>679</td>\n",
       "      <td>77</td>\n",
       "      <td>0.101852</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2415</td>\n",
       "      <td>METALLURGICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>856</td>\n",
       "      <td>725</td>\n",
       "      <td>131</td>\n",
       "      <td>0.153037</td>\n",
       "      <td>73000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2417</td>\n",
       "      <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>1258</td>\n",
       "      <td>1123</td>\n",
       "      <td>135</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2418</td>\n",
       "      <td>NUCLEAR ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2573</td>\n",
       "      <td>2200</td>\n",
       "      <td>373</td>\n",
       "      <td>0.144967</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2405</td>\n",
       "      <td>CHEMICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>32260</td>\n",
       "      <td>21239</td>\n",
       "      <td>11021</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>5001</td>\n",
       "      <td>ASTRONOMY AND ASTROPHYSICS</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>1792</td>\n",
       "      <td>832</td>\n",
       "      <td>960</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2414</td>\n",
       "      <td>MECHANICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>91227</td>\n",
       "      <td>80320</td>\n",
       "      <td>10907</td>\n",
       "      <td>0.119559</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2401</td>\n",
       "      <td>AEROSPACE ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>15058</td>\n",
       "      <td>12953</td>\n",
       "      <td>2105</td>\n",
       "      <td>0.139793</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2408</td>\n",
       "      <td>ELECTRICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>81527</td>\n",
       "      <td>65511</td>\n",
       "      <td>16016</td>\n",
       "      <td>0.196450</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2407</td>\n",
       "      <td>COMPUTER ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>41542</td>\n",
       "      <td>33258</td>\n",
       "      <td>8284</td>\n",
       "      <td>0.199413</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>5008</td>\n",
       "      <td>MATERIALS SCIENCE</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>4279</td>\n",
       "      <td>2949</td>\n",
       "      <td>1330</td>\n",
       "      <td>0.310820</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2404</td>\n",
       "      <td>BIOMEDICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>14955</td>\n",
       "      <td>8407</td>\n",
       "      <td>6548</td>\n",
       "      <td>0.437847</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2409</td>\n",
       "      <td>ENGINEERING MECHANICS PHYSICS AND SCIENCE</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>4321</td>\n",
       "      <td>3526</td>\n",
       "      <td>795</td>\n",
       "      <td>0.183985</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2402</td>\n",
       "      <td>BIOLOGICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>8925</td>\n",
       "      <td>6062</td>\n",
       "      <td>2863</td>\n",
       "      <td>0.320784</td>\n",
       "      <td>57100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2412</td>\n",
       "      <td>INDUSTRIAL AND MANUFACTURING ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>18968</td>\n",
       "      <td>12453</td>\n",
       "      <td>6515</td>\n",
       "      <td>0.343473</td>\n",
       "      <td>57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2400</td>\n",
       "      <td>GENERAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>61152</td>\n",
       "      <td>45683</td>\n",
       "      <td>15469</td>\n",
       "      <td>0.252960</td>\n",
       "      <td>56000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2403</td>\n",
       "      <td>ARCHITECTURAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2825</td>\n",
       "      <td>1835</td>\n",
       "      <td>990</td>\n",
       "      <td>0.350442</td>\n",
       "      <td>54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2102</td>\n",
       "      <td>COMPUTER SCIENCE</td>\n",
       "      <td>Computers &amp; Mathematics</td>\n",
       "      <td>128319</td>\n",
       "      <td>99743</td>\n",
       "      <td>28576</td>\n",
       "      <td>0.222695</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>2502</td>\n",
       "      <td>ELECTRICAL ENGINEERING TECHNOLOGY</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>11565</td>\n",
