--- a
+++ b/analytics/p11_Sleep_Glucose.ipynb
@@ -0,0 +1,1736 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0ef95ab1-8c24-457c-b9b8-0eee3955d0fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "edbac203-6c5d-4491-a386-4fcff5466456",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T01:17:00</td>\n",
+       "      <td>2023-12-22T09:03:00</td>\n",
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+       "      <td>16560</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T01:17:00</td>\n",
+       "      <td>2023-12-22T09:03:00</td>\n",
+       "      <td>27960</td>\n",
+       "      <td>8400</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>16560</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P10</td>\n",
+       "      <td>2023-12-22</td>\n",
+       "      <td>2023-12-22T01:17:00</td>\n",
+       "      <td>2023-12-22T09:03:00</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3791</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
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+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3792</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
+       "      <td>4140</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3793</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
+       "      <td>4140</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3794</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
+       "      <td>4140</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3795</th>\n",
+       "      <td>P14</td>\n",
+       "      <td>2024-01-08</td>\n",
+       "      <td>2024-01-08T00:01:00</td>\n",
+       "      <td>2024-01-08T07:34:00</td>\n",
+       "      <td>27180</td>\n",
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+       "      <td>6600</td>\n",
+       "      <td>16380</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3796 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "0                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "1                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "2                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "3                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "4                P10            2023-12-22  2023-12-22T01:17:00   \n",
+       "...              ...                   ...                  ...   \n",
+       "3791             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3792             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3793             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3794             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "3795             P14            2024-01-08  2024-01-08T00:01:00   \n",
+       "\n",
+       "         End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "0     2023-12-22T09:03:00         27960                    8400   \n",
+       "1     2023-12-22T09:03:00         27960                    8400   \n",
+       "2     2023-12-22T09:03:00         27960                    8400   \n",
+       "3     2023-12-22T09:03:00         27960                    8400   \n",
+       "4     2023-12-22T09:03:00         27960                    8400   \n",
+       "...                   ...           ...                     ...   \n",
+       "3791  2024-01-08T07:34:00         27180                    4140   \n",
+       "3792  2024-01-08T07:34:00         27180                    4140   \n",
+       "3793  2024-01-08T07:34:00         27180                    4140   \n",
+       "3794  2024-01-08T07:34:00         27180                    4140   \n",
+       "3795  2024-01-08T07:34:00         27180                    4140   \n",
+       "\n",
+       "      Deep Sleep Duration (s)  Light Sleep Duration (s)  Source  \n",
+       "0                        3000                     16560  device  \n",
+       "1                        3000                     16560  device  \n",
+       "2                        3000                     16560  device  \n",
+       "3                        3000                     16560  device  \n",
+       "4                        3000                     16560  device  \n",
+       "...                       ...                       ...     ...  \n",
+       "3791                     6600                     16380  device  \n",
+       "3792                     6600                     16380  device  \n",
+       "3793                     6600                     16380  device  \n",
+       "3794                     6600                     16380  device  \n",
+       "3795                     6600                     16380  device  \n",
+       "\n",
+       "[3796 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "df = pd.read_csv('../data/garmin/sleep.csv', sep=',')\n",
+    "\n",
+    "# Trim columns\n",
+    "df = df.loc[:, ['User First Name', 'Calendar Date (Local)', 'Start Time (Local)', 'End Time (Local)', 'Duration (s)', 'Rem Sleep Duration (s)', 'Deep Sleep Duration (s)', 'Light Sleep Duration (s)', 'Source']]\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "35371d2e-8419-44bd-83dd-6ecdfb18b717",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T23:04:00</td>\n",
