diff --git a/README.md b/README.md
index ebfe1aa..7be85c7 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,8 @@
 # cds-1011-health-data-analysis
 
-Proof of performance 1 in basics of data science.
\ No newline at end of file
+Proof of performance 1 in basics of data science.
+
+This study examines the relationship between training time and sleep quality based on activity and sleep data from a test subject over a period of 52 weeks.
+The analysis shows that activities that took place less than four hours before bedtime have a measurable, albeit weak, influence on sleep quality.
+
+It uses Python with pandas, matplotlib and seaborn to analyze the dataset.
\ No newline at end of file
diff --git a/activity_sleep.ipynb b/activity_sleep.ipynb
index 33e3aba..77e2f17 100644
--- a/activity_sleep.ipynb
+++ b/activity_sleep.ipynb
@@ -10,20 +10,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": null,
    "id": "5b2b0060",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import datetime\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 292,
+   "execution_count": 4,
    "id": "52f55dde",
    "metadata": {},
    "outputs": [],
@@ -77,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 293,
+   "execution_count": 5,
    "id": "826e5af0",
    "metadata": {},
    "outputs": [],
@@ -111,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 294,
+   "execution_count": 6,
    "id": "2cbef16d",
    "metadata": {},
    "outputs": [],
@@ -140,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": 7,
    "id": "d3ec20f3",
    "metadata": {},
    "outputs": [],
@@ -170,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 296,
+   "execution_count": 8,
    "id": "845cc713",
    "metadata": {},
    "outputs": [],
@@ -199,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 9,
    "id": "05da5fe7",
    "metadata": {},
    "outputs": [],
@@ -1343,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 10,
    "id": "41080d47",
    "metadata": {},
    "outputs": [
@@ -1378,7 +1377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": 11,
    "id": "cf54e6c7",
    "metadata": {},
    "outputs": [
@@ -1417,13 +1416,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": 14,
    "id": "e2246df2",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1435,8 +1434,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of blue points: 39\n",
-      "Number of red points: 81\n"
+      "Number of blue points: 67\n",
+      "Number of red points: 184\n"
      ]
     }
    ],
@@ -1444,25 +1443,25 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "filtered_df_combined_4h_before_sleep = df_combine_activities_4_hours_before_sleep[\n",
-    "    (df_combine_activities_4_hours_before_sleep[\"activity_calories\"].notna()) &\n",
-    "    (df_combine_activities_4_hours_before_sleep['sleep_duration_needed_delta'].abs() < pd.Timedelta(hours=0.5))\n",
+    "    (df_combine_activities_4_hours_before_sleep[\"activity_calories\"].notna()) #&\n",
+    "    #(df_combine_activities_4_hours_before_sleep['sleep_duration_needed_delta'].abs() < pd.Timedelta(hours=0.5))\n",
     "]\n",
     "plt.figure(figsize=(8, 6))\n",
     "plt.scatter(\n",
     "    filtered_df_combined_4h_before_sleep['activity_heart_rate_average'],\n",
-    "    filtered_df_combined_4h_before_sleep['sleep_hrv_status'],\n",
+    "    filtered_df_combined_4h_before_sleep['sleep_score'],\n",
     "    alpha=0.7,\n",
     "    color='blue'\n",
     ")\n",
     "filtered_df_combined_more_than_4h_before_sleep = df_combined[\n",
     "     (df_combined[\"activity_calories\"].notna()) &\n",
-    "     (df_combined[\"bedtime_activity_ending_delta\"] > pd.Timedelta(hours=4))&\n",
-    "     (df_combined['sleep_duration_needed_delta'].abs() < pd.Timedelta(hours=0.5))\n",
+    "     (df_combined[\"bedtime_activity_ending_delta\"] > pd.Timedelta(hours=4))#&\n",
+    "     #(df_combined['sleep_duration_needed_delta'].abs() < pd.Timedelta(hours=0.5))\n",
     " ]\n",
     "\n",
     "plt.scatter(\n",
     "    filtered_df_combined_more_than_4h_before_sleep['activity_heart_rate_average'],\n",
-    "    filtered_df_combined_more_than_4h_before_sleep['sleep_hrv_status'],\n",