       "      <td>8181</td>\n",
       "      <td>3384</td>\n",
       "      <td>0.292607</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2413</td>\n",
       "      <td>MATERIALS ENGINEERING AND MATERIALS SCIENCE</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>2993</td>\n",
       "      <td>2020</td>\n",
       "      <td>973</td>\n",
       "      <td>0.325092</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2499</td>\n",
       "      <td>MISCELLANEOUS ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>9133</td>\n",
       "      <td>7398</td>\n",
       "      <td>1735</td>\n",
       "      <td>0.189970</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>2406</td>\n",
       "      <td>CIVIL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>53153</td>\n",
       "      <td>41081</td>\n",
       "      <td>12072</td>\n",
       "      <td>0.227118</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>2500</td>\n",
       "      <td>ENGINEERING TECHNOLOGIES</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>3600</td>\n",
       "      <td>2695</td>\n",
       "      <td>905</td>\n",
       "      <td>0.251389</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>2411</td>\n",
       "      <td>GEOLOGICAL AND GEOPHYSICAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>720</td>\n",
       "      <td>488</td>\n",
       "      <td>232</td>\n",
       "      <td>0.322222</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>2410</td>\n",
       "      <td>ENVIRONMENTAL ENGINEERING</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>4047</td>\n",
       "      <td>2662</td>\n",
       "      <td>1385</td>\n",
       "      <td>0.342229</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>6107</td>\n",
       "      <td>NURSING</td>\n",
       "      <td>Health</td>\n",
       "      <td>209394</td>\n",
       "      <td>21773</td>\n",
       "      <td>187621</td>\n",
       "      <td>0.896019</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>2503</td>\n",
       "      <td>INDUSTRIAL PRODUCTION TECHNOLOGIES</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>4631</td>\n",
       "      <td>3477</td>\n",
       "      <td>1154</td>\n",
       "      <td>0.249190</td>\n",
       "      <td>46000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>5102</td>\n",
       "      <td>NUCLEAR, INDUSTRIAL RADIOLOGY, AND BIOLOGICAL ...</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>2116</td>\n",
       "      <td>528</td>\n",
       "      <td>1588</td>\n",
       "      <td>0.750473</td>\n",
       "      <td>46000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2105</td>\n",
       "      <td>INFORMATION SCIENCES</td>\n",
       "      <td>Computers &amp; Mathematics</td>\n",
       "      <td>11913</td>\n",
       "      <td>9005</td>\n",
       "      <td>2908</td>\n",
       "      <td>0.244103</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>1401</td>\n",
       "      <td>ARCHITECTURE</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>46420</td>\n",
       "      <td>25463</td>\n",
       "      <td>20957</td>\n",
       "      <td>0.451465</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>3605</td>\n",
       "      <td>GENETICS</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>3635</td>\n",
       "      <td>1761</td>\n",
       "      <td>1874</td>\n",
       "      <td>0.515543</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>3603</td>\n",
       "      <td>MOLECULAR BIOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>18300</td>\n",
       "      <td>7426</td>\n",
       "      <td>10874</td>\n",
       "      <td>0.594208</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>6108</td>\n",
       "      <td>PHARMACY PHARMACEUTICAL SCIENCES AND ADMINISTR...</td>\n",
       "      <td>Health</td>\n",
       "      <td>23551</td>\n",
       "      <td>8697</td>\n",
       "      <td>14854</td>\n",
       "      <td>0.630716</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>5003</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>66530</td>\n",
       "      <td>32923</td>\n",
       "      <td>33607</td>\n",
       "      <td>0.505141</td>\n",
       "      <td>39000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>3606</td>\n",
       "      <td>MICROBIOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>15232</td>\n",
       "      <td>6383</td>\n",
       "      <td>8849</td>\n",
       "      <td>0.580948</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>2106</td>\n",
       "      <td>COMPUTER ADMINISTRATION MANAGEMENT AND SECURITY</td>\n",
       "      <td>Computers &amp; Mathematics</td>\n",
       "      <td>8066</td>\n",
       "      <td>6607</td>\n",