+       "      <td>2023-12-27T23:05:00</td>\n",
+       "      <td>60</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
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+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1284</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T15:00:00</td>\n",
+       "      <td>2024-01-08T08:43:00</td>\n",
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+       "      <td>8100</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1285</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T15:00:00</td>\n",
+       "      <td>2024-01-08T08:43:00</td>\n",
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+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T15:00:00</td>\n",
+       "      <td>2024-01-08T08:43:00</td>\n",
+       "      <td>63780</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>8100</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1287</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T15:00:00</td>\n",
+       "      <td>2024-01-08T08:43:00</td>\n",
+       "      <td>63780</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15660</td>\n",
+       "      <td>8100</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1288</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-07</td>\n",
+       "      <td>2024-01-07T15:00:00</td>\n",
+       "      <td>2024-01-08T08:43:00</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>15660</td>\n",
+       "      <td>8100</td>\n",
+       "      <td>server</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>612 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "677              P11            2023-12-27  2023-12-27T23:04:00   \n",
+       "678              P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "679              P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "680              P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "681              P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "...              ...                   ...                  ...   \n",
+       "1284             P11            2024-01-07  2024-01-07T15:00:00   \n",
+       "1285             P11            2024-01-07  2024-01-07T15:00:00   \n",
+       "1286             P11            2024-01-07  2024-01-07T15:00:00   \n",
+       "1287             P11            2024-01-07  2024-01-07T15:00:00   \n",
+       "1288             P11            2024-01-07  2024-01-07T15:00:00   \n",
+       "\n",
+       "         End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "677   2023-12-27T23:05:00            60                       0   \n",
+       "678   2023-12-29T08:50:00         26400                    5220   \n",
+       "679   2023-12-29T08:50:00         26400                    5220   \n",
+       "680   2023-12-29T08:50:00         26400                    5220   \n",
+       "681   2023-12-29T08:50:00         26400                    5220   \n",
+       "...                   ...           ...                     ...   \n",
+       "1284  2024-01-08T08:43:00         63780                       0   \n",
+       "1285  2024-01-08T08:43:00         63780                       0   \n",
+       "1286  2024-01-08T08:43:00         63780                       0   \n",
+       "1287  2024-01-08T08:43:00         63780                       0   \n",
+       "1288  2024-01-08T08:43:00         63780                       0   \n",
+       "\n",
+       "      Deep Sleep Duration (s)  Light Sleep Duration (s)  Source  \n",
+       "677                        60                         0  server  \n",
+       "678                      6660                     11640  device  \n",
+       "679                      6660                     11640  device  \n",
+       "680                      6660                     11640  device  \n",
+       "681                      6660                     11640  device  \n",
+       "...                       ...                       ...     ...  \n",
+       "1284                    15660                      8100  server  \n",
+       "1285                    15660                      8100  server  \n",
+       "1286                    15660                      8100  server  \n",
+       "1287                    15660                      8100  server  \n",
+       "1288                    15660                      8100  server  \n",
+       "\n",
+       "[612 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select records for one user\n",
+    "p_df = df[df['User First Name'] == 'P11']\n",
+    "p_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2df3de7b-8cd8-4b07-802a-55eb967f339b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    .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>glucose</th>\n",
+       "      <th>recorded_timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>106</td>\n",
+       "      <td>2023-12-23 00:00:59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>104</td>\n",
+       "      <td>2023-12-23 00:02:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>102</td>\n",
+       "      <td>2023-12-23 00:03:01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>101</td>\n",