+    "    filtered_df_combined_more_than_4h_before_sleep['sleep_score'],\n",
     "    alpha=0.7,\n",
     "    color='red',\n",
     ")\n",
diff --git a/data/cleaned/combined_activities_sleep.csv b/data/cleaned/combined_activities_sleep.csv
new file mode 100644
index 0000000..3f62c9b
--- /dev/null
+++ b/data/cleaned/combined_activities_sleep.csv
@@ -0,0 +1,365 @@
+date,activity_calories,activity_duration,activity_heart_rate_average,activity_pace_average,activity_breathing_rate_average,activity_starting_time,activity_duration_seconds,activity_ending_time,sleep_score,sleep_resting_heart_rate,sleep_breathing_rate,sleep_hrv_status,sleep_duration,sleep_duration_needed,sleep_bedtime,sleep_duration_seconds,sleep_duration_needed_seconds,sleep_duration_needed_delta,sleep_duration_needed_delta_seconds,bedtime_activity_ending_delta
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diff --git a/eda.ipynb b/eda.ipynb
deleted file mode 100644
index 88b654e..0000000
--- a/eda.ipynb
+++ /dev/null
@@ -1,1556 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f6f0a842",
-   "metadata": {},
-   "source": [
-    "# Exploratory Data Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "386dcd6d",
-   "metadata": {},
-   "source": [
-    "import the required libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f9b0ae4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#importing the required libraries\n",
-    "import numpy as np \n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import os  \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "1c92d2c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the datasets\n",
-    "df_activities = pd.read_csv(\"all_activities.csv\")\n",
-    "df_sleep = pd.read_csv(\"sleep.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "4f18cf6a",
-   "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>Aktivitätstyp</th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Favorit</th>\n",
-       "      <th>Titel</th>\n",
-       "      <th>Distanz</th>\n",
-       "      <th>Kalorien</th>\n",
-       "      <th>Zeit</th>\n",
-       "      <th>Ø Herzfrequenz</th>\n",
-       "      <th>Maximale Herzfrequenz</th>\n",
-       "      <th>Aerober TE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Maximale Atemfrequenz</th>\n",
-       "      <th>Stressänderung</th>\n",
-       "      <th>Stress: Start</th>\n",
-       "      <th>Stress: Ende</th>\n",
-       "      <th>Ø Stress</th>\n",
-       "      <th>Maximaler Stress</th>\n",
-       "      <th>Zeit in Bewegung</th>\n",
-       "      <th>Verstrichene Zeit</th>\n",
-       "      <th>Minimale Höhe</th>\n",
-       "      <th>Maximale Höhe</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-21 09:53:57</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Berlin - BMW Berlin Marathon (42.195 km)</td>\n",
-       "      <td>42.65</td>\n",
-       "      <td>2,817</td>\n",
-       "      <td>04:35:15</td>\n",
-       "      <td>148</td>\n",
-       "      <td>165</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>41</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>04:34:55</td>\n",
-       "      <td>04:35:15</td>\n",
-       "      <td>33</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-20 15:18:50</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Berlin Laufen</td>\n",
-       "      <td>3.72</td>\n",
-       "      <td>246</td>\n",
-       "      <td>00:20:13</td>\n",
-       "      <td>144</td>\n",
-       "      <td>161</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:20:12</td>\n",
-       "      <td>00:20:13</td>\n",
-       "      <td>40</td>\n",
-       "      <td>47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Virtuelles Radfahren</td>\n",
-       "      <td>2025-09-19 12:31:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Zwift - Renewal on Bridges and Boardwalks in M...</td>\n",
-       "      <td>12.81</td>\n",
-       "      <td>210</td>\n",
-       "      <td>00:30:18</td>\n",
-       "      <td>116</td>\n",
-       "      <td>134</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:30:17</td>\n",