       "      <td>1459</td>\n",
       "      <td>0.180883</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>3601</td>\n",
       "      <td>BIOCHEMICAL SCIENCES</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>39107</td>\n",
       "      <td>18951</td>\n",
       "      <td>20156</td>\n",
       "      <td>0.515406</td>\n",
       "      <td>37400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>3602</td>\n",
       "      <td>BOTANY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>1329</td>\n",
       "      <td>626</td>\n",
       "      <td>703</td>\n",
       "      <td>0.528969</td>\n",
       "      <td>37000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>2107</td>\n",
       "      <td>COMPUTER NETWORKING AND TELECOMMUNICATIONS</td>\n",
       "      <td>Computers &amp; Mathematics</td>\n",
       "      <td>7613</td>\n",
       "      <td>5291</td>\n",
       "      <td>2322</td>\n",
       "      <td>0.305005</td>\n",
       "      <td>36400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>5004</td>\n",
       "      <td>GEOLOGY AND EARTH SCIENCE</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>10972</td>\n",
       "      <td>5813</td>\n",
       "      <td>5159</td>\n",
       "      <td>0.470197</td>\n",
       "      <td>36200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>5005</td>\n",
       "      <td>GEOSCIENCES</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>1978</td>\n",
       "      <td>809</td>\n",
       "      <td>1169</td>\n",
       "      <td>0.591001</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>6199</td>\n",
       "      <td>MISCELLANEOUS HEALTH MEDICAL PROFESSIONS</td>\n",
       "      <td>Health</td>\n",
       "      <td>13386</td>\n",
       "      <td>1589</td>\n",
       "      <td>11797</td>\n",
       "      <td>0.881294</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1301</td>\n",
       "      <td>ENVIRONMENTAL SCIENCE</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>25965</td>\n",
       "      <td>10787</td>\n",
       "      <td>15178</td>\n",
       "      <td>0.584556</td>\n",
       "      <td>35600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>5002</td>\n",
       "      <td>ATMOSPHERIC SCIENCES AND METEOROLOGY</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>4043</td>\n",
       "      <td>2744</td>\n",
       "      <td>1299</td>\n",
       "      <td>0.321296</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>2001</td>\n",
       "      <td>COMMUNICATION TECHNOLOGIES</td>\n",
       "      <td>Computers &amp; Mathematics</td>\n",
       "      <td>18035</td>\n",
       "      <td>11431</td>\n",
       "      <td>6604</td>\n",
       "      <td>0.366177</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>5098</td>\n",
       "      <td>MULTI-DISCIPLINARY OR GENERAL SCIENCE</td>\n",
       "      <td>Physical Sciences</td>\n",
       "      <td>62052</td>\n",
       "      <td>27015</td>\n",
       "      <td>35037</td>\n",
       "      <td>0.564639</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>3608</td>\n",
       "      <td>PHYSIOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>22060</td>\n",
       "      <td>8422</td>\n",
       "      <td>13638</td>\n",
       "      <td>0.618223</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>3611</td>\n",
       "      <td>NEUROSCIENCE</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>13663</td>\n",
       "      <td>4944</td>\n",
       "      <td>8719</td>\n",
       "      <td>0.638147</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>6103</td>\n",
       "      <td>HEALTH AND MEDICAL ADMINISTRATIVE SERVICES</td>\n",
       "      <td>Health</td>\n",
       "      <td>18109</td>\n",
       "      <td>4266</td>\n",
       "      <td>13843</td>\n",
       "      <td>0.764427</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>4002</td>\n",
       "      <td>NUTRITION SCIENCES</td>\n",
       "      <td>Health</td>\n",
       "      <td>18909</td>\n",
       "      <td>2563</td>\n",
       "      <td>16346</td>\n",
       "      <td>0.864456</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>6110</td>\n",
       "      <td>COMMUNITY AND PUBLIC HEALTH</td>\n",
       "      <td>Health</td>\n",
       "      <td>19735</td>\n",
       "      <td>4103</td>\n",
       "      <td>15632</td>\n",
       "      <td>0.792095</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>3699</td>\n",
       "      <td>MISCELLANEOUS BIOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>10706</td>\n",
       "      <td>4747</td>\n",
       "      <td>5959</td>\n",
       "      <td>0.556604</td>\n",
       "      <td>33500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>6106</td>\n",
       "      <td>HEALTH AND MEDICAL PREPARATORY PROGRAMS</td>\n",
       "      <td>Health</td>\n",
       "      <td>12740</td>\n",
       "      <td>5521</td>\n",
       "      <td>7219</td>\n",
       "      <td>0.566641</td>\n",
       "      <td>33500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>3600</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>280709</td>\n",
       "      <td>111762</td>\n",
       "      <td>168947</td>\n",
       "      <td>0.601858</td>\n",
       "      <td>33400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>3604</td>\n",
       "      <td>ECOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>9154</td>\n",
       "      <td>3878</td>\n",
       "      <td>5276</td>\n",
       "      <td>0.576360</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>6109</td>\n",
       "      <td>TREATMENT THERAPY PROFESSIONS</td>\n",
       "      <td>Health</td>\n",
       "      <td>48491</td>\n",
       "      <td>13487</td>\n",
       "      <td>35004</td>\n",
       "      <td>0.721866</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>6100</td>\n",
       "      <td>GENERAL MEDICAL AND HEALTH SERVICES</td>\n",
       "      <td>Health</td>\n",
       "      <td>33599</td>\n",
       "      <td>7574</td>\n",
       "      <td>26025</td>\n",
       "      <td>0.774577</td>\n",
       "      <td>32400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>6102</td>\n",
       "      <td>COMMUNICATION DISORDERS SCIENCES AND SERVICES</td>\n",
       "      <td>Health</td>\n",
       "      <td>38279</td>\n",
       "      <td>1225</td>\n",
       "      <td>37054</td>\n",
       "      <td>0.967998</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>3609</td>\n",
       "      <td>ZOOLOGY</td>\n",
       "      <td>Biology &amp; Life Science</td>\n",
       "      <td>8409</td>\n",
       "      <td>3050</td>\n",
       "      <td>5359</td>\n",
       "      <td>0.637293</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank  Major_code                                              Major  \\\n",
       "0      1        2419                              PETROLEUM ENGINEERING   \n",
       "1      2        2416                     MINING AND MINERAL ENGINEERING   \n",
       "2      3        2415                          METALLURGICAL ENGINEERING   \n",
       "3      4        2417          NAVAL ARCHITECTURE AND MARINE ENGINEERING   \n",
       "4      5        2418                                NUCLEAR ENGINEERING   \n",
       "5      6        2405                               CHEMICAL ENGINEERING   \n",
       "6      7        5001                         ASTRONOMY AND ASTROPHYSICS   \n",
       "7      8        2414                             MECHANICAL ENGINEERING   \n",
       "8      9        2401                              AEROSPACE ENGINEERING   \n",
       "9     10        2408                             ELECTRICAL ENGINEERING   \n",
       "10    11        2407                               COMPUTER ENGINEERING   \n",
       "11    12        5008                                  MATERIALS SCIENCE   \n",
       "12    13        2404                             BIOMEDICAL ENGINEERING   \n",
       "13    14        2409          ENGINEERING MECHANICS PHYSICS AND SCIENCE   \n",
       "14    15        2402                             BIOLOGICAL ENGINEERING   \n",
       "15    16        2412           INDUSTRIAL AND MANUFACTURING ENGINEERING   \n",
       "16    17        2400                                GENERAL ENGINEERING   \n",
       "17    18        2403                          ARCHITECTURAL ENGINEERING   \n",
       "18    19        2102                                   COMPUTER SCIENCE   \n",
       "19    20        2502                  ELECTRICAL ENGINEERING TECHNOLOGY   \n",
       "20    21        2413        MATERIALS ENGINEERING AND MATERIALS SCIENCE   \n",
       "21    22        2499                          MISCELLANEOUS ENGINEERING   \n",
       "22    23        2406                                  CIVIL ENGINEERING   \n",
       "23    24        2500                           ENGINEERING TECHNOLOGIES   \n",
       "24    25        2411             GEOLOGICAL AND GEOPHYSICAL ENGINEERING   \n",
       "25    26        2410                          ENVIRONMENTAL ENGINEERING   \n",
       "26    27        6107                                            NURSING   \n",
       "27    28        2503                 INDUSTRIAL PRODUCTION TECHNOLOGIES   \n",
       "28    29        5102  NUCLEAR, INDUSTRIAL RADIOLOGY, AND BIOLOGICAL ...   \n",
       "29    30        2105                               INFORMATION SCIENCES   \n",
       "..   ...         ...                                                ...   \n",
       "46    47        1401                                       ARCHITECTURE   \n",
       "47    48        3605                                           GENETICS   \n",
       "48    49        3603                                  MOLECULAR BIOLOGY   \n",
       "49    50        6108  PHARMACY PHARMACEUTICAL SCIENCES AND ADMINISTR...   \n",
       "50    51        5003                                          CHEMISTRY   \n",
       "51    52        3606                                       MICROBIOLOGY   \n",
       "52    53        2106    COMPUTER ADMINISTRATION MANAGEMENT AND SECURITY   \n",
       "53    54        3601                               BIOCHEMICAL SCIENCES   \n",
       "54    55        3602                                             BOTANY   \n",
       "55    56        2107         COMPUTER NETWORKING AND TELECOMMUNICATIONS   \n",
       "56    57        5004                          GEOLOGY AND EARTH SCIENCE   \n",
       "57    58        5005                                        GEOSCIENCES   \n",
       "58    59        6199           MISCELLANEOUS HEALTH MEDICAL PROFESSIONS   \n",
       "59    60        1301                              ENVIRONMENTAL SCIENCE   \n",
       "60    61        5002               ATMOSPHERIC SCIENCES AND METEOROLOGY   \n",
       "61    62        2001                         COMMUNICATION TECHNOLOGIES   \n",
       "62    63        5098              MULTI-DISCIPLINARY OR GENERAL SCIENCE   \n",
       "63    64        3608                                         PHYSIOLOGY   \n",
       "64    65        3611                                       NEUROSCIENCE   \n",
       "65    66        6103         HEALTH AND MEDICAL ADMINISTRATIVE SERVICES   \n",
       "66    67        4002                                 NUTRITION SCIENCES   \n",
       "67    68        6110                        COMMUNITY AND PUBLIC HEALTH   \n",
       "68    69        3699                              MISCELLANEOUS BIOLOGY   \n",
       "69    70        6106            HEALTH AND MEDICAL PREPARATORY PROGRAMS   \n",
       "70    71        3600                                            BIOLOGY   \n",
       "71    72        3604                                            ECOLOGY   \n",
       "72    73        6109                      TREATMENT THERAPY PROFESSIONS   \n",
       "73    74        6100                GENERAL MEDICAL AND HEALTH SERVICES   \n",
       "74    75        6102      COMMUNICATION DISORDERS SCIENCES AND SERVICES   \n",
       "75    76        3609                                            ZOOLOGY   \n",
       "\n",
       "             Major_category   Total     Men   Women  ShareWomen  Median  \n",
       "0               Engineering    2339    2057     282    0.120564  110000  \n",
       "1               Engineering     756     679      77    0.101852   75000  \n",
       "2               Engineering     856     725     131    0.153037   73000  \n",
       "3               Engineering    1258    1123     135    0.107313   70000  \n",
       "4               Engineering    2573    2200     373    0.144967   65000  \n",
       "5               Engineering   32260   21239   11021    0.341631   65000  \n",
       "6         Physical Sciences    1792     832     960    0.535714   62000  \n",
       "7               Engineering   91227   80320   10907    0.119559   60000  \n",
       "8               Engineering   15058   12953    2105    0.139793   60000  \n",
       "9               Engineering   81527   65511   16016    0.196450   60000  \n",
       "10              Engineering   41542   33258    8284    0.199413   60000  \n",
       "11              Engineering    4279    2949    1330    0.310820   60000  \n",
       "12              Engineering   14955    8407    6548    0.437847   60000  \n",
       "13              Engineering    4321    3526     795    0.183985   58000  \n",
       "14              Engineering    8925    6062    2863    0.320784   57100  \n",
       "15              Engineering   18968   12453    6515    0.343473   57000  \n",
       "16              Engineering   61152   45683   15469    0.252960   56000  \n",
       "17              Engineering    2825    1835     990    0.350442   54000  \n",
       "18  Computers & Mathematics  128319   99743   28576    0.222695   53000  \n",
       "19              Engineering   11565    8181    3384    0.292607   52000  \n",
       "20              Engineering    2993    2020     973    0.325092   52000  \n",
       "21              Engineering    9133    7398    1735    0.189970   50000  \n",
       "22              Engineering   53153   41081   12072    0.227118   50000  \n",
       "23              Engineering    3600    2695     905    0.251389   50000  \n",
       "24              Engineering     720     488     232    0.322222   50000  \n",
       "25              Engineering    4047    2662    1385    0.342229   50000  \n",
       "26                   Health  209394   21773  187621    0.896019   48000  \n",
       "27              Engineering    4631    3477    1154    0.249190   46000  \n",
       "28        Physical Sciences    2116     528    1588    0.750473   46000  \n",
       "29  Computers & Mathematics   11913    9005    2908    0.244103   45000  \n",
       "..                      ...     ...     ...     ...         ...     ...  \n",
       "46              Engineering   46420   25463   20957    0.451465   40000  \n",
       "47   Biology & Life Science    3635    1761    1874    0.515543   40000  \n",
       "48   Biology & Life Science   18300    7426   10874    0.594208   40000  \n",
       "49                   Health   23551    8697   14854    0.630716   40000  \n",
       "50        Physical Sciences   66530   32923   33607    0.505141   39000  \n",
       "51   Biology & Life Science   15232    6383    8849    0.580948   38000  \n",
       "52  Computers & Mathematics    8066    6607    1459    0.180883   37500  \n",
       "53   Biology & Life Science   39107   18951   20156    0.515406   37400  \n",
       "54   Biology & Life Science    1329     626     703    0.528969   37000  \n",
       "55  Computers & Mathematics    7613    5291    2322    0.305005   36400  \n",
       "56        Physical Sciences   10972    5813    5159    0.470197   36200  \n",
       "57        Physical Sciences    1978     809    1169    0.591001   36000  \n",
       "58                   Health   13386    1589   11797    0.881294   36000  \n",
       "59   Biology & Life Science   25965   10787   15178    0.584556   35600  \n",
       "60        Physical Sciences    4043    2744    1299    0.321296   35000  \n",
       "61  Computers & Mathematics   18035   11431    6604    0.366177   35000  \n",
       "62        Physical Sciences   62052   27015   35037    0.564639   35000  \n",
       "63   Biology & Life Science   22060    8422   13638    0.618223   35000  \n",
       "64   Biology & Life Science   13663    4944    8719    0.638147   35000  \n",
       "65                   Health   18109    4266   13843    0.764427   35000  \n",
       "66                   Health   18909    2563   16346    0.864456   35000  \n",
       "67                   Health   19735    4103   15632    0.792095   34000  \n",
       "68   Biology & Life Science   10706    4747    5959    0.556604   33500  \n",
       "69                   Health   12740    5521    7219    0.566641   33500  \n",
       "70   Biology & Life Science  280709  111762  168947    0.601858   33400  \n",
       "71   Biology & Life Science    9154    3878    5276    0.576360   33000  \n",
       "72                   Health   48491   13487   35004    0.721866   33000  \n",
       "73                   Health   33599    7574   26025    0.774577   32400  \n",
       "74                   Health   38279    1225   37054    0.967998   28000  \n",
       "75   Biology & Life Science    8409    3050    5359    0.637293   26000  \n",
       "\n",
       "[76 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying entire df; DO NOT recommend if you have a large data set\n",
    "df_WomenSTEM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Biology &amp; Life Science</th>\n",
       "      <td>36421.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computers &amp; Mathematics</th>\n",
       "      <td>42745.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>57382.758621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>36825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical Sciences</th>\n",
       "      <td>41890.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Median\n",
       "Major_category                       \n",
       "Biology & Life Science   36421.428571\n",
       "Computers & Mathematics  42745.454545\n",
       "Engineering              57382.758621\n",
       "Health                   36825.000000\n",
       "Physical Sciences        41890.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_WomenSTEM.groupby([\"Major_category\"])[[\"Median\"]].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ShareWomen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Biology &amp; Life Science</th>\n",
       "      <td>0.587193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computers &amp; Mathematics</th>\n",
       "      <td>0.311772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>0.238889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>0.795152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Physical Sciences</th>\n",
       "      <td>0.508683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ShareWomen\n",
       "Major_category                     \n",
       "Biology & Life Science     0.587193\n",
       "Computers & Mathematics    0.311772\n",
       "Engineering                0.238889\n",
       "Health                     0.795152\n",
       "Physical Sciences          0.508683"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_women_gb = df_WomenSTEM.groupby([\"Major_category\"])[[\"ShareWomen\"]].mean()\n",
    "major_women_gb #created a variable so I can use later while graphing"
   ]
  },
  {
   "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>Rank</th>\n",
       "      <th>Major_code</th>\n",
       "      <th>Total</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>ShareWomen</th>\n",
       "      <th>Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.493366</td>\n",
       "      <td>0.073390</td>\n",
       "      <td>-0.107290</td>\n",
       "      <td>0.189810</td>\n",
       "      <td>0.646869</td>\n",
       "      <td>-0.889302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major_code</th>\n",
       "      <td>0.493366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081084</td>\n",
       "      <td>-0.184553</td>\n",
       "      <td>0.258120</td>\n",
       "      <td>0.748909</td>\n",
       "      <td>-0.422164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.073390</td>\n",
       "      <td>0.081084</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.824321</td>\n",
       "      <td>0.909751</td>\n",
       "      <td>0.155359</td>\n",
       "      <td>-0.081263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Men</th>\n",
       "      <td>-0.107290</td>\n",
       "      <td>-0.184553</td>\n",
       "      <td>0.824321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.514899</td>\n",
       "      <td>-0.162413</td>\n",
       "      <td>0.058209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Women</th>\n",
       "      <td>0.189810</td>\n",
       "      <td>0.258120</td>\n",
       "      <td>0.909751</td>\n",
       "      <td>0.514899</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354353</td>\n",
       "      <td>-0.165738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShareWomen</th>\n",
       "      <td>0.646869</td>\n",
       "      <td>0.748909</td>\n",
       "      <td>0.155359</td>\n",
       "      <td>-0.162413</td>\n",
       "      <td>0.354353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.582589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>-0.889302</td>\n",
       "      <td>-0.422164</td>\n",
       "      <td>-0.081263</td>\n",
       "      <td>0.058209</td>\n",
       "      <td>-0.165738</td>\n",
       "      <td>-0.582589</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rank  Major_code     Total       Men     Women  ShareWomen  \\\n",
       "Rank        1.000000    0.493366  0.073390 -0.107290  0.189810    0.646869   \n",
       "Major_code  0.493366    1.000000  0.081084 -0.184553  0.258120    0.748909   \n",
       "Total       0.073390    0.081084  1.000000  0.824321  0.909751    0.155359   \n",
       "Men        -0.107290   -0.184553  0.824321  1.000000  0.514899   -0.162413   \n",
       "Women       0.189810    0.258120  0.909751  0.514899  1.000000    0.354353   \n",
       "ShareWomen  0.646869    0.748909  0.155359 -0.162413  0.354353    1.000000   \n",
       "Median     -0.889302   -0.422164 -0.081263  0.058209 -0.165738   -0.582589   \n",
       "\n",
       "              Median  \n",
       "Rank       -0.889302  \n",
       "Major_code -0.422164  \n",
       "Total      -0.081263  \n",
       "Men         0.058209  \n",
       "Women      -0.165738  \n",
       "ShareWomen -0.582589  \n",
       "Median      1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_WomenSTEM.corr() #table of correlation between all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cb0ea90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing as bar graph again, using pandas\n",
    "\n",
    "major_women_gb.plot.bar(title=\"Percentage of Women in STEM Majors\", color=\"navy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting as scatter plot, using matplotlib\n",
    "sharewomen = df_WomenSTEM[\"ShareWomen\"] #saving ShareWomen series as a variable, to use later\n",
    "womenmedian = df_WomenSTEM[\"Median\"] #new variable for another series, Median (salary)\n",
    "\n",
    "plt.scatter(list([sharewomen]), list([womenmedian]))#converted series into lists directly, and plotted the lists\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}