+       "      <td>2023-12-23 00:04:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>101</td>\n",
+       "      <td>2023-12-23 00:05:01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17630</th>\n",
+       "      <td>98</td>\n",
+       "      <td>2023-12-28 23:55:32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17631</th>\n",
+       "      <td>97</td>\n",
+       "      <td>2023-12-28 23:56:33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17632</th>\n",
+       "      <td>97</td>\n",
+       "      <td>2023-12-28 23:57:33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17633</th>\n",
+       "      <td>96</td>\n",
+       "      <td>2023-12-28 23:58:35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17634</th>\n",
+       "      <td>96</td>\n",
+       "      <td>2023-12-28 23:59:35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17635 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       glucose  recorded_timestamp\n",
+       "0          106 2023-12-23 00:00:59\n",
+       "1          104 2023-12-23 00:02:00\n",
+       "2          102 2023-12-23 00:03:01\n",
+       "3          101 2023-12-23 00:04:00\n",
+       "4          101 2023-12-23 00:05:01\n",
+       "...        ...                 ...\n",
+       "17630       98 2023-12-28 23:55:32\n",
+       "17631       97 2023-12-28 23:56:33\n",
+       "17632       97 2023-12-28 23:57:33\n",
+       "17633       96 2023-12-28 23:58:35\n",
+       "17634       96 2023-12-28 23:59:35\n",
+       "\n",
+       "[17635 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load glucose dataset\n",
+    "glucose_df = pd.read_csv('../data/P11/supersapiens/merged.csv', sep=';')\n",
+    "\n",
+    "# Convert timestamp\n",
+    "glucose_df['recorded_timestamp'] = pd.to_datetime(glucose_df['recorded_timestamp'])\n",
+    "\n",
+    "glucose_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6258651b-17d4-4bdb-82c2-794b760b9dce",
+   "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>glucose</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recorded_timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-12-21</th>\n",
+       "      <td>99.417051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-22</th>\n",
+       "      <td>102.465241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-23</th>\n",
+       "      <td>100.516803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-24</th>\n",
+       "      <td>98.061563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-25</th>\n",
+       "      <td>93.704066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-26</th>\n",
+       "      <td>99.168745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-27</th>\n",
+       "      <td>99.998476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-28</th>\n",
+       "      <td>96.941217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-29</th>\n",
+       "      <td>94.850168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-30</th>\n",
+       "      <td>102.743633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31</th>\n",
+       "      <td>101.346390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-01</th>\n",
+       "      <td>104.948331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-02</th>\n",
+       "      <td>108.605453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-03</th>\n",
+       "      <td>109.498243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-01-04</th>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       glucose\n",
+       "recorded_timestamp            \n",
+       "2023-12-21           99.417051\n",
+       "2023-12-22          102.465241\n",
+       "2023-12-23          100.516803\n",
+       "2023-12-24           98.061563\n",
+       "2023-12-25           93.704066\n",
+       "2023-12-26           99.168745\n",
+       "2023-12-27           99.998476\n",
+       "2023-12-28           96.941217\n",
+       "2023-12-29           94.850168\n",
+       "2023-12-30          102.743633\n",
+       "2023-12-31          101.346390\n",
+       "2024-01-01          104.948331\n",
+       "2024-01-02          108.605453\n",
+       "2024-01-03          109.498243\n",
+       "2024-01-04          106.852094"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group glucose by day\n",
+    "glucose_mean_per_day = pd.DataFrame(glucose_df.groupby(glucose_df['recorded_timestamp'].dt.date)['glucose'].mean())\n",
+    "\n",
+    "# Change index data type\n",
+    "glucose_mean_per_day.index = pd.to_datetime(glucose_mean_per_day.index)\n",
+    "\n",
+    "glucose_mean_per_day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e96fb9-ca44-42ff-9c09-5300d4ca7aaf",
+   "metadata": {},
+   "source": [
+    "# Aggregate average glucose level of the day with sleep data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1e3e11f6-4464-4935-a567-644258e92c4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\bjoer\\AppData\\Local\\Temp\\ipykernel_1788\\165701922.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\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",