-       "      <td>00:30:17</td>\n",
-       "      <td>4</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-15 11:16:13</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Rüthi Laufen</td>\n",
-       "      <td>5.60</td>\n",
-       "      <td>350</td>\n",
-       "      <td>00:30:39</td>\n",
-       "      <td>133</td>\n",
-       "      <td>151</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:30:37</td>\n",
-       "      <td>00:30:39</td>\n",
-       "      <td>421</td>\n",
-       "      <td>437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-13 10:51:36</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Rüthi Laufen</td>\n",
-       "      <td>9.01</td>\n",
-       "      <td>549</td>\n",
-       "      <td>00:44:56</td>\n",
-       "      <td>144</td>\n",
-       "      <td>171</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>41</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:44:45</td>\n",
-       "      <td>00:44:56</td>\n",
-       "      <td>421</td>\n",
-       "      <td>435</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 52 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Aktivitätstyp                Datum  Favorit  \\\n",
-       "0                Laufen  2025-09-21 09:53:57    False   \n",
-       "1                Laufen  2025-09-20 15:18:50    False   \n",
-       "2  Virtuelles Radfahren  2025-09-19 12:31:00    False   \n",
-       "3                Laufen  2025-09-15 11:16:13    False   \n",
-       "4                Laufen  2025-09-13 10:51:36    False   \n",
-       "\n",
-       "                                               Titel Distanz Kalorien  \\\n",
-       "0           Berlin - BMW Berlin Marathon (42.195 km)   42.65    2,817   \n",
-       "1                                      Berlin Laufen    3.72      246   \n",
-       "2  Zwift - Renewal on Bridges and Boardwalks in M...   12.81      210   \n",
-       "3                                       Rüthi Laufen    5.60      350   \n",
-       "4                                       Rüthi Laufen    9.01      549   \n",
-       "\n",
-       "       Zeit Ø Herzfrequenz Maximale Herzfrequenz Aerober TE  ...  \\\n",
-       "0  04:35:15            148                   165        5.0  ...   \n",
-       "1  00:20:13            144                   161        2.8  ...   \n",
-       "2  00:30:18            116                   134        1.5  ...   \n",
-       "3  00:30:39            133                   151        2.8  ...   \n",
-       "4  00:44:56            144                   171        3.5  ...   \n",
-       "\n",
-       "  Maximale Atemfrequenz Stressänderung Stress: Start Stress: Ende Ø Stress  \\\n",
-       "0                    41             --            --           --       --   \n",
-       "1                    38             --            --           --       --   \n",
-       "2                    --             --            --           --       --   \n",
-       "3                    39             --            --           --       --   \n",
-       "4                    41             --            --           --       --   \n",
-       "\n",
-       "  Maximaler Stress Zeit in Bewegung Verstrichene Zeit Minimale Höhe  \\\n",
-       "0               --         04:34:55          04:35:15            33   \n",
-       "1               --         00:20:12          00:20:13            40   \n",
-       "2               --         00:30:17          00:30:17             4   \n",
-       "3               --         00:30:37          00:30:39           421   \n",
-       "4               --         00:44:45          00:44:56           421   \n",
-       "\n",
-       "  Maximale Höhe  \n",
-       "0            56  \n",
-       "1            47  \n",
-       "2            59  \n",
-       "3           437  \n",
-       "4           435  \n",
-       "\n",
-       "[5 rows x 52 columns]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check activities data\n",
-    "df_activities.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "e65aa687",
-   "metadata": {},
-   "outputs": [
