Assingment2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5c13fa2b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Importing packages: \n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sb\n",
    "\n",
    "import sas7bdat\n",
    "from sas7bdat import *\n",
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "4b40b119",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd==1.2.0 in c:\\users\\dave_\\anaconda3\\lib\\site-packages (1.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd==1.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "cbd24d46",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Replicate</th>\n",
       "      <th>QID</th>\n",
       "      <th>t_FL_FEA</th>\n",
       "      <th>t_CtPo_EC</th>\n",
       "      <th>t_TtAr</th>\n",
       "      <th>t_CtAr</th>\n",
       "      <th>t_TbAr</th>\n",
       "      <th>t_TtBMD</th>\n",
       "      <th>t_CtBMD</th>\n",
       "      <th>t_CtTh</th>\n",
       "      <th>...</th>\n",
       "      <th>age_at_menop</th>\n",
       "      <th>meno_status</th>\n",
       "      <th>liver_ever</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>r_TTOP</th>\n",
       "      <th>t_TTOP</th>\n",
       "      <th>r_TTHip</th>\n",
       "      <th>t_TTHip</th>\n",
       "      <th>NumberHits</th>\n",
       "      <th>ID</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01545'</td>\n",
       "      <td>6204.0</td>\n",
       "      <td>9.5600</td>\n",
       "      <td>701.02992</td>\n",
       "      <td>99.59033</td>\n",
       "      <td>593.70643</td>\n",
       "      <td>281.8728</td>\n",
       "      <td>799.4792</td>\n",
       "      <td>1.063528</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>893.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01590'</td>\n",
       "      <td>5529.5</td>\n",
       "      <td>10.2100</td>\n",
       "      <td>689.24115</td>\n",
       "      <td>104.05397</td>\n",
       "      <td>576.36756</td>\n",
       "      <td>253.3904</td>\n",
       "      <td>813.7205</td>\n",
       "      <td>1.142136</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>894.0</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>b'CA01620'</td>\n",
       "      <td>5638.3</td>\n",
       "      <td>6.3200</td>\n",
       "      <td>588.60634</td>\n",
       "      <td>109.83789</td>\n",
       "      <td>473.16833</td>\n",
       "      <td>302.2676</td>\n",
       "      <td>873.6742</td>\n",
       "      <td>1.277396</td>\n",
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       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>690.8000</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1421.0</td>\n",
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       "      <th>1227</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'VR02755'</td>\n",
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       "      <td>92.40000</td>\n",
       "      <td>539.40000</td>\n",
       "      <td>266.3000</td>\n",
       "      <td>690.8000</td>\n",
       "      <td>0.920000</td>\n",
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       "      <td>63.0</td>\n",
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       "      <td>19.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.0838</td>\n",
       "      <td>825.30000</td>\n",
       "      <td>16.90000</td>\n",
       "      <td>770.10000</td>\n",
       "      <td>145.0000</td>\n",
       "      <td>374.6000</td>\n",
       "      <td>0.150000</td>\n",
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       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1425.0</td>\n",
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       "</table>\n",
       "<p>1231 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Replicate         QID  t_FL_FEA  t_CtPo_EC     t_TtAr     t_CtAr  \\\n",
       "0           1.0  b'CA01519'    4901.9     8.8300  960.47070   72.70221   \n",
       "1           1.0  b'CA01519'    4901.9     8.8300  960.47070   72.70221   \n",
       "2           1.0  b'CA01545'    6204.0     9.5600  701.02992   99.59033   \n",
       "3           1.0  b'CA01590'    5529.5    10.2100  689.24115  104.05397   \n",
       "4           1.0  b'CA01620'    5638.3     6.3200  588.60634  109.83789   \n",
       "...         ...         ...       ...        ...        ...        ...   \n",
       "1226        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1227        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1228        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1229        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1230        1.0  b'VR04913'    3375.0     0.0838  825.30000   16.90000   \n",
       "\n",
       "         t_TbAr   t_TtBMD   t_CtBMD    t_CtTh  ...  age_at_menop  meno_status  \\\n",
       "0     879.26660  160.3830  805.1053  0.757581  ...          53.0          2.0   \n",
       "1     879.26660  160.3830  805.1053  0.757581  ...          53.0          2.0   \n",
       "2     593.70643  281.8728  799.4792  1.063528  ...          32.0          3.0   \n",
       "3     576.36756  253.3904  813.7205  1.142136  ...          32.0          3.0   \n",
       "4     473.16833  302.2676  873.6742  1.277396  ...          55.0          3.0   \n",
       "...         ...       ...       ...       ...  ...           ...          ...   \n",
       "1226  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1227  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1228  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1229  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1230  770.10000  145.0000  374.6000  0.150000  ...          54.0          2.0   \n",
       "\n",
       "      liver_ever  STATUS  r_TTOP  t_TTOP  r_TTHip  t_TTHip  NumberHits      ID  \n",
       "0            0.0     0.0    23.0    23.0     23.0     23.0         2.0   891.0  \n",
       "1            0.0     0.0    23.0    23.0     23.0     23.0         2.0   892.0  \n",
       "2            0.0     0.0    34.0    34.0     34.0     34.0         1.0   893.0  \n",
       "3            0.0     0.0    34.0    34.0     34.0     34.0         1.0   894.0  \n",
       "4            0.0     0.0    29.0    29.0     29.0     29.0         3.0   895.0  \n",
       "...          ...     ...     ...     ...      ...      ...         ...     ...  \n",
       "1226         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1421.0  \n",
       "1227         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1422.0  \n",
       "1228         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1423.0  \n",
       "1229         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1424.0  \n",
       "1230         0.0     2.0     6.0     6.0      6.0      6.0         1.0  1425.0  \n",
       "\n",
       "[1231 rows x 76 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read SAS data and assign to a dataframe name\n",
    "\n",
    "xtrtmp = pd.read_sas(\"xtrfxnd.sas7bdat\",format='sas7bdat')\n",
    "\n",
    "xtrtmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "4077c865",
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>replicate</th>\n",
       "      <th>qid</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>892.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01545'</td>\n",
       "      <td>6204.0</td>\n",
       "      <td>9.5600</td>\n",
       "      <td>701.02992</td>\n",
       "      <td>99.59033</td>\n",
       "      <td>593.70643</td>\n",
       "      <td>281.8728</td>\n",
       "      <td>799.4792</td>\n",
       "      <td>1.063528</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01590'</td>\n",
       "      <td>5529.5</td>\n",
       "      <td>10.2100</td>\n",
       "      <td>689.24115</td>\n",
       "      <td>104.05397</td>\n",
       "      <td>576.36756</td>\n",
       "      <td>253.3904</td>\n",
       "      <td>813.7205</td>\n",
       "      <td>1.142136</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01620'</td>\n",
       "      <td>5638.3</td>\n",
       "      <td>6.3200</td>\n",
       "      <td>588.60634</td>\n",
       "      <td>109.83789</td>\n",
       "      <td>473.16833</td>\n",
       "      <td>302.2676</td>\n",
       "      <td>873.6742</td>\n",
       "      <td>1.277396</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>895.0</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'VR02755'</td>\n",
       "      <td>5465.0</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>651.50000</td>\n",
       "      <td>92.40000</td>\n",
       "      <td>539.40000</td>\n",
       "      <td>266.3000</td>\n",
       "      <td>690.8000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'VR02755'</td>\n",
       "      <td>5465.0</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>651.50000</td>\n",
       "      <td>92.40000</td>\n",
       "      <td>539.40000</td>\n",
       "      <td>266.3000</td>\n",
       "      <td>690.8000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'VR02755'</td>\n",
       "      <td>5465.0</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>651.50000</td>\n",
       "      <td>92.40000</td>\n",
       "      <td>539.40000</td>\n",
       "      <td>266.3000</td>\n",
       "      <td>690.8000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'VR02755'</td>\n",
       "      <td>5465.0</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>651.50000</td>\n",
       "      <td>92.40000</td>\n",
       "      <td>539.40000</td>\n",
       "      <td>266.3000</td>\n",
       "      <td>690.8000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'VR04913'</td>\n",
       "      <td>3375.0</td>\n",
       "      <td>0.0838</td>\n",
       "      <td>825.30000</td>\n",
       "      <td>16.90000</td>\n",
       "      <td>770.10000</td>\n",
       "      <td>145.0000</td>\n",
       "      <td>374.6000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1425.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1231 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      replicate         qid  t_fl_fea  t_ctpo_ec     t_ttar     t_ctar  \\\n",
       "0           1.0  b'CA01519'    4901.9     8.8300  960.47070   72.70221   \n",
       "1           1.0  b'CA01519'    4901.9     8.8300  960.47070   72.70221   \n",
       "2           1.0  b'CA01545'    6204.0     9.5600  701.02992   99.59033   \n",
       "3           1.0  b'CA01590'    5529.5    10.2100  689.24115  104.05397   \n",
       "4           1.0  b'CA01620'    5638.3     6.3200  588.60634  109.83789   \n",
       "...         ...         ...       ...        ...        ...        ...   \n",
       "1226        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1227        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1228        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1229        1.0  b'VR02755'    5465.0     0.1475  651.50000   92.40000   \n",
       "1230        1.0  b'VR04913'    3375.0     0.0838  825.30000   16.90000   \n",
       "\n",
       "         t_tbar   t_ttbmd   t_ctbmd    t_ctth  ...  age_at_menop  meno_status  \\\n",
       "0     879.26660  160.3830  805.1053  0.757581  ...          53.0          2.0   \n",
       "1     879.26660  160.3830  805.1053  0.757581  ...          53.0          2.0   \n",
       "2     593.70643  281.8728  799.4792  1.063528  ...          32.0          3.0   \n",
       "3     576.36756  253.3904  813.7205  1.142136  ...          32.0          3.0   \n",
       "4     473.16833  302.2676  873.6742  1.277396  ...          55.0          3.0   \n",
       "...         ...       ...       ...       ...  ...           ...          ...   \n",
       "1226  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1227  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1228  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1229  539.40000  266.3000  690.8000  0.920000  ...          63.0          3.0   \n",
       "1230  770.10000  145.0000  374.6000  0.150000  ...          54.0          2.0   \n",
       "\n",
       "      liver_ever  status  r_ttop  t_ttop  r_tthip  t_tthip  numberhits      id  \n",
       "0            0.0     0.0    23.0    23.0     23.0     23.0         2.0   891.0  \n",
       "1            0.0     0.0    23.0    23.0     23.0     23.0         2.0   892.0  \n",
       "2            0.0     0.0    34.0    34.0     34.0     34.0         1.0   893.0  \n",
       "3            0.0     0.0    34.0    34.0     34.0     34.0         1.0   894.0  \n",
       "4            0.0     0.0    29.0    29.0     29.0     29.0         3.0   895.0  \n",
       "...          ...     ...     ...     ...      ...      ...         ...     ...  \n",
       "1226         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1421.0  \n",
       "1227         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1422.0  \n",
       "1228         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1423.0  \n",
       "1229         0.0     2.0    19.0    19.0     19.0     19.0         4.0  1424.0  \n",
       "1230         0.0     2.0     6.0     6.0      6.0      6.0         1.0  1425.0  \n",
       "\n",
       "[1231 rows x 76 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert all columns to lowercase\n",
    "xtrtmp.columns = xtrtmp.columns.str.lower()\n",
    "xtrtmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "07de2356",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#vartokeep = ['id', 't_ttbmd', 't_ctbmd', 'r_ttbmd', 'r_ctbmd', 't_moart', 'r_moart', 'hicnum', 'centre', 'age', 'osteomed', 'newosteofx', 'prevfx_any']\n",
    "#Q1Subset = xtrtmp.loc[:, vartokeep]\n",
    "#Q1Subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "60887289",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'CA'    352\n",
       "b'TO'    292\n",
       "b'VR'    225\n",
       "b'HA'    200\n",
       "b'SK'    161\n",
       "Name: hicnum, dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#freq count for hicnum\n",
    "xtrtmp['hicnum'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b53e35fa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#freq count for centre\n",
    "xtrtmp['centre'].value_counts()\n",
    "\n",
    "centrelist = xtrtmp['centre']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "3295d2d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-161-7cb0ec2d3667>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if xtrtmp['centre'][i] == 1.0 : xtrtmp['sitename'][i] = 'Calgary'\n",
      "C:\\Users\\dave_\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1637: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n",
      "<ipython-input-161-7cb0ec2d3667>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  elif xtrtmp['centre'][i] == 2.0 : xtrtmp['sitename'][i] = 'Hamilton'\n",
      "<ipython-input-161-7cb0ec2d3667>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  elif xtrtmp['centre'][i] == 3.0 : xtrtmp['sitename'][i] = 'Saskatoon'\n",
      "<ipython-input-161-7cb0ec2d3667>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  elif xtrtmp['centre'][i] == 4.0 : xtrtmp['sitename'][i] = 'Toronto - Referent'\n",
      "<ipython-input-161-7cb0ec2d3667>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  elif xtrtmp['centre'][i] == 5.0 : xtrtmp['sitename'][i] = 'Vancouver'\n"
     ]
    }
   ],
   "source": [
    "#create new column 'sitename' and code to match centre value\n",
    "xtrtmp['sitename'] = np.NaN #create empty list \n",
    "for i in xtrtmp.index: #loop thru center values and recode accordigly\n",
    "    if xtrtmp['centre'][i] == 1.0 : xtrtmp['sitename'][i] = 'Calgary'\n",
    "    elif xtrtmp['centre'][i] == 2.0 : xtrtmp['sitename'][i] = 'Hamilton'\n",
    "    elif xtrtmp['centre'][i] == 3.0 : xtrtmp['sitename'][i] = 'Saskatoon'\n",
    "    elif xtrtmp['centre'][i] == 4.0 : xtrtmp['sitename'][i] = 'Toronto - Referent'\n",
    "    elif xtrtmp['centre'][i] == 5.0 : xtrtmp['sitename'][i] = 'Vancouver'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c44ab5f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Calgary               352\n",
       "Toronto - Referent    292\n",
       "Vancouver             225\n",
       "Hamilton              200\n",
       "Saskatoon             161\n",
       "Name: sitename, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify sitename value counts\n",
    "xtrtmp['sitename'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "9fff856f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    766\n",
       "1.0    464\n",
       "Name: prevfx_any, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get freq of prevfx_any\n",
    "xtrtmp['prevfx_any'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b4a54abd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Site')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot t_ttbmd by site\n",
    "import seaborn as s \n",
    "plt.figure(figsize=(12,6))\n",
    "s.boxplot(data=xtrtmp, x='t_ttbmd', y='sitename', orient='h') # orient=h is default, could also be v (vertical)\n",
    "plt.title(\"Total BMD at Distal Tibia by Site\")\n",
    "plt.xlabel('Total BMD at Distal Tibia')\n",
    "plt.ylabel('Site')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "9142ba95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ovgdPG+L1f6a4/8lQGnmfmyYinkLRbCcoEsPBw68Ot+xwNXuwuUnM4BHiRnMuP6VeJ+OIeBlw4Ajb71Svq3dzwSjufv62cvKswa8PY7znXKOeH/Xv6j6DYmhugB8O0+n8/5WPr2TzkLZnjaWTerndgyNiuxHKdLM5Yb1iFKsf8Rwtv4POLSffFxGPHSGWpo2+p6nHRERSM3ygfHwBxU0L9wGIiO0i4o1sTj5+SdG+vtY15ePOEfF66vs5xcVkAN+s/mOMiK6IeClFM6Nmt09vSETsEBGvoLgRHRS1Gp8bzToy86OZ+d7MbGaHUyJifkS8MSJ+TnGzOShuLjeWIZQvLB/fGhHHl/dJICIeHRGfobgAG64zePV9fm1ETMioZhGxU0QcGBFfohguei+KTs2vy8yG+4cAp0bENyPiNRGxqYNyRMyOiLezeVCBCwYtV93HF0RxV+16qsfxScDnqxdxETErIt5GkTw11Km+Az0IXBgRh1STrIg4gGIUst0p7o0x3HCwg433nGvUvcA5EfHasmlV9a7o51Pc+X4D8C9DLZyZKyg6jG/P5h9cxnIn9apPAn+MiE9ExPMjYmb1hYjYrRxI4edsTsI+PYp1N/J9C0UCtopi0ISfR8Sbo2b44ojYPSJeHRHfZusfl6TGZaZ//vnn34h/FO3hk/LGwnVefw9FtX+Wf3+haDJQnf4tsOcQy15cU+4+YGX59+6aMm+vKZMUFzVry+d3Ai+vea1n0PoPHC72Efa7p2a991K0n67+/Zniju7V1x8A/maI9awsyywa5faXlsstq/PagTXbvrsmrrsoLsBrj9eDFJ2kdxzj+78rRb+G6vo2lO9x9T3/Yk2sS+ss/8KasuspbgS5Elg5yjiqx3Ftzf7eUR77HPT3C+DJDRzbpUPMrz3X/jJo3v9S3IW7drlHlOdEtcydNefys2vKnTloXX8pj0lS1Ka9q3y+1bFh8+dwq/NhDO/pcTUx9IxQtnrcj6vzWnUdbwZuY/Nn4f5B59/hQ6y7WubAZp5zoziX3gNcXxPnPTXb3Ai8tYF1/X3NMleP83351aDzY2MZ0+BzfB1wwnDfC0Osf8Tv27Lc0ylGN6uN4+5B72sCF433XPRv6v5ZIyKpKTLzMxT3CPkfihGGdqS4WPw1xShAz8qh70T9Wopf7P+Pog/D3uXfrjXr/yJFx+NlFG2jp1MMrdlP0XTj6ibvUj2Db2i4O0VH1RUUv2I+KTNH0/SkmR5RE9csigu231L0OXkHsEdmvjsz14xl5Vk0M3kuRTKzkuKicD3F+3F0Zr59hOV/RvH+XUyR0M1l8/s8FjPYvL+7UVyU/ZHiV+yPAftn5vMy85qhVzGkj1E09fsOxQXqemA2RZJxEcUF94FZ3hW9KjP/QpFwfYPi3NyFzfs4o6boG4F3U7w/6yg6r19Ncdfx57G57f9k80eKi9fPUyRh21McszOBp2fm+cMsu5XxnnOj8BeK0cs+AdwM7EBxwf194HmZ+Z8NrONsNtfKjreT+kHAEcB/UCS8d1AM6bwdxY8MvwT+FXhCZo6q9rU04vctFPcyoRia+F0Un9u7KGphplHccf7rFH1IXo00RpGZI5eSJElSXRHxGopkZC1Fze897Y1ImhysEZEkSRqfheXjmSYhUuNMRCRJksYoIo6nGLp8I0VzKkkNmt7uACRJkiaTiHg2RV+gXdjct+ILmXlt24KSJiETEUmSpNGZQdHBewPFyFJLgVPaGZA0GdlZXZIkSVLLWSMyRe2+++7Z09PT7jAkSZK0jVuxYsVdmfnIwfNNRKaonp4eli9f3u4wJEmStI2LiJvqzXfULEmSJEktZyIiSZIkqeVMRCRJkiS1nH1EJEnaBvT391OpVNodxqgMDAwA0N3d3eZIttTb28vChQtHLig1wcMPP8ytt97Kgw8+2O5Qxm3GjBnMmzeP7bbbrqHyJiKSJG0DKpUKV11zHRt23K3doTSsa829ANy+rnMuR7rW3N3uEDTF3Hrrrey000709PQQEe0OZ8wyk1WrVnHrrbeyzz77NLRM53zyJUnSuGzYcTfWPv6wdofRsJnXXwDQUTFXY5Ja5cEHH5z0SQhARDBnzhzuvPPOhpexj4gkSZLURpM9Caka7X6YiEiSJElT2IUXXsjjHvc4ent7+cQnPrHV65nJCSecQG9vL0996lO54oormrJdm2ZJkiRJHeJd//g+/nxX8/oqPWr33Tj1P/59yNc3bNjAO9/5Ti666CLmzZvHAQccwJFHHskTn/jETWV+8IMfcMMNN3DDDTdw6aWX8o53vINLL7103LGZiEiSJEkd4s933c0f5r6oeSu846fDvnzZZZfR29vLvvvuC8BRRx3Fueeeu0Uicu6553LMMccQETz72c/mnnvu4bbbbmOPPfYYV2g2zZIkbdLf309/f3+7w5Ckrfj9NDEGBgbYa6+9Nk3Pmzdv09DaoykzFtaISJI2mWz3oZA0dfj9NDEyc6t5gzudN1JmLKwRkSRJkqaoefPmccstt2yavvXWW9lzzz1HXWYsTEQkTXmrVq3ihBNOYNWqVS1ZbqxavT1JUnNUKhUOP/zwjqzVOeCAA7jhhhu48cYbeeihh/jGN77BkUceuUWZI488kjPOOIPM5Ne//jW77LLLuPuHgImIJHH66adz9dVXc8YZZ7RkubFq9fYkSc2xePFiHnjgARYvXtzuULYyffp0Tj31VF760pfyhCc8gde//vU86UlP4otf/CJf/OIXATjssMPYd9996e3t5a1vfStf+MIXmrPtpqxFkiapVatWceGFF5KZXHjhhRxzzDHMmTNnwpZrdZySpPaqVCqsXLkSgJUrV1KpVOjt7R2y/KN2323Eka5G41G77zZimcMOO4zDDjtsi3lvf/vbNz2PCD7/+c83LaYqExFJU9rpp5/Oxo0bgWIs9TPOOIP3vOc9E7Zcq+McrYGBAdauXUtfX1/T162JValUmPbQ1h1KNTrTHryPSuV+PwMdqFKpMHPmzHaHMWqDa0EWL17M0qVLhyw/3D0/tjU2zZI0pV188cWsX78egPXr13PRRRdN6HJj1ertSZKao1obMtT0VGaNiKQp7ZBDDuGCCy5g/fr1TJ8+nZe85CUTulyr4xyt7u5uAJYsWTIh69fE6evrY8Uf72h3GJPexhk707vvXD8DHWiy1lL19PRskXz09PS0LZZOY42IpCnt2GOPZdq04quwq6uLY445ZkKXG6tWb0+S1BwnnnjisNNTmYmIpCltzpw5HHrooUQEhx56aMMdwMe6XKvjlCS1V29v76ZakJ6enmE7qk81JiKSprxjjz2WpzzlKaOuZRjrcmPV6u1JkprjxBNPZNasWdaGDGIiImnKmzNnDp/73OdGXcsw1uXGqtXbkyQ1R29vL+eff37H1oa8+c1v5lGPehRPfvKT676emZxwwgn09vby1Kc+lSuuuKIp27WzuiRJktQhPvxP7+Leu5o38MQuu8/llE+fOmyZ4447jne9611D1rj/4Ac/4IYbbuCGG27g0ksv5R3veAeXXnrpuGMzEZEkbdKpv9ZJ0lT5frr3rjv4wGOub9r6/u0PI5d54QtfOOywwueeey7HHHMMEcGzn/1s7rnnHm677Tb22GOPccVmIiJJ2mThwoXtDkGS6vL7qX0GBgbYa6+9Nk3PmzePgYGBcSci9hGRJEmSNKTM3GpeRIx7vSYiTRIRj46Ib0TEHyLidxFxQUQ8dpjyq1sZnyRJkjQW8+bN45Zbbtk0feutt7LnnnuOe70mIk0QRUr4HWBZZj4mM58IfBiY24Jt27xOkiRJE+bII4/kjDPOIDP59a9/zS677DLuZllgH5FmOQh4ODO/WJ2RmVdFxOyI+DHwCGA74MTMPLd2wYiYBpwKvAi4kSI5/HJmnh0R/wK8HJgJ/BJ4W2ZmRCwrp58HXBIRxwGPzcyHI2Jn4LfAfpn58ITutSSpo3StuZuZ11/Q7jAa1rVmFUBHxdy15m5a8Dui1FGOPvpoli1bxl133cW8efM4+eSTefjh4jLy7W9/O4cddhgXXHABvb297LjjjnzlK19pynZNRJrjycCKOvMfBF6VmfdFxO7AryPie7llQ7tXAz3AU4BHAdcBXy5fOzUzPwoQEV8FjgC+X762a2a+qHytBzgc+C5wFHBOvSQkIo4HjgeYP3/+WPdVkrY5/f39VCqVdocxZgMDA6xevZrZM7aDjfe3O5yGrYvi3+HsQTHPnDmT7u7udoQEzJ0yozOpM+2y+9yGRroazfpGcuaZZw77ekTw+c9/vlkhbWIiMrECOCUiXghsBLopfma5vabM84FvZeZG4PaI+EnNawdFxPuBHYHdgGvZnIicVVPuv4D3UyQibwLeWi+YzDwNOA1gwYIFW/c6kqQpqlKpcMO1VzJ/9oZ2hzImD9zfRQB7zZhk8W9XfbJ206ybV3fR3dvLkiVL2hKS1G4j3fNjW2Ii0hzXAq+tM/+NwCOBZ5bNplYCMwaVqTvkQETMAL4ALMjMWyJi0aBlH6g+ycxfRERPRLwI6MrMa8a8J5I0Rc2fvYEPP+O+docxJqdcsTPApI2/VnVfJG377KzeHJcAO0TEppqIiDgA2Bv4c5mEHFROD/Zz4DURMS0i5gIHlvOrScddETGb+olOrTOAM4HmNNqTJEmSJpCJSBOUfT5eBbykHL73WmARcAGwICKWU9SO1LtN5jnArcA1wJeAS4F7M/Me4D+BqymaXF0+Qhhfo+gUP3wjP0mSJHWUevfpmIxGux82zWqSzPwT8Po6Lz1niPKzy8eNEfHezFwdEXOAyyiSDzLzRODEOsseWGeVzwfOLhMYSZIkTQIzZsxg1apVzJkzpyk3CWyXzGTVqlXMmDG4F8LQTEQ6w3kRsSuwPfCxzLx9hPJbiIh+4GXAYRMQm6Rx6u/vB2DhwoVtjkSSRs/vsIk1b948br31Vu688852hzJuM2bMYN68eQ2XNxHpAEPUcIxmeb8ZpA42mYeFlSS/wybWdtttxz777NPuMNrCPiKSJEmSWs5ERJIkSVLLmYhIkiRJajkTEUmSJEktZ2d1SZpgAwMDrF27lr6+vnaHoiFUKhW2f9jf5jrBHWum8VCl4uelg1QqFWbOnNnuMLQN8ltXkiRJUstZIyJJE6y7uxuAJUuWtDkSDaWvr491Ny1vdxgC5u64kR327vXz0kGsndJEsUZEkiRJUsuZiEiSJElqORMRSZIkSS1nIiJJkiSp5UxEJEmSJLWco2ZJ0gTr7e1tdwiSNGZ+h2mimIhI0gRbuHBhu0OQpDHzO0wTxaZZkiRJklrORESSJElSy5mISJIkSWo5+4hIkgTcvLqLU67Yud1hjMlN93cBTNr4a928uov92h2EpJYwEZEkTXmTfVSgWQMDAOzQ3d3mSMZvPyb/+yGpMSYikqQpz1GBJKn17CMiSZIkqeVMRCRJkiS1nImIJEmSpJYzEZEkSZLUciYikiRJklrORESSJElSy5mISJIkSWo5ExFJkiRJLWciIkmSJKnlTEQkSZIktZyJiCRJkqSWm97uACRJ2tb09/dTqVTaHcaEGBgYAKC7u7vNkQyvt7eXhQsXtjsMScMwEZEkqckqlQpXXXMdG3bcrd2hNF3XmnsBuH1d515CdK25u90hSGpA536LSJI0iW3YcTfWPv6wdofRdDOvvwCgo/etGqOkzmYfEUmSJEktZyIiSZIkqeVMRCRJkiS1nImIJI1Bf38//f397Q5DkqYsv4cnPzurS9IYbKtDs0rSZOH38ORnjYgkSZKkljMRkSRJktRyJiKSJEmSWs5ERJIkSVLL2VldksZgYGCAtWvX0tfX1+5Q1IEqlQrTHsp2hzFlTXvwPiqV+/18buMqlQozZ85sdxgaB2tEJEmSJLWcNSKSNAbd3d0ALFmypM2RqBP19fWx4o93tDuMKWvjjJ3p3Xeun89tnDVek581IpIkSZJazkREkiRJUsuZiEiSJElqORMRSZIkSS1nIiJJkiSp5Rw1S5LGoLe3t90hSNKU5vfw5GciIkljsHDhwnaHIElTmt/Dk59NsyRJkiS1nInIKETE6kHTx0XEqU1a939FxBPL5ysjYveI2DUi/qEZ65ckSZI6iYlIh8jMt2Tm7wbN3hUwEZEkSdI2xz4iTRIRLwdOBLYHVgFvzMw7ImIRsA+wB/BY4B+BZwMvAwaAl2fmwxGxDHhvZi6vWe0ngMdExFXARcD7gU+WyyawODPPiogDgUXAXcCTgRXA32ZmTtweS5KG07XmbmZef0G7w2i6rjWrADp637rW3A3MbXcYkkZgIjI6M8ukoGo34Hvl858Dz87MjIi3UCQN/1S+9hjgIOCJwK+A12Tm+yPiO8DhwHeH2N4HgSdn5v4AEfEaYH/gacDuwOUR8bOy7NOBJwF/An4BPK+MaZOIOB44HmD+/Pmj23NJapH+/n4qlUq7wxizgYEBVq9ezewZ28HG+9sdzlZmzpxJd3f3mJcfGFgPQHd3J1/oz3VEJWkSMBEZnbXVpACKPiLAgnJyHnBWROxBUStyY81yPyhrPa4GuoALy/lXAz2j2P7zgTMzcwNwR0T8FDgAuA+4LDNvLeO6qlzvFolIZp4GnAawYMECa0skdaRKpcIN117J/Nkb2h3KmDxwfxcB7DWj8+K/eXUX3b29LFmypN2hSJKJSBP1A/+Rmd+raSpVtQ4gMzdGxMM1TaY2Mrr3IIZ5bV3N8w2jXK8kdZT5szfw4Wfc1+4wxuSUK3YG6Mj4q7FJUiews3rz7ELR5wPg2Cat835gp5rpnwF/ExFdEfFI4IXAZU3aliRJktQyJiLNswj4VkT8L0Wn8XHLzFXALyLimoj4d+A7wG+B3wCXAO/PzNubsS1JkiSplWy+MwqZOXvQ9FJgafn8XODcOsssGmodta9l5oE1z3tqnr9h0CrfV/7VrnMZsKxm+l3D7ogkSZLUZtaISNIQ+vv76e/vb3cYkprAz7PUeawRkaQhTOYhZCVtyc+z1HmsEZEkSZLUciYikiRJklrOREQtU6lUOPzww60e38asWrWKE044gVWrVrU7FEnSJLJ8+XIOPvhgVqxY0e5Q1CYmImqZxYsX88ADD7B48eJ2h6ImOv3007n66qs544wz2h2KJGkSWbRoERs3buSkk05qdyhqExMRtUSlUmHlypUArFy50lqRbcSqVau48MILyUwuvPBCa0UkSQ1Zvnw5q1evBmD16tXWikxRjpqllhhcC7J48WKWLl3anmDUNKeffjobN24EYMOGDZxxxhm85z3vaXNUzTMwMMDatWvp6+trdyhTSqVSYfuH/Z1sItyxZhoPVSpT8pyuVCrMnDmz3WGotGjRoi2mTzrpJM4777z2BKO28ZteLVGtDRlqWpPTxRdfzPr16wFYv349F110UZsjkiRNBtXakKGmNTVYI6KW6Onp2SL56OnpaVssap5DDjmECy64gPXr1zN9+nRe8pKXtDukpuru7gZgyZIlbY5kaunr62PdTcvbHcY2ae6OG9lh794peU5PxVqgTjZ79uwtko/Zs2e3MRq1izUiaokTTzxx2GlNTsceeyzTphVfI11dXRxzzDFtjkiSNBkMbpp18skntycQtZWJiFqit7d3Uy1IT08Pvb297Q1ITTFnzhwOPfRQIoJDDz2UOXPmtDskSdIksGDBgk21ILNnz+aZz3xmmyNSO5iIqGVOPPFEZs2aZW3INubYY4/lKU95irUhkqRRWbRoEdOmTbM2ZAqzj4hapre3l/PPP7/dYajJ5syZw+c+97l2hyFJmmQWLFjAJZdc0u4w1EYmIpI0BJsQStsOP89S5zERkaQhLFy4sN0hSGoSP89S57GPiCRJkqSWMxGRJEmS1HImIpIkSZJazj4ikqSOc/PqLk65Yud2hzEmN93fBdCR8d+8uov92h2EJJVMRCRJHWWyj240a2AAgB26u9scydb2Y/IfX0nbDhMRSVJHcXQjSZoa7CMiSZIkqeVMRCRJkiS1nImIJEmSpJYzEZEkSZLUciYikiRJklrORESSJElSy5mISJIkSWo5ExFJkiRJLWciIkmSJKnlTEQkSZIktZyJiCRJkqSWm97uACRJ0pb6+/upVCrtDmNcBgYGAOju7m7J9np7e1m4cGFLtiWpOUxEJEnqMJVKhauuuY4NO+7W7lDGrGvNvQDcvm7iLzW61tw94duQ1HwmIpIkdaANO+7G2scf1u4wxmzm9RcAtGQfqtuSNLnYR0SSJElSy5mISJIkSWo5ExFJkiRJLWciIkna5vT399Pf39/uMKRJwc+L2sXO6pKkbc5kH/pWaiU/L2oXa0QkSZIktZyJiCRJkqSWMxGRJEmSJrnly5dz8MEHs2LFiq1eu+SSSzjwwAP5yU9+0obIhmYiIkmSJE1yixYtYuPGjZx00klbvXbKKacA8K//+q+tDmtY40pEIuLxEfGqiPi7ZgUkSZIkqXHLly9n9erVAKxevXqLWpFLLrmE9evXA7B+/fqOqhWJzBz9QhH7A/8FPL06LzO7ytdeBPwA+JvM/H5zwlSzLViwIJcvX97uMCRpQrz2ta9l7dq19Pb2tjuUMalUKtz/UPLA/ke1O5Qxm3n9BQCsffxhE76tWVd9g522j0n7frdbpVJh5syZnH322e0ORWN0xBFHbEpEAGbPns15550HwCGHHLIpEQGYPn06F198cUvji4gVmblg8PxR14hExGOBZcDjgCUUSUetnwF3A68dfZiSJEmSRqM2CRk8XZuE1Jtup7HcR+QkYHvgmZl5XUScBLys+mJmZkT8CjigSTFKkjQq3d3dACxZsqTNkYxNX18fK/54R7vDmDQ2ztiZ3n3nTtr3u936+vraHYLGafbs2VvViFRNnz59qxqRTjGWPiIvBr6dmdcNU+ZmYM+xhSRJkiSpUYsWLdpi+uSTT970/MMf/vAWr33kIx9pRUgNGUsisitwawPr3X4M65YkSZI0CgsWLNhUCzJ79mye+cxnbnrt4IMP3lQLMn36dA466KC2xFjPWBKRPwMj9QZ7EnDLGNYtSZIkaZQWLVrEtGnTtqgNqarWinRSbQiMrY/IJcDREfG4zPz94Bcj4gCK5lufH29wkiRJkka2YMECLrnkkrqvHXzwwRx88MEtjmhkY0lEPg68DvhZRCyi7AsSEU8CXkjRmf1+4FNNilGSpFFxGFepcX5e1C6jTkQy8/cR8RrgTODUcnYAvy0f7wFenZk3NytISZJGY+HChe0OQZo0/LyoXcY0fldmXhgR+wDHAs8G5gD3Ar8GvpKZdzcvREmSJEnbmjEPJJyZ91Dc0HCbG7Q7Ij4CvAHYAGwE3paZl45i+UXA6sxsqHlaRBwH/Cgz/zT6aCVJkqTJZyx3Vv9yRBw5QpkjIuLLYw+rfSLiOcARwDMy86nAIUz8CGDH4X1XJEmSNIWMpUbkOGAl8L1hyjyNotnWm8ew/nbbA7grM9cBZOZdABHxL8DLgZnALylqSTIiTgDeDqwHfpeZR9WuLCLeCry6/Hvf4HUArwEWAF+LiLXAc4DnUnT2nw5cDrwjM9dFxIuHmL8SOL1c93bA6zLz+gk4NpKkFulaczczr7+g3WGMWdeaVQAt2YeuNXcDcyd8O5KaKzJzdAtEbAQWZeZHhynzUeCDmTnpbmoYEbOBnwM7AhcDZ2XmTyNit2rfl4j4KvDNzPx+RPwJ2KdMCHbNzHuqTbOAB4G/pkgM1g2zjmXAezNzeUTMAG4AXpyZ/xcRZwBXAF+sNz8zP1smIp/OzP6I+AeK2py3DLefCxYsyOXLlzfz0ElSR+vv76dSqbQ7jIYMDAywdu3adocxonXr1gGwww47jOq1iTBz5ky6u7tHLNfb22vnbKnFImJFZi4YPH+sfUSGzF4iYgeKYXxvH+O62yozV0fEM4EXAAcBZ0XEB4H7I+L9FAnKbsC1wPcpRgv7WkR8F/huzar+juIO9K/MzIfLeQcNsY5ajwNuzMz/K6dPB94J/GSI+Z8tp79dPq6gqH3ZSkQcDxwPMH/+/AaOhiRtOyqVCjdceyXzZ29odygj2h2K+u0Od9ODXQDstV2dpGlT/C1KqNbfw7qbbhu2yM2ru1oTi6SGNJSIRMQfB816T0S8qU7RLuCRwA4Uv+BPSpm5AVgGLIuIqymaUD0VWJCZt5Q1HjPK4odTJF5HAv9c3k8F4Bpgf2AecGNZ0/GFIdZRK4YIa6j5VevKxw0M8b5m5mnAaVDUiIywPkna5syfvYEPP+O+doexzTjlip0BJs0xrcYrqTM02ll9GsWFcFDUhsQQfw8DVwP/RtEfYtKJiMdFxH41s/YHqneQv6tsuvXasuw0YK/M/AnwfmBXYHZZ9kqKBOZ7EbEnm5OOLdZRuh/YqXx+PdATEdW7C/0d8NNh5kuSJEmTTkM1IpnZU31e9hH5zHB9RCa52UB/ROxK0QG9QtGc6R6KJGslRUdxKGqA/icidqFIxD5T9hEBIDN/HhHvBc4HXgL8Z511ACwFvljTWf1NwLciotop/YtlH5Ot5jd/9yVJkqSJN5Y+IgdRXEhvkzJzBcWoVYOdWP4N9vw661hU8/yHwA+HW0dmngOcUzPrx8DT65Qban5PzfPlwIF14pQkSZI6xqgTkcy0OZAkTZD+/n4AR/WRpDHye3TyGDERiYhjyqffycz7a6ZHlJlnjDkySZqCJsvwspLUqfwenTwaqRFZStFB/dcUnaqr08Opdmo3EZEkSZK0lUYSkTdTJBXVwbnrDdsrSZIkSQ0bMRHJzKWDpk+fsGgkSZIkTQljvbP6FiLiSOBgiiZZP83Mb4+wiCRJkqQprKEbGkbEyyPiZxHxojqvLQW+A5wALKS4z8U5g8tJkiRJUlWjNSJHAs8ALq2dGRFHAMcADwCfoejMfjzwyog4OjPPbGKskrTNGxgYYO3atfT19bU7lG1OpVJh+4cb+v1N26g71kzjoUrFz9c2rlKpMHPmzHaHoQY0mog8C/hVZj44aH61I/ubMvNsgIj4KvAH4I2AiYgkSZKkrTSaiDwa+FWd+S8E7qHmruCZeXtEnA88b9zRSdIU093dDcCSJUvaHMm2p6+vj3U3LW93GGqjuTtuZIe9e/18beOs8Zo8Gq2jfgRwd+2MiJgP7Ab8PDMH31fkRmDO+MOTJEmStC1qNBG5H5g3aN4zy8crh1hmcDMuSZIkSQIaT0SuBg6PiNk1815F0T/k53XK78PmGyBKkiRJ0hYaTUS+RtE866cRcUJEnErRGf124Ce1BSMigOcDv2tmoJIkSZK2HY12Vv9v4NXAS4H9KW5c+DDQl5kbBpV9MUXn9oubFKMkTRm9vb3tDkGSJjW/RyePhhKRzNwYEYcDRwPPBVYB387Mq+oU3x1YAnyvWUFK0lSxcOHCdocgSZOa36OTR6M1ImTmRoomWl8bodw3gG+MMy5JkiRJ2zBvMStJkiSp5UxEJEmSJLWciYgkSZKklmu4j4gkSZPdzau7OOWKndsdxjbjpvu7ACbNMb15dRf7tTsISZuYiEiSpgSH9Gy+WQMDAOzQ3d3mSBqzH54HUicxEZEkTQkO6SlJncU+IpIkSZJazkREkiRJUsuZiEiSJElqORMRSZIkSS1nIiJJkiSp5UxEJEmSJLWciYgkSZKkljMRkSRJktRyJiKSJEmSWs5ERJIkSVLLTW93AJIkaXz6+/upVCrtDmPcBgYGAOju7m5zJI3r7e1l4cKF7Q5DmpRMRCRJmuQqlQpXXXMdG3bcrd2hjEvXmnsBuH3d5Lg86Vpzd7tDkCa1yfFJlyRJw9qw426sffxh7Q5jXGZefwHApNmParySxsY+IpIkSZJazkREkiRJUsuZiEiSJElqORMRSZIkSS1nIiJJU1B/fz/9/f3tDkOSWsrvvs7iqFmSNAVtC/eckKTR8ruvs1gjIkmSJKnlTEQkSZKkDlepVDj88MO3qVodExFJkiSpwy1evJgHHniAxYsXtzuUpjERkSRJkjpYpVJh5cqVAKxcuXKbqRWxs7okTUEDAwOsXbuWvr6+doeiJqhUKkx7KNsdxpQz7cH7qFTu93M0iVQqFWbOnNnuMEZtcC3I4sWLWbp0aXuCaSJrRCRJkqQOVq0NGWp6srJGRJKmoO7ubgCWLFnS5kjUDH19faz44x3tDmPK2ThjZ3r3nevnaBKZrLVXPT09WyQfPT09bYulmawRkSRJkjrYiSeeOOz0ZGUiIkmSJHWw3t7eTbUgPT099Pb2tjegJjERkSRJkjrciSeeyKxZs7aZ2hCwj4gkSZLU8Xp7ezn//PPbHUZTmYhI0hS0rVTrS9Jo+N3XWUxEJGkKWrhwYbtDkKSW87uvs9hHRJIkSVLLmYhIkiRJarm2JSIRMScirir/bo+IgZrp7Zu8rVdGxBObuL5lEfH7iPhNRFweEfs3sMyZEfHbiHhPs+Kos42eiHjDRK1fkiRJapa29RHJzFXA/gARsQhYnZmfGmm5iOjKzA2j3NwrgfOA341yueG8MTOXR8SbgH8HXjJUwYh4NPDczNy70ZVHxPTMXD/KmHqANwBfH+VykiRJUkt1VGf1iHgx8CmKuC4H3pGZ6yJiJfBl4K+BUyMigA8DAZyfmR8ol18NLAGOANYCrwAeAxwJvCgiTgReA+wEfBHYEfgD8ObM/MsYw/4V8L5y+7OAfuAp5T4sysxzgR8Bj4qIq4CFwJ+AzwOPBNYAb83M6yNiKXA38HTgioj4wjDl7gMWAI8G3p+ZZwOfAJ5Qbuf0zPzMGPdJkjSE/v5+KpVKu8PYQqVSoeuBNcy+4qubZ24sf7Ob1tWeoBqU07Zj44ydAehaswqAmddf0M6QGta15m5gbrvDkCatTkpEZgBLgRdn5v9FxBnAO4DPlq8/mJnPj4g9gV8DzwT+AvwoIl6Zmd8FZgG/zsyPRMQnKS7cF0fE94Dzyot1IuK3wMLM/GlEfBQ4CXj3GOM+FPhu+fwjwCWZ+eaI2BW4LCIupkiEzsvM/cvt/xh4e2beEBF/BXwBOLhcx2OBQzJzwwjl9gCeDzwe+B5wNvBB4L2ZeUS9QCPieOB4gPnz549xdyVpaqtUKtxw7ZXMnz3ayvmJs9d2wK4AD22ad9P9RQKy907r2hFSQ25e3UVsvz29+xYX8wMDRUOA7u7JcnE/1+FgpXHopESkC7gxM/+vnD4deCebE5GzyscDgGWZeSdARHwNeCFFMvAQRRMsgBXUaS4VEbsAu2bmT2u2860xxPu1sgakC3hGOe+vgSMj4r3l9AxgPkXtTHX7s4HnAt8qKnYA2KFmvd8qk5CRyn03MzcCv4uIhr6xM/M04DSABQsWZEN7KUnayvzZG/jwM+5rdxjDOuWKopahk+M85Yqd2WHvXpYsWdLuUCS1QSclIg80+HoMU+bhzKxeYG9gjPsXEV0UiQzA9zLzX+oUeyPwG4rmUJ8HXl3G9prM/P2g9fXUTE4D7qnWjtTxQIPlan/iGu6YSJIkSR2nk4bvnQH0RES1jvPvgJ/WKXcpRX+P3cuE4eghytW6n6JfCJl5L/CXiHjBUNvJzA2ZuX/5Vy8JqZZ7GDgReHZEPAH4IbCw7MNCRDy9zjL3ATdGxOvKMhERTxtruaH2U5IkSepknZSIPAi8iaIp0tXARooO5VvIzNuADwE/oaiRuKLsED6cbwDvi4grI+IxwLHAv5d9RfYHPjrWoDNzLfBp4L3Ax4DtgN9GxDXldD1vBP4+In4DXEvRqX485ap+C6wvhxWesGGCJUmSpPHqiKZZmbmoZrJeLULPoOmvU2eI2sycXfP8bIoO3GTmL4DB9xF59jjiPXDQ9KdrJt9Wp/xK4Mk10zdSdHIfXO64QdONlptdPj4MvHjEHZAkSZLarJNqRCRpSunv76e/v7/dYUia4vwuUrt0RI2IJE1FnXYvCklTk99FahdrRCRJkiS1nImIJEmSpJYzEZEkSZLUciYikiRJklrOzuqS1CYDAwOsXbuWvr6+doeiUahUKmz/sL/jNcMda6bxUKXiZ6DNKpUKM2fObHcYmoL8JpUkSZLUctaISFKbdHd3A7BkyZI2R6LR6OvrY91Ny9sdxjZh7o4b2WHvXj8DbWaNlNrFGhFJkiRJLWciIkmSJKnlTEQkSZIktZyJiCRJkqSWs7O6JLVJb29vu0OQJL+L1DYmIpLUJgsXLmx3CJLkd5HaxqZZkiRJklrORESSJElSy5mISJIkSWo5ExFJkiRJLWdndUmSRunm1V2ccsXO7Q5jWDfd3wXQ0XHevLqL/dodhKS2MRGRJGkUJstQp7MGBgDYobu7zZEMbT8mz/GU1HwmIpIkjYJDnUpSc9hHRJIkSVLLmYhIkiRJajkTEUmSJEktZyIiSZIkqeVMRCRJkiS1nImIJEmSpJYzEZEkSZLUciYikiRJklrORESSJElSy5mISJIkSWq56e0OQJKkya6/v59KpdLuMMZtYGAAgO7u7jZH0rje3l4WLlzY7jAkjYGJiCRJ41SpVLjqmuvYsONu7Q5lXLrW3AvA7esmx+VB15q72x2CpHGYHN80kiR1uA077sbaxx/W7jDGZeb1FwBMmv2oxitpcrKPiCRJkqSWMxGRJEmS1HImIpIkSZJazkREkiRJUsuZiEhSB+jv76e/v7/dYUjShPP7TlWOmiVJHWBbuAeFJDXC7ztVWSMiSZIkqeVMRCRJkiS1nImIJEmSpJYzEZEkSZLUcnZWl6QOMDAwwNq1a+nr62t3KBqDSqXCtIey3WFMOdMevI9K5X4/N5NMpVJh5syZ7Q5DHcAaEUmSJEktZ42IJHWA7u5uAJYsWdLmSDQWfX19rPjjHe0OY8rZOGNneved6+dmkrEGS1XWiEiSJElqORMRSZIkSS1nIiJJkiSp5UxEJEmSJLWciYgkSZKklnPULEnqAL29ve0OQZJawu87VZmISFIHWLhwYbtDkKSW8PtOVTbNkiRJktRyJiKliFgWES8dNO/dEfGFdsUkSZIkbatMRDY7Ezhq0LyjyvmTSkTY5E6SJEkdzQvWzc4GFkfEDpm5LiJ6gD2BN0TEZ4CZwNmZeRJARKwETgdeDmwHvC4zr4+I2UA/sABI4OTMPCcijgY+DARwfmZ+oFzP6sycXT5/LXAE0Af8Btg3MzdGxI7A74F9gfnA54FHAmuAt5bbXQrcDTwduAL4p4k7VJI0Pv39/VQqlXaH0TSVSoWuBx5g9vKlMK2r3eGM3Yb1AMy+4qujXjSnbcfGGTs3O6Jhda25G5jb0m1Kah4TkVJmroqIy4BDgXMpakPOAj6emXdHRBfw44h4amb+tlzsrsx8RkT8A/Be4C3APwP3ZuZTACLiERGxJ/BvwDOBvwA/iohXZuZ3h4jl3oj4DfAi4CcUyc4PM/PhiDgNeHtm3hARfwV8ATi4XPSxwCGZuaHeeiPieOB4gPnz54/1UEnSuFUqFW649krmz677dTXp7LUd3NTVBWxk753WtTucMbtjTdFQYu6OD41quZtXdxHbb0/vvq1OCuY6ApM0iZmIbKnaPKuaiLwZeH15AT8d2AN4IlBNRL5dPq4AXl0+P4SaJl6Z+ZeIeCGwLDPvBIiIrwEvBL47TCxnAX9DkYgcBXyhrG15LvCtiKiW26FmmW8NlYSUsZwGnAawYMGCHGbbkjTh5s/ewIefcV+7w2iaU64oagO2pX1q1ClX7MwOe/eyZMmSdociaRIxEdnSd4H/iIhnUDTF+gtFTccBZUKxFJhRU776s9cGNh/LoGiSVSsYWm3Z2nV/D/h4ROxGUZNyCTALuCcz9x9iXQ8Msx1JkiSpY9hZvUZmrgaWAV+mqB3ZmeLi/t6ImAu8rIHV/Ah4V3UiIh4BXAq8KCJ2L5t4HQ38tCxyR0Q8ISKmAa8aFMtlwBLgvMzckJn3ATdGxOvKdUdEPG08+yxJkiS1g4nI1s4EngZ8IzN/A1wJXEuRnPyigeUXA4+IiGvKfh4HZeZtwIcomln9BrgiM88ty38QOI+ixuO2Qes6C/jb8rHqjcDfl+u+FnjF6HdRkiRJai+bZg2Smd+hpilVZh43RLmemufLgQPL56uBY+uU/zrw9Trzz6YYsaveNs5mULOuzLyRokP94LJ145QkSZI6kTUiktQE/f399Pf3tzsMSR3E7wVpeNaISFITbEv3xJDUHH4vSMOzRkSSJElSy5mISJIkSWo5ExFJkiRJLWciIkmSJKnl7KwuSU0wMDDA2rVr6evra3cok0KlUmH7h/0tbFtxx5ppPFSpeP4PUqlUmDlzZrvDkDqW/wUkSZIktZw1IpLUBN3d3QAsWbKkzZFMDn19fay7aXm7w1CTzN1xIzvs3ev5P4g1RNLwrBGRJEmS1HImIpIkSZJazkREkiRJUsuZiEiSJElqORMRSZIkSS3nqFmS1AS9vb3tDkFSh/F7QRqeiYgkNcHChQvbHYKkDuP3gjQ8m2ZJkiRJajkTEUmSJEktZyIiSZIkqeXsIyJJaoubV3dxyhU7tzuMprnp/i6AbWqfGnXz6i72a3cQkiYdExFJUstti6MJzRoYAGCH7u42R9J6+7FtvqeSJpaJiCSp5RxNSJJkHxFJkiRJLWciIkmSJKnlTEQkSZIktZyJiCRJkqSWMxGRJEmS1HKRme2OQW0QEXcCN41ysd2BuyYgnG2Rx6pxHqvGeJwa57FqnMeqMR6nxnmsGjeVjtXemfnIwTNNRNSwiFiemQvaHcdk4LFqnMeqMR6nxnmsGuexaozHqXEeq8Z5rGyaJUmSJKkNTEQkSZIktZyJiEbjtHYHMIl4rBrnsWqMx6lxHqvGeawa43FqnMeqcVP+WNlHRJIkSVLLWSMiSZIkqeVMRDSiiDg0In4fEZWI+GC74+k0EbEyIq6OiKsiYnk5b7eIuCgibigfH9HuONshIr4cEX+OiGtq5g15bCLiQ+V59vuIeGl7om6PIY7VoogYKM+tqyLisJrXpuSxioi9IuInEXFdRFwbEX3lfM+rQYY5Vp5Xg0TEjIi4LCJ+Ux6rk8v5nlc1hjlOnlNDiIiuiLgyIs4rpz2natg0S8OKiC7g/4CXALcClwNHZ+bv2hpYB4mIlcCCzLyrZt4ngbsz8xNl8vaIzPxAu2Jsl4h4IbAaOCMzn1zOq3tsIuKJwJnAs4A9gYuBx2bmhjaF31JDHKtFwOrM/NSgslP2WEXEHsAemXlFROwErABeCRyH59UWhjlWr8fzagsREcCszFwdEdsBPwf6gFfjebXJMMfpUDyn6oqIfwQWADtn5hH+D9ySNSIaybOASmb+MTMfAr4BvKLNMU0GrwBOL5+fTvHPf8rJzJ8Bdw+aPdSxeQXwjcxcl5k3AhWK829KGOJYDWXKHqvMvC0zryif3w9cB3TjebWVYY7VUKbyscrMXF1Oblf+JZ5XWxjmOA1lSh6nqoiYBxwO/FfNbM+pGiYiGkk3cEvN9K0M/49sKkrgRxGxIiKOL+fNzczboLgYAB7Vtug6z1DHxnOtvndFxG/LplvVKnyPFRARPcDTgUvxvBrWoGMFnldbKZvQXAX8GbgoMz2v6hjiOIHnVD2fBd4PbKyZ5zlVw0REI4k682zPt6XnZeYzgJcB7yyb2Gj0PNe29v+AxwD7A7cBny7nT/ljFRGzgXOAd2fmfcMVrTNvqh8rz6s6MnNDZu4PzAOeFRFPHqb4lD1WQxwnz6lBIuII4M+ZuaLRRerM2+aPlYmIRnIrsFfN9DzgT22KpSNl5p/Kxz8D36GoSr2jbJ9dbaf95/ZF2HGGOjaea4Nk5h3lP/2NwH+yuZp+Sh+rsm36OcDXMvPb5WzPqzrqHSvPq+Fl5j3AMop+D55XQ6g9Tp5TdT0POLLsR/oN4OCI+B88p7ZgIqKRXA7sFxH7RMT2wFHA99ocU8eIiFllJ1AiYhbw18A1FMfo2LLYscC57YmwIw11bL4HHBURO0TEPsB+wGVtiK9jVP9ZlV5FcW7BFD5WZWfZ/wauy8z/qHnJ82qQoY6V59XWIuKREbFr+XwmcAhwPZ5XWxjqOHlObS0zP5SZ8zKzh+La6ZLM/Fs8p7Ywvd0BqLNl5vqIeBfwQ6AL+HJmXtvmsDrJXOA7xf97pgNfz8wLI+Jy4JsR8ffAzcDr2hhj20TEmcCBwO4RcStwEvAJ6hybzLw2Ir4J/A5YD7xzWx8tpNYQx+rAiNifonp+JfA2mPLH6nnA3wFXl+3UAT6M51U9Qx2roz2vtrIHcHo5UuQ04JuZeV5E/ArPq1pDHaevek41zO+qGg7fK0mSJKnlbJolSZIkqeVMRCRJkiS1nImIJEmSpJYzEZEkSZLUciYikiRJklrORESSppiIWBoRGRE97Y5Fw4uIleUN0TpCRCyLiAkZbjMiesrzcumg+aM+XzvtuEmqz0REkpqgvFAazd9xo1h3Wy+qai4Ea/82RMSqiLgkIt44xHIra8ofNMz6v1JTbtGg1xYN2u7GiLgvIm6KiAsi4gMR0d3kXa4XYzWOA0e5XE+dY7c2Iv4cEb+OiFMj4gUTE/Wm83LZRK1/mO0uG+XnYWmrY5TUft7QUJKa4+Q6894N7AIsAe4Z9NpVExvOhDiXzXFvD+wLHAkcFBFPzMyPDLHceuCtwE8GvxAROwOvL8sM9z/pp8Cy8vksihurPQ94GXByRCzKzE+MZmda7F7gs+Xz6cBuwNOAdwDvjIgfAcdk5h2DlntxyyJsrqVsfr+qXkmxz7XnUdVVwADwBIpjNV6T9bhJU4qJiCQ1QWYuGjyvrPXYBfhsZq5scUgT4buZubR2RkQ8E1gO/GNEfCwzH6yz3HnAqyNiTmauGvTaG4Edge8Arxpm28sGH+OICODVwGnAxyOCDk5G7hniHNkX+G/gr4ELI+I5tccwM//QuhCbZ/B5AkXtEEUistV5VOP6Jm1/Uh43aaqxaZYktUFEvD4ifhYR95ZNda6OiA9FxA41ZQ4s2+PvDew9VFOWiHhlRPxPRPxfRDwQEasjYkVEnBARE/o9n5krgLuBGcBOQxT7T2AH4O/qvPZW4BbgwjFsOzPzHOC15ayTImKPRpaNiIMi4rSI+F3Z1GttRFwTESdFxIxBZVcCJ5WTP6l9H0Ybc519+CNwOMUF+P7A2wdve3CzvIjYvnxvr4iIv0TEmrLcuRFxSFnmuJr4XjTo3FlUs67jIuKciPhjeQzui4hfRMTfjnffRiuG6CNSY1pE/GNEXB8RD0bErRHxmbJWbfC66h23XSLifVE0J7w1Ih6KiDsj4nsR8ewJ2CVJI7BGRJJaLCJOAT4E3AV8HVhN0cToFOClEfGSzHwYWEnR5Ovd5aKfrVnNVTXPPwFsBC6laN6yC3AwRZOwA6ifADRFRDyDopnRTZl55xDFLqLYl7dQsw9lbcrTKfZx41hjyMyfRMTPgedT1JB8voHFPgA8HvglcD5FIvU8YBFwYEQckpkbyrKfpWhW9CLg9HJfmiYz10TEp4D/oqgh+uwIiywFjgauAc4A1gJ7Uuz/ocDFFOfHyRQJ1E3lMlXLap7/P+B3wM+A24A5wGHAVyPicZn5z2PdrwnwGeCFwDcpmne9lOKz8YKIeP4QtXG1ngD8K8W+ng/8BZhP0bzwZRHx8swcdUIsaexMRCSphSLiORRJyC3AszLz9nL+hyiaJx0BvA84pWzOtahs4lW3+Vfp8MFNUcqakK8Ax0TEqZl5aRPCf2VsHrloe6CH4iLuVoZPdpKi+dHHyqZHvyrnv5UiAfkyRdOk8VhGcSH+LBpLRP4BuDEzt6jViIiPASdS1LKcBZCZn42IXSkSkaWZuWycsdZTXefTI2J6Zq6vVygidgGOAlYAf1WTLFVfn1PGfBVwVUScBKwc5tx5cp1zZ3vgB8AHI+KLmTkwtl1quucB+2fmTbDpM/MtiuTzfcDHRlj+OmDPzLyrdmZEzAMuo0h0TESkFrJpliS11pvLx8XVJASgvPD8J4oL87eMZoX12sNn5kaKGhEofjluhldQ/MJ+EkUydTTF/5GvA1ePsOyXgQ0UyQcRMQt4A/DDzLy5CbFVL5Yf2UjhzPzj4CSk9NnysVnHrFHV+LsoapiGkkAA66hTi1SnD86whjh3HqJI5qbTWZ2+l1STENh0jr+P4ji8ecilNpe/d3ASUs6/FTgbeHxEzG9ivJJGYCIiSa31jPLxksEvZOb/UdQu7FP+At+QiJgTEZ+IiN+W/UOq/RdWlEWaNbztmzIzMjMoLlJ7KJqFvQ/4dUTMHmrBzPwTcAHw+rJN/1EUfUr+s0mxRXVTDRWOmBURH46Iy8t+OhvLY1a9UJ3wIYEHh1TzfMh9yMz7gO8Dz6Wo8fiXsr/LjmPaaMT8iPh82e9iTc25c05ZpNXHYTg/HTyj7GNzC9DTyGcmIp4XEd+MiFsiYl3N/i4si3TS/krbPJtmSVJr7VI+3jbE67dRtFvfha2H/N1KefF1ObAPRfOSMyg6j68HdgX6KDqKN1XZJOgm4KMR8ViKvg0LgY8Ps9h/Ai+nqEl5E3A7xUV1M+xZPg7VT2WTiNiOIhF8FkU/i7PK5R4ui5zEBByzEVTj30DRd2E4f0PRx+UNbB42+sGIOBt4b50hgOuKYsSuy4BHAP8L/Ihi6NwNFEnmsbT+OAxnqP26nWJAh2E/MxHxKoqajwcp+i39AXiAokblQIqmd520v9I2z0REklqreo+ER1NcCA22x6ByI3kLRRJycp3hbZ9DkYhMtEspEpFnjVDuAoomSCcC84CPD9UXYgyqN0xspC/MKyhiPT0zj6t9oRx166R6C02wavwrRjommbmWolP9oojYi6ID93HA31IkEI3eIPEfKTqnv6nOsMxHUyQinWQu8Ps68x9dPo70mfkY8BCwIDOvq30hIr5EkYhIaiGbZklSa11ZPh44+IWI6KW4QL8xM++peWkDRd+BenrLx3PqvNaqC6tHlI/D/k8pa1G+TLGP1Q7s4xYRB1N0ZF5L0eF/JGM5ZtVO4UO9D2NWNqv6p3Lya6NZNjNvycyvUfRpuQF4frXDemkjnX3ujMZWMZW1OntRdMi/Z4Tle4Hf1UlCplEMdCCpxUxEJKm1vlw+nhgRmzpWR0QX8CmK7+XBF+irgEdGxMw661tZPh5YOzMink7RoXxCRcQjKJpZwdZ30q7ncxQ3LnzpeG86F4VXU4ycBHBS7QAAw1hZPh44aH37Av82xDLVTuBN7cwcEftQDCX7eIok9UsjlH9kRPxVnZdmUfS5WU/xq3/VKooL9XpWlo8HDtrGSxnlgAkt0hcRe1cnygTi3yk+M19pYPmVwH4RUW0GV70p5knAE5sbqqRG2DRLklooM38ZEZ8E3g9cU7brf4DiPiJPBn5OcXFV68cU9wO5MCJ+RjFi0m8y8/sUfULeB3w2Ig6i+FV8P4phgL9N0Z+gWWqH7+2iqNl4OUXznsuBL460gnLUou+OYdsHxuYb8c2k6FPxPIpmaeuAD2Tm4OM2lO8DFYq7wT+FIgGYT3HMzqd+svETitqFj0fEkyn7cWTm4ga3uWtN/NMpapGeBjyH4kL6QuDYzFw3wnq6KQYGuA64gqKj9s5l7I8GPpeZ99eU/zFwVER8n2LwgvXAzzLzZ8AXKJLIb0XEORTN5p5McS+Sb9Lcc6cZfkHRQf8simZYL6U4hiuATzaw/GcoztEry/19mOIceiLFOfHyiQha0tBMRCSpxTLzAxFxJfAu4BhgO4r+IicCny6HT621mKLj+cspLpy6KG6s9/3M/FNEvIBi9KrnU1ycXU9xn4yLae7F5CvKv6r7y239G9DfwA3lxuNF5V9SJG53A9dS1CD8z2judZGZD5TNuT5BURvwAuCPFH0I/oM6xywzr4uIY4H3Uhzb6t3XG01EdmFz35N1wH3lNr8AnJWZP29wPSvL9RxI0a9kd4pj8Xvgg8A3BpXvozhmL6a4UeE0ig7uP8vM35bJ6+LytenAbyjuy3EPnZeIvIeiNu2tFH1hVlEMUf0vjZx7mfmliFhHcRPEYyma8v0vRTL2GkxEpJaL+sOoS5IkSdLEsY+IJEmSpJYzEZEkSZLUciYikiRJklrORESSJElSy5mISJIkSWo5ExFJkiRJLWciIkmSJKnlTEQkSZIktZyJiCRJkqSWMxGRJEmS1HL/H+cyXd0fcjw2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot by site and new fracture side by side \n",
    "plt.figure(figsize=(12,6))\n",
    "s.boxplot(data=xtrtmp , x='t_ttbmd', y='sitename', orient='h', hue='prevfx_any',) # orient=h is default, could also be v (vertical)\n",
    "plt.title(\"Total BMD at Distal Tibia by Site\", fontsize = 25)\n",
    "plt.xlabel('Total BMD at Distal Tibia', fontsize=20)\n",
    "plt.ylabel('Site', fontsize = 20)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6ec968e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#plt.figure(figsize=(12,6))\n",
    "#s.boxplot(data=xtrtmp[xtrtmp['prevfx_any']==0.0] , x='t_ttbmd', y='sitename', orient='h') # orient=h is default, could also be v (vertical)\n",
    "#plt.title(\"t_ttbmd by Site\")\n",
    "#plt.xlabel('t_ttbmd')\n",
    "#plt.ylabel('Site')\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "7d52bb72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_moart</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    freq\n",
       "r_moart      \n",
       "1.0       264\n",
       "2.0       567\n",
       "3.0       270\n",
       "4.0        55\n",
       "5.0        22"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=xtrtmp['r_moart'], columns='freq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "f02a1e14",
   "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>col_0</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_moart</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    freq\n",
       "t_moart      \n",
       "1.0       847\n",
       "2.0       271\n",
       "3.0        60\n",
       "4.0         5\n",
       "5.0         3"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=xtrtmp['t_moart'], columns='freq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "728c0357",
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dave_\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q4. b) Determine the distribution (frequencies) of motion grades (moart)\n",
    "plt.hist(xtrtmp['r_moart'],edgecolor='k',color='blue', bins=5)\n",
    "plt.title(\"r_moart Distribution\")\n",
    "plt.xlabel('r_moart')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "s.distplot(xtrtmp['t_moart'], hist=True) \n",
    "plt.title('t_moart Frequency')\n",
    "plt.xlabel('t_moart')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "24266abf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Site')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Q4.c) Repeat the boxplot exercise but look only at motion artifact (t_moart) grades 3 and above, restricting the site to Toronto and Vancouver\n",
    "\n",
    "import seaborn as s\n",
    "#create new subset with T / V\n",
    "boxV= xtrtmp[xtrtmp['sitename'] == 'Vancouver'] #create subset df 1\n",
    "boxT= xtrtmp[xtrtmp['sitename'] == 'Toronto - Referent'] #create subset df 2\n",
    "boxTV = pd.concat([boxT, boxV], axis=0) #merge df's\n",
    "boxTV\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "s.boxplot(data=boxTV[boxTV['t_moart'] >=3.0], x='t_ttbmd', y='sitename' , orient='h') \n",
    "plt.title(\"Total BMD at Distal Tibia by Site\")\n",
    "plt.xlabel('Total BMD at Distal Tibia')\n",
    "plt.ylabel('Site')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "d91ce32c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total BMD at Distal Tibia')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Q5. Create a scatterplot of how t_ttbmd relates to age\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "tbmd_age = s.scatterplot(x= 'age', y = 't_ttbmd', \n",
    "            data=xtrtmp, \n",
    "            hue='newosteofx', \n",
    "            s=100) \n",
    "\n",
    "tbmd_age.set_title('Total BMD at Distal Tibia by Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Total BMD at Distal Tibia')\n",
    "\n",
    "# Alternatively, set labels with \n",
    "# fig.set_axis_labels(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4f424c39",
   "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>replicate</th>\n",
       "      <th>qid</th>\n",
       "      <th>t_fl_fea</th>\n",
       "      <th>t_ctpo_ec</th>\n",
       "      <th>t_ttar</th>\n",
       "      <th>t_ctar</th>\n",
       "      <th>t_tbar</th>\n",
       "      <th>t_ttbmd</th>\n",
       "      <th>t_ctbmd</th>\n",
       "      <th>t_ctth</th>\n",
       "      <th>...</th>\n",
       "      <th>meno_status</th>\n",
       "      <th>liver_ever</th>\n",
       "      <th>status</th>\n",
       "      <th>r_ttop</th>\n",
       "      <th>t_ttop</th>\n",
       "      <th>r_tthip</th>\n",
       "      <th>t_tthip</th>\n",
       "      <th>numberhits</th>\n",
       "      <th>id</th>\n",
       "      <th>sitename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01519'</td>\n",
       "      <td>4901.9</td>\n",
       "      <td>8.83</td>\n",
       "      <td>960.47070</td>\n",
       "      <td>72.70221</td>\n",
       "      <td>879.26660</td>\n",
       "      <td>160.3830</td>\n",
       "      <td>805.1053</td>\n",
       "      <td>0.757581</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>Calgary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01519'</td>\n",
       "      <td>4901.9</td>\n",
       "      <td>8.83</td>\n",
       "      <td>960.47070</td>\n",
       "      <td>72.70221</td>\n",
       "      <td>879.26660</td>\n",
       "      <td>160.3830</td>\n",
       "      <td>805.1053</td>\n",
       "      <td>0.757581</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>Calgary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01545'</td>\n",
       "      <td>6204.0</td>\n",
       "      <td>9.56</td>\n",
       "      <td>701.02992</td>\n",
       "      <td>99.59033</td>\n",
       "      <td>593.70643</td>\n",
       "      <td>281.8728</td>\n",
       "      <td>799.4792</td>\n",
       "      <td>1.063528</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>Calgary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01590'</td>\n",
       "      <td>5529.5</td>\n",
       "      <td>10.21</td>\n",
       "      <td>689.24115</td>\n",
       "      <td>104.05397</td>\n",
       "      <td>576.36756</td>\n",
       "      <td>253.3904</td>\n",
       "      <td>813.7205</td>\n",
       "      <td>1.142136</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>Calgary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b'CA01620'</td>\n",
       "      <td>5638.3</td>\n",
       "      <td>6.32</td>\n",
       "      <td>588.60634</td>\n",
       "      <td>109.83789</td>\n",
       "      <td>473.16833</td>\n",
       "      <td>302.2676</td>\n",
       "      <td>873.6742</td>\n",
       "      <td>1.277396</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>Calgary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   replicate         qid  t_fl_fea  t_ctpo_ec     t_ttar     t_ctar  \\\n",
       "0        1.0  b'CA01519'    4901.9       8.83  960.47070   72.70221   \n",
       "1        1.0  b'CA01519'    4901.9       8.83  960.47070   72.70221   \n",
       "2        1.0  b'CA01545'    6204.0       9.56  701.02992   99.59033   \n",
       "3        1.0  b'CA01590'    5529.5      10.21  689.24115  104.05397   \n",
       "4        1.0  b'CA01620'    5638.3       6.32  588.60634  109.83789   \n",
       "\n",
       "      t_tbar   t_ttbmd   t_ctbmd    t_ctth  ...  meno_status  liver_ever  \\\n",
       "0  879.26660  160.3830  805.1053  0.757581  ...          2.0         0.0   \n",
       "1  879.26660  160.3830  805.1053  0.757581  ...          2.0         0.0   \n",
       "2  593.70643  281.8728  799.4792  1.063528  ...          3.0         0.0   \n",
       "3  576.36756  253.3904  813.7205  1.142136  ...          3.0         0.0   \n",
       "4  473.16833  302.2676  873.6742  1.277396  ...          3.0         0.0   \n",
       "\n",
       "   status  r_ttop  t_ttop  r_tthip  t_tthip  numberhits     id  sitename  \n",
       "0     0.0    23.0    23.0     23.0     23.0         2.0  891.0   Calgary  \n",
       "1     0.0    23.0    23.0     23.0     23.0         2.0  892.0   Calgary  \n",
       "2     0.0    34.0    34.0     34.0     34.0         1.0  893.0   Calgary  \n",
       "3     0.0    34.0    34.0     34.0     34.0         1.0  894.0   Calgary  \n",
       "4     0.0    29.0    29.0     29.0     29.0         3.0  895.0   Calgary  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nandf = xtrtmp[xtrtmp.isna().any(axis=1)]\n",
    "nandf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "393d5e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q6. a) Please create new cross-calibrated values for all sites' 't_ttbmd', 't_ctbmd', 'r_ttbmd', 'r_ctbmd' values according to this table.\n",
    "#copy df to add new values to\n",
    "xtrtmp2 = xtrtmp\n",
    "\n",
    "#add 't_xtbmd_new' to xtrtmp df   't_ttbmd', 't_ctbmd', 'r_ttbmd', 'r_ctbmd \n",
    "         # s.boxplot(ax=ax1, data=xtrtmp, x='xvar', y='yvar', hue='categoryvar' ) \n",
    "\n",
    "# Method 1 - Vancouver & Calgary\n",
    "xtrtmp.loc[xtrtmp['sitename'] == 'Vancouver', 't_ttbmd_new'] = 0.99 * xtrtmp['t_ttbmd'] - 3.93\n",
    "xtrtmp.loc[xtrtmp['sitename'] == 'Vancouver', 't_ctbmd_new'] = 1.04 * xtrtmp['t_ctbmd'] - 0.05\n",
    "\n",
    "xtrtmp.loc[xtrtmp['sitename'] == 'Calgary', 't_ttbmd_new'] = 0.97 * xtrtmp['t_ttbmd'] + 19.06\n",
    "xtrtmp.loc[xtrtmp['sitename'] == 'Calgary', 't_ctbmd_new'] = 1.05 * xtrtmp['t_ctbmd'] - 6.82\n",
    "\n",
    "\n",
    "# Method 2: Saskatoon  \n",
    "def SaskatoonTTbmd(sitename, t_ttbmd): #funciton to return cross calibrated value t_ttbmd for Saskatoon\n",
    "    if sitename == \"Saskatoon\":\n",
    "        return t_ttbmd * 1.03 - 232.26 \n",
    "    else:\n",
    "        return t_ttbmd\n",
    "    \n",
    "def SaskatoonCTbmd(sitename, t_ctbmd): #function to return cross calibrated value t_ctbmd for Saskatoon\n",
    "    if sitename == \"Saskatoon\":\n",
    "        return t_ctbmd * 0.99 - 0.03\n",
    "    else:\n",
    "        return t_ctbmd  \n",
    "    \n",
    "for i in xtrtmp.index: #apply two above functions to generate new values t_ttbmd t_ctbmd\n",
    "    if xtrtmp['sitename'][i] == \"Saskatoon\" : xtrtmp['t_ttbmd_new'] = xtrtmp.apply(lambda x: SaskatoonTTbmd(x['sitename'], x['t_ttbmd']), axis=1) \n",
    "    if xtrtmp['sitename'][i] == \"Saskatoon\" : xtrtmp['t_ctbmd_new'] = xtrtmp.apply(lambda x: SaskatoonCTbmd(x['sitename'], x['t_ctbmd']), axis=1)\n",
    "        \n",
    "\n",
    " # xtrtmp['t_ttbmd_new'] = xtrtmp.apply(lambda x: SaskatoonTTbmd(x['sitename'], x['t_ttbmd']), axis=1)\n",
    "# xtrtmp['t_ctbmd_new'] = xtrtmp.apply(lambda x: SaskatoonCTbmd(x['sitename'], x['t_ctbmd']), axis=1)\n",
    "       \n",
    "###### not using this method ####\n",
    "###Method 3 Calgary\n",
    "#for i in xtrtmp.index: #t_ttbmd\n",
    "#    if xtrtmp['sitename'][i] == \"Calgary\" : xtrtmp['t_ttbmd_new'][i] = 0.97 * xtrtmp['t_ttbmd'][i] + 19.06\n",
    "#    elif xtrtmp['sitename'][i] == \"Calgary\" : xtrtmp['t_ctbmd_new'][i] = 1.05 * xtrtmp['t_ctbmd'][i] - 6.82  \n",
    "##########\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "994a5ad5",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'method' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-189-66918235818d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mxtrtmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxtrtmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sitename'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Vancouver'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m't_ttbmd_new'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.99\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mxtrtmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m't_ttbmd'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m3.93\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mxtrtmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mxtrtmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sitename'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Vancouver'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mxtrtmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sitename'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'method' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "xtrtmp.loc[xtrtmp['sitename'] == 'Vancouver', 't_ttbmd_new'] = 0.99 * xtrtmp['t_ttbmd'] - 3.93\n",
    "xtrtmp.loc[xtrtmp['sitename'] == 'Vancouver'].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5786061f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['t_fl_fea', 't_ctpo_ec', 't_ttar', 't_ctar', 't_tbar', 't_ttbmd',\n",
       "       't_ctbmd', 't_ctth', 't_tbbmd', 't_tbn', 't_tbth', 't_tbsp', 't_moart',\n",
       "       'r_fl_fea', 'r_ctpo_ec', 'r_ttar', 'r_ctar', 'r_tbar', 'r_ttbmd',\n",
       "       'r_ctbmd', 'r_ctth', 'r_tbbmd', 'r_tbn', 'r_tbth', 'r_tbsp', 'r_moart',\n",
       "       't_bvtv', 'r_bvtv', 'fnewopfxd', 'fnewhipfxd', 'newosteofx',\n",
       "       'firstnewosteofx_site', 'firstnewosteofx_year', 'firstnewosteofx_month',\n",
       "       'firstnewosteofx_day', 'newosteofx_trauma', 'newhipfx',\n",
       "       'firstnewhipfx_year', 'firstnewhipfx_month', 'firstnewhipfx_day',\n",
       "       'newhipfx_trauma', 'age', 'height', 'weight', 'centre', 'parenthipfx',\n",
       "       'prevfx_any', 'cig_cur', 'nbalc_week', 'rheu_ever', 'cortico_oral_reg',\n",
       "       'nbfall_pastyr', 'everlost10lbs', 'osteomed', 'prev_backmt',\n",
       "       'prev_hipmt', 'hicnum', 'sex', 'type1diabetes_ever',\n",
       "       'untreat_hyperthyr_ever', 'age_at_menop', 'meno_status', 'liver_ever',\n",
       "       'r_ttop', 't_ttop', 'r_tthip', 't_tthip', 'sitename', 't_ttbmd_new',\n",
       "       't_ctbmd_new'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrtmp.columns[xtrtmp.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "58f5c00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Bone Mineral Density')"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q6. b) Create a 1 x 2 subplot containing 2 boxplots: 1 for before and 1 after calibration\n",
    "\n",
    "fig, axes = plt.subplots(1,2)\n",
    "fig.set_size_inches(12, 5)\n",
    "fig.suptitle('Total Bone Mineral Density at the distal radius - Before and After Calibration', fontsize=15)\n",
    "\n",
    "\n",
    "\n",
    "sb.boxplot(ax=axes[0], data=xtrtmp, x='sitename', y='t_ttbmd') #hue='t_ttbmd_new')\n",
    "axes[0].set_title('Before', fontsize=16)\n",
    "axes[0].set_xlabel('Site', fontsize=14)\n",
    "axes[0].set_ylabel('Bone Mineral Density', fontsize=14)\n",
    "\n",
    "\n",
    "\n",
    "sb.boxplot(ax=axes[1], data=xtrtmp, x='sitename', y='t_ttbmd_new')\n",
    "axes[1].set_title('After', size=16)\n",
    "axes[1].set_xlabel('Site', fontsize=14)\n",
    "axes[1].set_ylabel('Bone Mineral Density', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14abc292",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "d27effe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#xtrtmp.loc[xtrtmp['sitename'] == 'Vancouver', 't_ttbmd_new'] = 0.99 * xtrtmp['t_ttbmd'] - 3.93\n"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}