+      "  p_df['Calendar Date (Local)'] = pd.to_datetime(p_df['Calendar Date (Local)'])\n"
+     ]
+    },
+    {
+     "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>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>glucose</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T23:04:00</td>\n",
+       "      <td>2023-12-27T23:05:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>server</td>\n",
+       "      <td>99.998476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>678</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.850168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>679</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.850168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.850168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.850168</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>934</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T19:47:00</td>\n",
+       "      <td>2024-01-05T09:24:00</td>\n",
+       "      <td>49020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22020</td>\n",
+       "      <td>11400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>935</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T19:47:00</td>\n",
+       "      <td>2024-01-05T09:24:00</td>\n",
+       "      <td>49020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22020</td>\n",
+       "      <td>11400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>936</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T19:47:00</td>\n",
+       "      <td>2024-01-05T09:24:00</td>\n",
+       "      <td>49020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22020</td>\n",
+       "      <td>11400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>937</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T19:47:00</td>\n",
+       "      <td>2024-01-05T09:24:00</td>\n",
+       "      <td>49020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22020</td>\n",
+       "      <td>11400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>938</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T11:25:00</td>\n",
+       "      <td>2024-01-04T12:46:00</td>\n",
+       "      <td>4860</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>262 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "677             P11            2023-12-27  2023-12-27T23:04:00   \n",
+       "678             P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "679             P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "680             P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "681             P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "..              ...                   ...                  ...   \n",
+       "934             P11            2024-01-04  2024-01-04T19:47:00   \n",
+       "935             P11            2024-01-04  2024-01-04T19:47:00   \n",
+       "936             P11            2024-01-04  2024-01-04T19:47:00   \n",
+       "937             P11            2024-01-04  2024-01-04T19:47:00   \n",
+       "938             P11            2024-01-04  2024-01-04T11:25:00   \n",
+       "\n",
+       "        End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "677  2023-12-27T23:05:00            60                       0   \n",
+       "678  2023-12-29T08:50:00         26400                    5220   \n",
+       "679  2023-12-29T08:50:00         26400                    5220   \n",
+       "680  2023-12-29T08:50:00         26400                    5220   \n",
+       "681  2023-12-29T08:50:00         26400                    5220   \n",
+       "..                   ...           ...                     ...   \n",
+       "934  2024-01-05T09:24:00         49020                       0   \n",
+       "935  2024-01-05T09:24:00         49020                       0   \n",
+       "936  2024-01-05T09:24:00         49020                       0   \n",
+       "937  2024-01-05T09:24:00         49020                       0   \n",
+       "938  2024-01-04T12:46:00          4860                       0   \n",
+       "\n",
+       "     Deep Sleep Duration (s)  Light Sleep Duration (s)  Source     glucose  \n",
+       "677                       60                         0  server   99.998476  \n",
+       "678                     6660                     11640  device   94.850168  \n",
+       "679                     6660                     11640  device   94.850168  \n",
+       "680                     6660                     11640  device   94.850168  \n",
+       "681                     6660                     11640  device   94.850168  \n",
+       "..                       ...                       ...     ...         ...  \n",
+       "934                    22020                     11400  server  106.852094  \n",
+       "935                    22020                     11400  server  106.852094  \n",
+       "936                    22020                     11400  server  106.852094  \n",
+       "937                    22020                     11400  server  106.852094  \n",
+       "938                        0                         0  server  106.852094  \n",
+       "\n",
+       "[262 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert column type\n",
+    "glucose_mean_per_day.index = pd.to_datetime(glucose_mean_per_day.index)\n",
+    "p_df['Calendar Date (Local)'] = pd.to_datetime(p_df['Calendar Date (Local)'])\n",
+    "\n",
+    "# Join Garmin Dataset with Glucose Data\n",
+    "p_df_merged = pd.merge(p_df, glucose_mean_per_day, left_on='Calendar Date (Local)', how='inner', right_index=True)\n",
+    "\n",
+    "p_df_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "14caa6da-863f-4b44-81e0-f9d999ce5cd2",
+   "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>User First Name</th>\n",
+       "      <th>Calendar Date (Local)</th>\n",
+       "      <th>Start Time (Local)</th>\n",
+       "      <th>End Time (Local)</th>\n",
+       "      <th>Duration (s)</th>\n",
+       "      <th>Rem Sleep Duration (s)</th>\n",
+       "      <th>Deep Sleep Duration (s)</th>\n",
+       "      <th>Light Sleep Duration (s)</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>glucose</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-27</td>\n",
+       "      <td>2023-12-27T23:04:00</td>\n",
+       "      <td>2023-12-27T23:05:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>server</td>\n",
+       "      <td>99.998476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-29</td>\n",
+       "      <td>2023-12-29T01:30:00</td>\n",
+       "      <td>2023-12-29T08:50:00</td>\n",
+       "      <td>26400</td>\n",
+       "      <td>5220</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>11640</td>\n",
+       "      <td>device</td>\n",
+       "      <td>94.850168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-30</td>\n",
+       "      <td>2023-12-30T02:32:00</td>\n",
+       "      <td>2023-12-30T08:17:00</td>\n",
+       "      <td>20700</td>\n",
+       "      <td>2940</td>\n",
+       "      <td>3960</td>\n",
+       "      <td>10680</td>\n",
+       "      <td>device</td>\n",
+       "      <td>102.743633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>2023-12-31T02:51:00</td>\n",
+       "      <td>2023-12-31T07:42:00</td>\n",
+       "      <td>17460</td>\n",
+       "      <td>3960</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>9120</td>\n",
+       "      <td>device</td>\n",
+       "      <td>101.346390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>2023-12-31T15:00:00</td>\n",
+       "      <td>2024-01-01T00:00:00</td>\n",
+       "      <td>32400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>server</td>\n",
+       "      <td>101.346390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-01</td>\n",
+       "      <td>2024-01-01T15:00:00</td>\n",
+       "      <td>2024-01-02T15:01:00</td>\n",
+       "      <td>86460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>74400</td>\n",
+       "      <td>7500</td>\n",
+       "      <td>server</td>\n",
+       "      <td>104.948331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-03</td>\n",
+       "      <td>2024-01-03T01:45:00</td>\n",
+       "      <td>2024-01-03T08:01:00</td>\n",
+       "      <td>22560</td>\n",
+       "      <td>4020</td>\n",
+       "      <td>6240</td>\n",
+       "      <td>10380</td>\n",
+       "      <td>device</td>\n",
+       "      <td>109.498243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T00:49:00</td>\n",
+       "      <td>2024-01-04T08:05:00</td>\n",
+       "      <td>26160</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>9720</td>\n",
+       "      <td>27480</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T03:43:00</td>\n",
+       "      <td>2024-01-04T08:05:00</td>\n",
+       "      <td>15720</td>\n",
+       "      <td>7200</td>\n",
+       "      <td>2880</td>\n",
+       "      <td>18840</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T05:42:00</td>\n",
+       "      <td>2024-01-04T08:05:00</td>\n",
+       "      <td>8580</td>\n",
+       "      <td>4080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13080</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T07:31:00</td>\n",
+       "      <td>2024-01-04T08:05:00</td>\n",
+       "      <td>2040</td>\n",
+       "      <td>4080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>device</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T19:47:00</td>\n",
+       "      <td>2024-01-04T23:41:00</td>\n",
+       "      <td>14040</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5760</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T08:25:00</td>\n",
+       "      <td>2024-01-04T12:46:00</td>\n",
+       "      <td>15660</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T09:27:00</td>\n",
+       "      <td>2024-01-04T12:46:00</td>\n",
+       "      <td>11940</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T09:49:00</td>\n",
+       "      <td>2024-01-04T12:46:00</td>\n",
+       "      <td>10620</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T10:20:00</td>\n",
+       "      <td>2024-01-04T12:46:00</td>\n",
+       "      <td>8760</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T19:47:00</td>\n",
+       "      <td>2024-01-05T09:24:00</td>\n",
+       "      <td>49020</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22020</td>\n",
+       "      <td>11400</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>P11</td>\n",
+       "      <td>2024-01-04</td>\n",
+       "      <td>2024-01-04T11:25:00</td>\n",
+       "      <td>2024-01-04T12:46:00</td>\n",
+       "      <td>4860</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>server</td>\n",
+       "      <td>106.852094</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   User First Name Calendar Date (Local)   Start Time (Local)  \\\n",
+       "0              P11            2023-12-27  2023-12-27T23:04:00   \n",
+       "1              P11            2023-12-29  2023-12-29T01:30:00   \n",
+       "2              P11            2023-12-30  2023-12-30T02:32:00   \n",
+       "3              P11            2023-12-31  2023-12-31T02:51:00   \n",
+       "4              P11            2023-12-31  2023-12-31T15:00:00   \n",
+       "5              P11            2024-01-01  2024-01-01T15:00:00   \n",
+       "6              P11            2024-01-03  2024-01-03T01:45:00   \n",
+       "7              P11            2024-01-04  2024-01-04T00:49:00   \n",
+       "8              P11            2024-01-04  2024-01-04T03:43:00   \n",
+       "9              P11            2024-01-04  2024-01-04T05:42:00   \n",
+       "10             P11            2024-01-04  2024-01-04T07:31:00   \n",
+       "11             P11            2024-01-04  2024-01-04T19:47:00   \n",
+       "12             P11            2024-01-04  2024-01-04T08:25:00   \n",
+       "13             P11            2024-01-04  2024-01-04T09:27:00   \n",
+       "14             P11            2024-01-04  2024-01-04T09:49:00   \n",
+       "15             P11            2024-01-04  2024-01-04T10:20:00   \n",
+       "16             P11            2024-01-04  2024-01-04T19:47:00   \n",
+       "17             P11            2024-01-04  2024-01-04T11:25:00   \n",
+       "\n",
+       "       End Time (Local)  Duration (s)  Rem Sleep Duration (s)  \\\n",
+       "0   2023-12-27T23:05:00            60                       0   \n",
+       "1   2023-12-29T08:50:00         26400                    5220   \n",
+       "2   2023-12-30T08:17:00         20700                    2940   \n",
+       "3   2023-12-31T07:42:00         17460                    3960   \n",
+       "4   2024-01-01T00:00:00         32400                       0   \n",
+       "5   2024-01-02T15:01:00         86460                       0   \n",
+       "6   2024-01-03T08:01:00         22560                    4020   \n",
+       "7   2024-01-04T08:05:00         26160                    7200   \n",
+       "8   2024-01-04T08:05:00         15720                    7200   \n",
+       "9   2024-01-04T08:05:00          8580                    4080   \n",
+       "10  2024-01-04T08:05:00          2040                    4080   \n",
+       "11  2024-01-04T23:41:00         14040                       0   \n",
+       "12  2024-01-04T12:46:00         15660                       0   \n",
+       "13  2024-01-04T12:46:00         11940                       0   \n",
+       "14  2024-01-04T12:46:00         10620                       0   \n",
+       "15  2024-01-04T12:46:00          8760                       0   \n",
+       "16  2024-01-05T09:24:00         49020                       0   \n",
+       "17  2024-01-04T12:46:00          4860                       0   \n",
+       "\n",
+       "    Deep Sleep Duration (s)  Light Sleep Duration (s)  Source     glucose  \n",
+       "0                        60                         0  server   99.998476  \n",
+       "1                      6660                     11640  device   94.850168  \n",
+       "2                      3960                     10680  device  102.743633  \n",
+       "3                      3360                      9120  device  101.346390  \n",
+       "4                         0                      1380  server  101.346390  \n",
+       "5                     74400                      7500  server  104.948331  \n",
+       "6                      6240                     10380  device  109.498243  \n",
+       "7                      9720                     27480  device  106.852094  \n",
+       "8                      2880                     18840  device  106.852094  \n",
+       "9                         0                     13080  device  106.852094  \n",
+       "10                        0                         0  device  106.852094  \n",
+       "11                     5760                      1020  server  106.852094  \n",
+       "12                        0                        60  server  106.852094  \n",
+       "13                        0                        60  server  106.852094  \n",
+       "14                        0                        60  server  106.852094  \n",
+       "15                        0                         0  server  106.852094  \n",
+       "16                    22020                     11400  server  106.852094  \n",
+       "17                        0                         0  server  106.852094  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Trim columns\n",
+    "#sleep_glucose_df = p_df_merged.loc[:, ['User First Name', 'Calendar Date (Local)', 'Start Time (Local)', 'End Time (Local)', 'Duration (s)', 'Rem Sleep Duration (s)', 'Deep Sleep Duration (s)', 'Light Sleep Duration (s)', 'Source']]\n",
+    "sleep_glucose_df = p_df_merged.drop_duplicates(ignore_index=True)\n",
+    "\n",
+    "sleep_glucose_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "416d1e3b-34e3-421f-8241-8a8dad214188",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['Duration (s)'], label='Sleep Duration', alpha=0.7)\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['glucose'], label='Glucose', alpha=0.7)\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Values')\n",
+    "plt.title('Total Sleep Duration and Glucose over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a5756452-f907-462b-bd02-562e417cad67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a figure and axis\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Plot 'sleepDuration' on the primary y-axis\n",
+    "ax1.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['Duration (s)'], label='Sleep Duration', alpha=0.7, color='b')\n",
+    "ax1.set_xlabel('Date')\n",
+    "ax1.set_ylabel('Sleep Duration', color='b')\n",
+    "ax1.tick_params('y', colors='b')\n",
+    "\n",
+    "# Create a secondary y-axis for 'glucose'\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['glucose'], label='Glucose', alpha=0.7, color='r')\n",
+    "ax2.set_ylabel('Glucose', color='r')\n",
+    "ax2.tick_params('y', colors='r')\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "#plt.xticks(df['Calendar Date (Local)'], rotation=90, ha='right', fontsize=8)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set title\n",
+    "plt.title('Total Sleep Duration and Glucose over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "fig.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9))\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "233ba1e9-9dab-4a4a-b6c3-fa18fd8691e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['Duration (s)'], label='Sleep Duration', alpha=0.7)\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Sleep Duration')\n",
+    "plt.title('Total Sleep Duration over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n",
+    "\n",
+    "# Create a bar chart\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Create a bar chart\n",
+    "plt.bar(sleep_glucose_df['Calendar Date (Local)'], sleep_glucose_df['glucose'], label='Glucose', alpha=0.7)\n",
+    "\n",
+    "# Format the x-axis labels\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Set labels and title\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Glucose Level')\n",
+    "plt.title('Total Glucose over Time')\n",
+    "\n",
+    "# Display legend\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28a6e44e-ac75-4343-a251-c14b4d43d5b1",
+   "metadata": {},
+   "source": [
+    "# Correlation between Sleep Duration and Glucose Level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5437e036-0e99-4119-9d3d-751c71cb7d3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.07510519821225699\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb424cab-a065-408a-8f18-51bf6dff7253",
+   "metadata": {},
+   "source": [
+    "As the correlation coefficient is close to zero, it indicates that there is almost no linear relationship between the two columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "aff8c3f7-ae66-4aa2-9c43-193bf1adf86f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: 0.008524317684081099\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Deep Sleep Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6225363-e04d-4a78-99bb-21544891fcf3",
+   "metadata": {},
+   "source": [
+    "A correlation coefficient of approximately -0.43 indicates a moderate negative correlation between the two variables. The negative sign suggests that as one variable increases, the other tends to decrease, and vice versa. The magnitude of -0.43 suggests that the relationship is stronger than a weak correlation but not extremely strong."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2a595082-8fd5-4d01-895b-e7663a5e298c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.08878599406172011\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Rem Sleep Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "af7877e2-e9ec-452b-84f5-3ba173fb0508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient: -0.003438664102713951\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "column1 = sleep_glucose_df['glucose']\n",
+    "column2 = sleep_glucose_df['Light Sleep Duration (s)']\n",
+    "\n",
+    "# Calculate the correlation coefficient\n",
+    "correlation_coefficient = column1.corr(column2)\n",
+    "\n",
+    "print(f'Correlation Coefficient: {correlation_coefficient}')\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "sns.scatterplot(x=column1, y=column2)\n",
+    "\n",
+    "# Add a regression line and correlation coefficient\n",
+    "sns.regplot(x=column1, y=column2, ci=None, line_kws={'color': 'red'})\n",
+    "plt.title(f'Correlation: {column1.corr(column2):.2f}')\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}