-    {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Sleep Score 7 Tage</th>\n",
-       "      <th>Score</th>\n",
-       "      <th>Ruheherzfrequenz</th>\n",
-       "      <th>Body Battery</th>\n",
-       "      <th>Pulsoximeter</th>\n",
-       "      <th>Atmung</th>\n",
-       "      <th>HFV-Status</th>\n",
-       "      <th>Qualität</th>\n",
-       "      <th>Dauer</th>\n",
-       "      <th>Schlafbedürfnis</th>\n",
-       "      <th>Schlafenszeit</th>\n",
-       "      <th>Aufstehzeit;;;;;;;;;;;</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-30</td>\n",
-       "      <td>77</td>\n",
-       "      <td>44</td>\n",
-       "      <td>55</td>\n",
-       "      <td>--</td>\n",
-       "      <td>11.38</td>\n",
-       "      <td>83</td>\n",
-       "      <td>Ausreichend</td>\n",
-       "      <td>6h 47min</td>\n",
-       "      <td>7h 0min</td>\n",
-       "      <td>11:22 PM</td>\n",
-       "      <td>6:09 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-29</td>\n",
-       "      <td>73</td>\n",
-       "      <td>46</td>\n",
-       "      <td>62</td>\n",
-       "      <td>--</td>\n",
-       "      <td>12</td>\n",
-       "      <td>84</td>\n",
-       "      <td>Ausreichend</td>\n",
-       "      <td>9h 6min</td>\n",
-       "      <td>8h 40min</td>\n",
-       "      <td>10:52 PM</td>\n",
-       "      <td>8:02 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-28</td>\n",
-       "      <td>34</td>\n",
-       "      <td>47</td>\n",
-       "      <td>21</td>\n",
-       "      <td>--</td>\n",
-       "      <td>14.96</td>\n",
-       "      <td>84</td>\n",
-       "      <td>Schlecht</td>\n",
-       "      <td>6h 34min</td>\n",
-       "      <td>7h 40min</td>\n",
-       "      <td>12:34 AM</td>\n",
-       "      <td>8:09 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-27</td>\n",
-       "      <td>93</td>\n",
-       "      <td>41</td>\n",
-       "      <td>67</td>\n",
-       "      <td>--</td>\n",
-       "      <td>11.13</td>\n",
-       "      <td>92</td>\n",
-       "      <td>Ausgezeichnet</td>\n",
-       "      <td>8h 32min</td>\n",
-       "      <td>7h 40min</td>\n",
-       "      <td>10:39 PM</td>\n",
-       "      <td>7:20 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-26</td>\n",
-       "      <td>97</td>\n",
-       "      <td>42</td>\n",
-       "      <td>71</td>\n",
-       "      <td>--</td>\n",
-       "      <td>11.15</td>\n",
-       "      <td>90</td>\n",
-       "      <td>Ausgezeichnet</td>\n",
-       "      <td>7h 50min</td>\n",
-       "      <td>7h 40min</td>\n",
-       "      <td>10:07 PM</td>\n",
-       "      <td>5:57 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Sleep Score 7 Tage Score Ruheherzfrequenz Body Battery Pulsoximeter Atmung  \\\n",
-       "0         2025-09-30    77               44           55           --  11.38   \n",
-       "1         2025-09-29    73               46           62           --     12   \n",
-       "2         2025-09-28    34               47           21           --  14.96   \n",
-       "3         2025-09-27    93               41           67           --  11.13   \n",
-       "4         2025-09-26    97               42           71           --  11.15   \n",
-       "\n",
-       "  HFV-Status       Qualität     Dauer Schlafbedürfnis Schlafenszeit  \\\n",
-       "0         83    Ausreichend  6h 47min         7h 0min      11:22 PM   \n",
-       "1         84    Ausreichend   9h 6min        8h 40min      10:52 PM   \n",
-       "2         84       Schlecht  6h 34min        7h 40min      12:34 AM   \n",
-       "3         92  Ausgezeichnet  8h 32min        7h 40min      10:39 PM   \n",
-       "4         90  Ausgezeichnet  7h 50min        7h 40min      10:07 PM   \n",
-       "\n",
-       "  Aufstehzeit;;;;;;;;;;;  \n",
-       "0     6:09 AM;;;;;;;;;;;  \n",
-       "1     8:02 AM;;;;;;;;;;;  \n",
-       "2     8:09 AM;;;;;;;;;;;  \n",
-       "3     7:20 AM;;;;;;;;;;;  \n",
-       "4     5:57 AM;;;;;;;;;;;  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check sleep data\n",
-    "df_sleep.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "2b832a91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# change the first column name to Datum\n",
-    "df_sleep.rename(columns={df_sleep.columns[0]: 'Datum'}, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "70fe281d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Score</th>\n",
-       "      <th>Ruheherzfrequenz</th>\n",
-       "      <th>Body Battery</th>\n",
-       "      <th>Pulsoximeter</th>\n",
-       "      <th>Atmung</th>\n",
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-       "      <th>Aufstehzeit;;;;;;;;;;;</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-30</td>\n",
-       "      <td>77</td>\n",
-       "      <td>44</td>\n",
-       "      <td>55</td>\n",
-       "      <td>--</td>\n",
-       "      <td>11.38</td>\n",
-       "      <td>83</td>\n",
-       "      <td>Ausreichend</td>\n",
-       "      <td>6h 47min</td>\n",
-       "      <td>7h 0min</td>\n",
-       "      <td>11:22 PM</td>\n",
-       "      <td>6:09 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-29</td>\n",
-       "      <td>73</td>\n",
-       "      <td>46</td>\n",
-       "      <td>62</td>\n",
-       "      <td>--</td>\n",
-       "      <td>12</td>\n",
-       "      <td>84</td>\n",
-       "      <td>Ausreichend</td>\n",
-       "      <td>9h 6min</td>\n",
-       "      <td>8h 40min</td>\n",
-       "      <td>10:52 PM</td>\n",
-       "      <td>8:02 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-28</td>\n",
-       "      <td>34</td>\n",
-       "      <td>47</td>\n",
-       "      <td>21</td>\n",
-       "      <td>--</td>\n",
-       "      <td>14.96</td>\n",
-       "      <td>84</td>\n",
-       "      <td>Schlecht</td>\n",
-       "      <td>6h 34min</td>\n",
-       "      <td>7h 40min</td>\n",
-       "      <td>12:34 AM</td>\n",
-       "      <td>8:09 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-27</td>\n",
-       "      <td>93</td>\n",
-       "      <td>41</td>\n",
-       "      <td>67</td>\n",
-       "      <td>--</td>\n",
-       "      <td>11.13</td>\n",
-       "      <td>92</td>\n",
-       "      <td>Ausgezeichnet</td>\n",
-       "      <td>8h 32min</td>\n",
-       "      <td>7h 40min</td>\n",
-       "      <td>10:39 PM</td>\n",
-       "      <td>7:20 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-26</td>\n",
-       "      <td>97</td>\n",
-       "      <td>42</td>\n",
-       "      <td>71</td>\n",
-       "      <td>--</td>\n",
-       "      <td>11.15</td>\n",
-       "      <td>90</td>\n",
-       "      <td>Ausgezeichnet</td>\n",
-       "      <td>7h 50min</td>\n",
-       "      <td>7h 40min</td>\n",
-       "      <td>10:07 PM</td>\n",
-       "      <td>5:57 AM;;;;;;;;;;;</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Datum Score Ruheherzfrequenz Body Battery Pulsoximeter Atmung  \\\n",
-       "0  2025-09-30    77               44           55           --  11.38   \n",
-       "1  2025-09-29    73               46           62           --     12   \n",
-       "2  2025-09-28    34               47           21           --  14.96   \n",
-       "3  2025-09-27    93               41           67           --  11.13   \n",
-       "4  2025-09-26    97               42           71           --  11.15   \n",
-       "\n",
-       "  HFV-Status       Qualität     Dauer Schlafbedürfnis Schlafenszeit  \\\n",
-       "0         83    Ausreichend  6h 47min         7h 0min      11:22 PM   \n",
-       "1         84    Ausreichend   9h 6min        8h 40min      10:52 PM   \n",
-       "2         84       Schlecht  6h 34min        7h 40min      12:34 AM   \n",
-       "3         92  Ausgezeichnet  8h 32min        7h 40min      10:39 PM   \n",
-       "4         90  Ausgezeichnet  7h 50min        7h 40min      10:07 PM   \n",
-       "\n",
-       "  Aufstehzeit;;;;;;;;;;;  \n",
-       "0     6:09 AM;;;;;;;;;;;  \n",
-       "1     8:02 AM;;;;;;;;;;;  \n",
-       "2     8:09 AM;;;;;;;;;;;  \n",
-       "3     7:20 AM;;;;;;;;;;;  \n",
-       "4     5:57 AM;;;;;;;;;;;  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check the sleep data again\n",
-    "df_sleep.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "daebf9ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Aktivitätstyp</th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Favorit</th>\n",
-       "      <th>Titel</th>\n",
-       "      <th>Distanz</th>\n",
-       "      <th>Kalorien</th>\n",
-       "      <th>Zeit</th>\n",
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-       "      <th>Aerober TE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Maximale Atemfrequenz</th>\n",
-       "      <th>Stressänderung</th>\n",
-       "      <th>Stress: Start</th>\n",
-       "      <th>Stress: Ende</th>\n",
-       "      <th>Ø Stress</th>\n",
-       "      <th>Maximaler Stress</th>\n",
-       "      <th>Zeit in Bewegung</th>\n",
-       "      <th>Verstrichene Zeit</th>\n",
-       "      <th>Minimale Höhe</th>\n",
-       "      <th>Maximale Höhe</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-21 09:53:57</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Berlin - BMW Berlin Marathon (42.195 km)</td>\n",
-       "      <td>42.65</td>\n",
-       "      <td>2,817</td>\n",
-       "      <td>04:35:15</td>\n",
-       "      <td>148</td>\n",
-       "      <td>165</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>41</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>04:34:55</td>\n",
-       "      <td>04:35:15</td>\n",
-       "      <td>33</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-20 15:18:50</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Berlin Laufen</td>\n",
-       "      <td>3.72</td>\n",
-       "      <td>246</td>\n",
-       "      <td>00:20:13</td>\n",
-       "      <td>144</td>\n",
-       "      <td>161</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:20:12</td>\n",
-       "      <td>00:20:13</td>\n",
-       "      <td>40</td>\n",
-       "      <td>47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Virtuelles Radfahren</td>\n",
-       "      <td>2025-09-19 12:31:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Zwift - Renewal on Bridges and Boardwalks in M...</td>\n",
-       "      <td>12.81</td>\n",
-       "      <td>210</td>\n",
-       "      <td>00:30:18</td>\n",
-       "      <td>116</td>\n",
-       "      <td>134</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:30:17</td>\n",
-       "      <td>00:30:17</td>\n",
-       "      <td>4</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-15 11:16:13</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Rüthi Laufen</td>\n",
-       "      <td>5.60</td>\n",
-       "      <td>350</td>\n",
-       "      <td>00:30:39</td>\n",
-       "      <td>133</td>\n",
-       "      <td>151</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:30:37</td>\n",
-       "      <td>00:30:39</td>\n",
-       "      <td>421</td>\n",
-       "      <td>437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Laufen</td>\n",
-       "      <td>2025-09-13 10:51:36</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Rüthi Laufen</td>\n",
-       "      <td>9.01</td>\n",
-       "      <td>549</td>\n",
-       "      <td>00:44:56</td>\n",
-       "      <td>144</td>\n",
-       "      <td>171</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>41</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>--</td>\n",
-       "      <td>00:44:45</td>\n",
-       "      <td>00:44:56</td>\n",
-       "      <td>421</td>\n",
-       "      <td>435</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 52 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Aktivitätstyp                Datum  Favorit  \\\n",
-       "0                Laufen  2025-09-21 09:53:57    False   \n",
-       "1                Laufen  2025-09-20 15:18:50    False   \n",
-       "2  Virtuelles Radfahren  2025-09-19 12:31:00    False   \n",
-       "3                Laufen  2025-09-15 11:16:13    False   \n",
-       "4                Laufen  2025-09-13 10:51:36    False   \n",
-       "\n",
-       "                                               Titel Distanz Kalorien  \\\n",
-       "0           Berlin - BMW Berlin Marathon (42.195 km)   42.65    2,817   \n",
-       "1                                      Berlin Laufen    3.72      246   \n",
-       "2  Zwift - Renewal on Bridges and Boardwalks in M...   12.81      210   \n",
-       "3                                       Rüthi Laufen    5.60      350   \n",
-       "4                                       Rüthi Laufen    9.01      549   \n",
-       "\n",
-       "       Zeit Ø Herzfrequenz Maximale Herzfrequenz Aerober TE  ...  \\\n",
-       "0  04:35:15            148                   165        5.0  ...   \n",
-       "1  00:20:13            144                   161        2.8  ...   \n",
-       "2  00:30:18            116                   134        1.5  ...   \n",
-       "3  00:30:39            133                   151        2.8  ...   \n",
-       "4  00:44:56            144                   171        3.5  ...   \n",
-       "\n",
-       "  Maximale Atemfrequenz Stressänderung Stress: Start Stress: Ende Ø Stress  \\\n",
-       "0                    41             --            --           --       --   \n",
-       "1                    38             --            --           --       --   \n",
-       "2                    --             --            --           --       --   \n",
-       "3                    39             --            --           --       --   \n",
-       "4                    41             --            --           --       --   \n",
-       "\n",
-       "  Maximaler Stress Zeit in Bewegung Verstrichene Zeit Minimale Höhe  \\\n",
-       "0               --         04:34:55          04:35:15            33   \n",
-       "1               --         00:20:12          00:20:13            40   \n",
-       "2               --         00:30:17          00:30:17             4   \n",
-       "3               --         00:30:37          00:30:39           421   \n",
-       "4               --         00:44:45          00:44:56           421   \n",
-       "\n",
-       "  Maximale Höhe  \n",
-       "0            56  \n",
-       "1            47  \n",
-       "2            59  \n",
-       "3           437  \n",
-       "4           435  \n",
-       "\n",
-       "[5 rows x 52 columns]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_activities.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "119e7f65",
-   "metadata": {},
-   "source": [
-    "Um die Frage \"Wie sich der Sport auf die Ruheherzfrequenz (RHF) auswirkt\" zu beantworten, erstellen wir einen neuen, kombinierten Datensatz.\n",
-    "\n",
-    "Dafür nutzen wir:\n",
-    "\n",
-    "Aus sleep.csv: Das Date (Datum) und die RHR (Ruheherzfrequenz). Dies ist der Wert, den wir messen möchten.\n",
-    "\n",
-    "Aus all-activities.csv: Den Activity_Timestamp (Zeitstempel der Aktivität) und die Calorie (Kalorienverbrauch).\n",
-    "\n",
-    "Zuerst summieren wir die Calorie-Werte pro Datum in der all-activities.csv, um die tägliche Gesamtaktivität zu erhalten. Anschließend verbinden wir diese täglichen Aktivitätsdaten mit den RHF-Werten aus der sleep.csv über das gemeinsame Datum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "411dec6a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Ruheherzfrequenz</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-30</td>\n",
-       "      <td>44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-29</td>\n",
-       "      <td>46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-28</td>\n",
-       "      <td>47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-27</td>\n",
-       "      <td>41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-26</td>\n",
-       "      <td>42</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Datum Ruheherzfrequenz\n",
-       "0  2025-09-30               44\n",
-       "1  2025-09-29               46\n",
-       "2  2025-09-28               47\n",
-       "3  2025-09-27               41\n",
-       "4  2025-09-26               42"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Pull the Datum and Ruheherzfrequenz from sleep.csv\n",
-    "df_sleep_filtered = df_sleep[['Datum', 'