PGP-AgeHeightWeight.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age, Weight and Height of PGP Participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set default plot stype for matplotlib\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# Set default figure size for matplotlib\n",
    "plt.rcParams['figure.figsize'] = (12, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Connect to untap database (using sqlite3), found here: \n",
    "# https://workbench.su92l.arvadosapi.com/projects/su92l-j7d0g-k6xjjk9g0l5pmqh#Description\n",
    "\n",
    "conn = sqlite3.connect('Database/untap.db')\n",
    "c = conn.cursor()\n",
    "c.execute('SELECT * FROM demographics')\n",
    "rows = c.fetchall()\n",
    "\n",
    "# Converting to a dataframe\n",
    "demographics = pd.DataFrame(rows,columns=zip(*c.description)[0])\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>human_id</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>gender</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>blood_type</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>hu43860C</td>\n",
       "      <td>1954-08-28 (61 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>246lbs (112kg)</td>\n",
       "      <td>6ft 5in (195cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>huC30901</td>\n",
       "      <td>1962-05-23 (53 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>175lbs (79kg)</td>\n",
       "      <td>6ft 2in (187cm)</td>\n",
       "      <td>O-</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>hu9385BA</td>\n",
       "      <td>1955-07-05 (60 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>155lbs (70kg)</td>\n",
       "      <td></td>\n",
       "      <td>A+</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>hu04FD18</td>\n",
       "      <td>1954-09-18 (61 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>huAE6220</td>\n",
       "      <td>1949-04-24 (66 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>B+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>hu604D39</td>\n",
       "      <td>1958-01-19 (58 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>245lbs (111kg)</td>\n",
       "      <td>5ft 8in (172cm)</td>\n",
       "      <td>AB+</td>\n",
       "      <td>Black or African American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>hu6E4515</td>\n",
       "      <td>1921-07-09 (94 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>180lbs (82kg)</td>\n",
       "      <td>6ft (182cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>huA90CE6</td>\n",
       "      <td>1958-09-25 (57 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>hu34D5B9</td>\n",
       "      <td>1973-01-20 (43 years old)</td>\n",
       "      <td></td>\n",
       "      <td>170lbs (77kg)</td>\n",
       "      <td>5ft 11in (180cm)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>hu2FEC01</td>\n",
       "      <td>1985-10-06 (30 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>143lbs (65kg)</td>\n",
       "      <td>5ft 9in (175cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>huAA16BD</td>\n",
       "      <td>1927-11-17 (88 years old)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>hu2DBF2D</td>\n",
       "      <td>1982-12-07 (33 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>190lbs (86kg)</td>\n",
       "      <td>5ft 10in (177cm)</td>\n",
       "      <td>O-</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>hu342A08</td>\n",
       "      <td>1943-09-18 (72 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>160lbs (73kg)</td>\n",
       "      <td>5ft 9in (175cm)</td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>hu4339C0</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>14</th>\n",
       "      <td>15</td>\n",
       "      <td>hu72A81D</td>\n",
       "      <td>1968-11-01 (47 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>145lbs (66kg)</td>\n",
       "      <td>5ft 10in (177cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>hu0E64A1</td>\n",
       "      <td>1985-02-20 (30 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>120lbs (54kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>hu3CAB43</td>\n",
       "      <td>1967-11-30 (48 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>115lbs (52kg)</td>\n",
       "      <td>5ft 6in (167cm)</td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>hu2D6140</td>\n",
       "      <td>1942-01-05 (74 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>142lbs (64kg)</td>\n",
       "      <td>5ft 6in (167cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>huA0E089</td>\n",
       "      <td>1942-11-22 (73 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>139lbs (63kg)</td>\n",
       "      <td>5ft 4in (162cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>hu8229AE</td>\n",
       "      <td>1969-09-11 (46 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>153lbs (69kg)</td>\n",
       "      <td>5ft 2in (157cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>huCA017E</td>\n",
       "      <td>1949-03-19 (66 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>142lbs (64kg)</td>\n",
       "      <td>5ft 6in (167cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>hu92C40A</td>\n",
       "      <td>1944-01-13 (72 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>132lbs (60kg)</td>\n",
       "      <td>5ft 7in (170cm)</td>\n",
       "      <td></td>\n",
       "      <td>American Indian or Alaska Native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>hu38168C</td>\n",
       "      <td>1949-07-08 (66 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>105lbs (48kg)</td>\n",
       "      <td>5ft 3in (160cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>huB1FD55</td>\n",
       "      <td>1958-09-19 (57 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>huD81F3D</td>\n",
       "      <td>1969-04-24 (46 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>210lbs (95kg)</td>\n",
       "      <td>6ft 3in (190cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>huD37D14</td>\n",
       "      <td>1970-02-20 (45 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>147lbs (67kg)</td>\n",
       "      <td>5ft 6in (167cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>huBAAC98</td>\n",
       "      <td>1980-07-30 (35 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>240lbs (109kg)</td>\n",
       "      <td>6ft 2in (187cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>huB4940E</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>28</th>\n",
       "      <td>29</td>\n",
       "      <td>huEC6EEC</td>\n",
       "      <td>1964-07-10 (51 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>180lbs (82kg)</td>\n",
       "      <td>5ft 4in (162cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>Hispanic or Latino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>huF5E0B6</td>\n",
       "      <td>1971-12-12 (44 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>185lbs (84kg)</td>\n",
       "      <td>6ft (182cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304</td>\n",
       "      <td>hu4A6650</td>\n",
       "      <td>1943-10-26 (72 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>227lbs (103kg)</td>\n",
       "      <td>5ft 11in (180cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>hu594129</td>\n",
       "      <td>1962-02-20 (53 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>163lbs (74kg)</td>\n",
       "      <td>5ft 11in (180cm)</td>\n",
       "      <td>B+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>huB256A2</td>\n",
       "      <td>1982-04-14 (33 years old)</td>\n",
       "      <td></td>\n",
       "      <td>115lbs (52kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>hu7D6AB1</td>\n",
       "      <td>1948-07-30 (67 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>93lbs (42kg)</td>\n",
       "      <td>5ft (152cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>huFEC65A</td>\n",
       "      <td>1969-11-25 (46 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>huF9A043</td>\n",
       "      <td>1957-01-08 (59 years old)</td>\n",
       "      <td></td>\n",
       "      <td>162lbs (73kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1310</td>\n",
       "      <td>huF9C3EE</td>\n",
       "      <td>1985-11-20 (30 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>170lbs (77kg)</td>\n",
       "      <td>5ft 10in (177cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>Asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>1311</td>\n",
       "      <td>huD0127A</td>\n",
       "      <td>1964-05-22 (51 years old)</td>\n",
       "      <td></td>\n",
       "      <td>212lbs (96kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>1312</td>\n",
       "      <td>hu5C4EBA</td>\n",
       "      <td>1966-10-27 (49 years old)</td>\n",
       "      <td></td>\n",
       "      <td>270lbs (122kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>1313</td>\n",
       "      <td>huD66092</td>\n",
       "      <td>1948-01-02 (68 years old)</td>\n",
       "      <td></td>\n",
       "      <td>116lbs (53kg)</td>\n",
       "      <td>5ft 4in (162cm)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>1314</td>\n",
       "      <td>hu039674</td>\n",
       "      <td>1981-04-18 (34 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>165lbs (75kg)</td>\n",
       "      <td>5ft 6in (167cm)</td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>1315</td>\n",
       "      <td>hu941A10</td>\n",
       "      <td>1946-03-01 (69 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>140lbs (64kg)</td>\n",
       "      <td>5ft 3in (160cm)</td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>1316</td>\n",
       "      <td>hu1D278B</td>\n",
       "      <td>1976-11-26 (39 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>190lbs (86kg)</td>\n",
       "      <td>5ft 11in (180cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>Hispanic/Latino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>1317</td>\n",
       "      <td>hu045168</td>\n",
       "      <td>1980-06-04 (35 years old)</td>\n",
       "      <td></td>\n",
       "      <td>143lbs (65kg)</td>\n",
       "      <td>5ft 9in (175cm)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>1318</td>\n",
       "      <td>hu2561FE</td>\n",
       "      <td>1955-01-08 (61 years old)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5ft 9in (175cm)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>1319</td>\n",
       "      <td>huD8A5B6</td>\n",
       "      <td>1956-01-28 (60 years old)</td>\n",
       "      <td></td>\n",
       "      <td>184lbs (83kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>1320</td>\n",
       "      <td>hu2CA680</td>\n",
       "      <td>1964-12-24 (51 years old)</td>\n",
       "      <td></td>\n",
       "      <td>140lbs (64kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>1321</td>\n",
       "      <td>hu631D32</td>\n",
       "      <td>1937-10-19 (78 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>156lbs (71kg)</td>\n",
       "      <td>5ft 7in (170cm)</td>\n",
       "      <td>AB+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>1322</td>\n",
       "      <td>hu934217</td>\n",
       "      <td>1962-10-01 (53 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>191lbs (87kg)</td>\n",
       "      <td>5ft 6in (167cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>1323</td>\n",
       "      <td>hu524B5B</td>\n",
       "      <td>1961-05-26 (54 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>147lbs (67kg)</td>\n",
       "      <td>5ft 3in (160cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>1324</td>\n",
       "      <td>hu78E6A9</td>\n",
       "      <td>1963-05-20 (52 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>142lbs (64kg)</td>\n",
       "      <td>5ft 5in (165cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>1325</td>\n",
       "      <td>huDF8970</td>\n",
       "      <td>1975-07-16 (40 years old)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>1326</td>\n",
       "      <td>hu313A20</td>\n",
       "      <td>1982-05-29 (33 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>165lbs (75kg)</td>\n",
       "      <td>6ft (182cm)</td>\n",
       "      <td>O+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>1327</td>\n",
       "      <td>huFBD57F</td>\n",
       "      <td>1962-03-27 (53 years old)</td>\n",
       "      <td>Male</td>\n",
       "      <td>201lbs (91kg)</td>\n",
       "      <td>5ft 9in (175cm)</td>\n",
       "      <td>A+</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>1328</td>\n",
       "      <td>huC5203C</td>\n",
       "      <td>1978-12-28 (37 years old)</td>\n",
       "      <td>Female</td>\n",
       "      <td>98lbs (44kg)</td>\n",
       "      <td>5ft (152cm)</td>\n",
       "      <td></td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>1329</td>\n",
       "      <td>huABB4CD</td>\n",
       "      <td>1975-01-08 (41 years old)</td>\n",
       "      <td></td>\n",
       "      <td>174lbs (79kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>1330</td>\n",
       "      <td>huDD1522</td>\n",
       "      <td>1986-02-06 (29 years old)</td>\n",
       "      <td></td>\n",
       "      <td>153lbs (69kg)</td>\n",
       "      <td>5ft 4in (162cm)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>1331</td>\n",
       "      <td>hu25C433</td>\n",
       "      <td>1951-08-17 (64 years old)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>1332</td>\n",
       "      <td>hu419539</td>\n",
       "      <td>1974-07-22 (41 years old)</td>\n",
       "      <td></td>\n",
       "      <td>161lbs (73kg)</td>\n",
       "      <td>6ft (182cm)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>1333</td>\n",
       "      <td>hu7FC773</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>108lbs (49kg)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1333 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  human_id              date_of_birth  gender          weight  \\\n",
       "0        1  hu43860C  1954-08-28 (61 years old)    Male  246lbs (112kg)   \n",
       "1        2  huC30901  1962-05-23 (53 years old)    Male   175lbs (79kg)   \n",
       "2        3  hu9385BA  1955-07-05 (60 years old)    Male   155lbs (70kg)   \n",
       "3        4  hu04FD18  1954-09-18 (61 years old)    Male                   \n",
       "4        5  huAE6220  1949-04-24 (66 years old)    Male                   \n",
       "5        6  hu604D39  1958-01-19 (58 years old)    Male  245lbs (111kg)   \n",
       "6        7  hu6E4515  1921-07-09 (94 years old)    Male   180lbs (82kg)   \n",
       "7        8  huA90CE6  1958-09-25 (57 years old)    Male                   \n",
       "8        9  hu34D5B9  1973-01-20 (43 years old)           170lbs (77kg)   \n",
       "9       10  hu2FEC01  1985-10-06 (30 years old)    Male   143lbs (65kg)   \n",
       "10      11  huAA16BD  1927-11-17 (88 years old)                           \n",
       "11      12  hu2DBF2D  1982-12-07 (33 years old)    Male   190lbs (86kg)   \n",
       "12      13  hu342A08  1943-09-18 (72 years old)  Female   160lbs (73kg)   \n",
       "13      14  hu4339C0                                                      \n",
       "14      15  hu72A81D  1968-11-01 (47 years old)  Female   145lbs (66kg)   \n",
       "15      16  hu0E64A1  1985-02-20 (30 years old)    Male   120lbs (54kg)   \n",
       "16      17  hu3CAB43  1967-11-30 (48 years old)    Male   115lbs (52kg)   \n",
       "17      18  hu2D6140  1942-01-05 (74 years old)  Female   142lbs (64kg)   \n",
       "18      19  huA0E089  1942-11-22 (73 years old)  Female   139lbs (63kg)   \n",
       "19      20  hu8229AE  1969-09-11 (46 years old)  Female   153lbs (69kg)   \n",
       "20      21  huCA017E  1949-03-19 (66 years old)    Male   142lbs (64kg)   \n",
       "21      22  hu92C40A  1944-01-13 (72 years old)  Female   132lbs (60kg)   \n",
       "22      23  hu38168C  1949-07-08 (66 years old)  Female   105lbs (48kg)   \n",
       "23      24  huB1FD55  1958-09-19 (57 years old)    Male                   \n",
       "24      25  huD81F3D  1969-04-24 (46 years old)    Male   210lbs (95kg)   \n",
       "25      26  huD37D14  1970-02-20 (45 years old)  Female   147lbs (67kg)   \n",
       "26      27  huBAAC98  1980-07-30 (35 years old)    Male  240lbs (109kg)   \n",
       "27      28  huB4940E                                                      \n",
       "28      29  huEC6EEC  1964-07-10 (51 years old)  Female   180lbs (82kg)   \n",
       "29      30  huF5E0B6  1971-12-12 (44 years old)    Male   185lbs (84kg)   \n",
       "...    ...       ...                        ...     ...             ...   \n",
       "1303  1304  hu4A6650  1943-10-26 (72 years old)    Male  227lbs (103kg)   \n",
       "1304  1305  hu594129  1962-02-20 (53 years old)    Male   163lbs (74kg)   \n",
       "1305  1306  huB256A2  1982-04-14 (33 years old)           115lbs (52kg)   \n",
       "1306  1307  hu7D6AB1  1948-07-30 (67 years old)  Female    93lbs (42kg)   \n",
       "1307  1308  huFEC65A  1969-11-25 (46 years old)    Male                   \n",
       "1308  1309  huF9A043  1957-01-08 (59 years old)           162lbs (73kg)   \n",
       "1309  1310  huF9C3EE  1985-11-20 (30 years old)    Male   170lbs (77kg)   \n",
       "1310  1311  huD0127A  1964-05-22 (51 years old)           212lbs (96kg)   \n",
       "1311  1312  hu5C4EBA  1966-10-27 (49 years old)          270lbs (122kg)   \n",
       "1312  1313  huD66092  1948-01-02 (68 years old)           116lbs (53kg)   \n",
       "1313  1314  hu039674  1981-04-18 (34 years old)    Male   165lbs (75kg)   \n",
       "1314  1315  hu941A10  1946-03-01 (69 years old)  Female   140lbs (64kg)   \n",
       "1315  1316  hu1D278B  1976-11-26 (39 years old)    Male   190lbs (86kg)   \n",
       "1316  1317  hu045168  1980-06-04 (35 years old)           143lbs (65kg)   \n",
       "1317  1318  hu2561FE  1955-01-08 (61 years old)                           \n",
       "1318  1319  huD8A5B6  1956-01-28 (60 years old)           184lbs (83kg)   \n",
       "1319  1320  hu2CA680  1964-12-24 (51 years old)           140lbs (64kg)   \n",
       "1320  1321  hu631D32  1937-10-19 (78 years old)    Male   156lbs (71kg)   \n",
       "1321  1322  hu934217  1962-10-01 (53 years old)    Male   191lbs (87kg)   \n",
       "1322  1323  hu524B5B  1961-05-26 (54 years old)  Female   147lbs (67kg)   \n",
       "1323  1324  hu78E6A9  1963-05-20 (52 years old)  Female   142lbs (64kg)   \n",
       "1324  1325  huDF8970  1975-07-16 (40 years old)                           \n",
       "1325  1326  hu313A20  1982-05-29 (33 years old)    Male   165lbs (75kg)   \n",
       "1326  1327  huFBD57F  1962-03-27 (53 years old)    Male   201lbs (91kg)   \n",
       "1327  1328  huC5203C  1978-12-28 (37 years old)  Female    98lbs (44kg)   \n",
       "1328  1329  huABB4CD  1975-01-08 (41 years old)           174lbs (79kg)   \n",
       "1329  1330  huDD1522  1986-02-06 (29 years old)           153lbs (69kg)   \n",
       "1330  1331  hu25C433  1951-08-17 (64 years old)                           \n",
       "1331  1332  hu419539  1974-07-22 (41 years old)           161lbs (73kg)   \n",
       "1332  1333  hu7FC773                                      108lbs (49kg)   \n",
       "\n",
       "                height blood_type                              race  \n",
       "0      6ft 5in (195cm)         O+                             White  \n",
       "1      6ft 2in (187cm)         O-                             White  \n",
       "2                              A+                                    \n",
       "3                              O+                             White  \n",
       "4                              B+                             White  \n",
       "5      5ft 8in (172cm)        AB+         Black or African American  \n",
       "6          6ft (182cm)         O+                             White  \n",
       "7                                                             White  \n",
       "8     5ft 11in (180cm)                                               \n",
       "9      5ft 9in (175cm)         A+                             White  \n",
       "10                                                                   \n",
       "11    5ft 10in (177cm)         O-                             White  \n",
       "12     5ft 9in (175cm)                                        White  \n",
       "13                                                                   \n",
       "14    5ft 10in (177cm)         A+                             White  \n",
       "15                                                            White  \n",
       "16     5ft 6in (167cm)                                        White  \n",
       "17     5ft 6in (167cm)         O+                             White  \n",
       "18     5ft 4in (162cm)         A+                             White  \n",
       "19     5ft 2in (157cm)         O+                             White  \n",
       "20     5ft 6in (167cm)         A+                             Asian  \n",
       "21     5ft 7in (170cm)             American Indian or Alaska Native  \n",
       "22     5ft 3in (160cm)         O+                             Asian  \n",
       "23                                                            White  \n",
       "24     6ft 3in (190cm)         A+                             White  \n",
       "25     5ft 6in (167cm)         O+                             White  \n",
       "26     6ft 2in (187cm)         O+                             White  \n",
       "27                                                                   \n",
       "28     5ft 4in (162cm)         O+                Hispanic or Latino  \n",
       "29         6ft (182cm)         A+                             White  \n",
       "...                ...        ...                               ...  \n",
       "1303  5ft 11in (180cm)         A+                             White  \n",
       "1304  5ft 11in (180cm)         B+                             White  \n",
       "1305                                                                 \n",
       "1306       5ft (152cm)         O+                             Asian  \n",
       "1307                                                          White  \n",
       "1308                                                                 \n",
       "1309  5ft 10in (177cm)         A+                             Asian  \n",
       "1310                                                                 \n",
       "1311                                                                 \n",
       "1312   5ft 4in (162cm)                                               \n",
       "1313   5ft 6in (167cm)                                        White  \n",
       "1314   5ft 3in (160cm)                                        White  \n",
       "1315  5ft 11in (180cm)         O+                   Hispanic/Latino  \n",
       "1316   5ft 9in (175cm)                                               \n",
       "1317   5ft 9in (175cm)                                               \n",
       "1318                                                                 \n",
       "1319                                                                 \n",
       "1320   5ft 7in (170cm)        AB+                             White  \n",
       "1321   5ft 6in (167cm)         O+                             White  \n",
       "1322   5ft 3in (160cm)         O+                             White  \n",
       "1323   5ft 5in (165cm)         O+                             White  \n",
       "1324                                                                 \n",
       "1325       6ft (182cm)         O+                             White  \n",
       "1326   5ft 9in (175cm)         A+                             White  \n",
       "1327       5ft (152cm)                                        White  \n",
       "1328                                                                 \n",
       "1329   5ft 4in (162cm)                                               \n",
       "1330                                                                 \n",
       "1331       6ft (182cm)                                               \n",
       "1332                                                                 \n",
       "\n",
       "[1333 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Doing some basic data munging to clean up birth date data\n",
    "\n",
    "# Convert date to all lower case and strip out blank spaces for height and weight\n",
    "demographics.date_of_birth = demographics.date_of_birth.str.strip()\n",
    "\n",
    "# Remove characters with parantheses, replace empties with NaNs and convert to datetimes\n",
    "g = lambda x:re.sub('\\s\\([^)]*\\)','',x)   \n",
    "birthdate = demographics.date_of_birth.apply(g)\n",
    "\n",
    "demographics['only_birth'] = demographics.date_of_birth.apply(g)\n",
    "\n",
    "demographics.only_birth = demographics['only_birth'].replace(r'\\s+|^$', np.nan, regex=True)\n",
    "demographics.only_birth = pd.to_datetime(demographics.only_birth,format='%Y-%m-%d')\n",
    "\n",
    "# Calculate current age based on birth date\n",
    "ageSec = date.today()-demographics.only_birth\n",
    "demographics['only_age'] = ageSec/np.timedelta64(365,'D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PQf3LL7/UO++8o3fffVdms1mjR4/W6NGjFR0drb/97W+aM2eOFi1a5M+5AgAA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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc3a9a4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a histogram of ages \n",
    "\n",
    "# Find null values\n",
    "idx = demographics.only_age.notnull()\n",
    "\n",
    "# Create the histogram (excluding null values)\n",
    "plt.hist(demographics.only_age[idx],25,normed=True)\n",
    "\n",
    "plt.title(\"Histogram of PGP Ages\")\n",
    "plt.xlabel(\"age in yrs\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.savefig('Images/histogramAges.png',format='png',dpi=300)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JHTp08HKLAAAAgH/wKHwvWrRIN9xwg4YNGyaLxSKXy6UPP/xQL774olJSUrzd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\nIZRvAAAAwBDKNwAAAGAI5RsAAAAw5At7PutPP+AL7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc38f50dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stack histograms for male and female participants\n",
    "\n",
    "idxM = (demographics.gender == 'Male') & idx \n",
    "idxF = (demographics.gender == 'Female') & idx\n",
    "\n",
    "plt.hist([demographics.only_age[idxM], demographics.only_age[idxF]],25, stacked=True)\n",
    "\n",
    "plt.title(\"Histogram of PGP Ages\")\n",
    "plt.xlabel(\"age in yrs\")\n",
    "plt.ylabel(\"number of participants\")\n",
    "plt.legend(['Male','Female'])\n",
    "\n",
    "plt.savefig('Images/histogramAges.png',format='png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Data munging for weight data\n",
    "\n",
    "# Strip off any spaces/banks at beginning or end\n",
    "demographics.weight = demographics.weight.str.strip()\n",
    "\n",
    "# Extract weight (in kg) \n",
    "g2 = lambda x:x[x.find(\"(\")+1:x.find(\"k\")]\n",
    "only_weight = demographics.weight.apply(g2)\n",
    "\n",
    "demographics['only_weight'] = only_weight\n",
    "\n",
    "# Replace empties (spaces) with NaNs\n",
    "demographics.only_weight = demographics['only_weight'].replace(r'\\s+|^$', np.nan, regex=True)\n",
    "demographics.only_weight\n",
    "\n",
    "# Convert from strings to floats\n",
    "demographics['only_weight'] = demographics.only_weight.astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JSYm3ygTQwsXFxSk7O9vbZcBDzv58U1JSlJCQ4OWKALQEXrsI0+Fw6NixY1q8eLGmTJmi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\nAlIAFxEREREpIAVwEREREZEC+gKqC9T0nS4wswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc38f450d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the histogram of weights\n",
    "\n",
    "idx = demographics.only_weight.notnull()\n",
    "idxM = (demographics.gender == 'Male') & idx \n",
    "idxF = (demographics.gender == 'Female') & idx\n",
    "\n",
    "plt.hist([demographics.only_weight[idxM], demographics.only_weight[idxF]], 25)\n",
    "\n",
    "plt.title(\"Histogram of PGP Weights\")\n",
    "plt.xlabel(\"weight (kg)\")\n",
    "plt.ylabel(\"number of participants\")\n",
    "plt.legend(['Male','Female'])\n",
    "\n",
    "plt.savefig('Images/histogramWeights.png',format='png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Data munging for height data\n",
    "\n",
    "# strip off any spaces/banks at beginning or end\n",
    "demographics.height = demographics.height.str.strip()\n",
    "\n",
    "# extract height (in kg) \n",
    "g2 = lambda x:x[x.find(\"(\")+1:x.find(\"k\")]\n",
    "only_height = demographics.weight.apply(g2)\n",
    "\n",
    "demographics['only_height'] = only_height\n",
    "\n",
    "# replace empties (spaces) with NaNs\n",
    "demographics.only_height = demographics['only_height'].replace(r'\\s+|^$', np.nan, regex=True)\n",
    "\n",
    "# convert from strings to floats\n",
    "demographics['only_height'] = demographics.only_height.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NgnI6fBcVFenEiRPl1jEVIQAAAOA+twJ4RkaGnn32WR06dKjCunfeeafWiwIA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\nExEREREVEAM4EREREVEB/QcFmDNmYpMlDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc3a6df190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the histogram of heights\n",
    "\n",
    "idx = demographics.only_height.notnull()\n",
    "idxM = (demographics.gender == 'Male') & idx \n",
    "idxF = (demographics.gender == 'Female') & idx\n",
    "\n",
    "plt.hist([demographics.only_height[idxM], demographics.only_height[idxF]], 25)\n",
    "\n",
    "plt.title(\"Histogram of PGP heights\")\n",
    "plt.xlabel(\"height (cm)\")\n",
    "plt.ylabel(\"number of participants\")\n",
    "plt.legend(['Male','Female'])\n",
    "\n",
    "plt.savefig('Images/histogramHeights.png',format='png',dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}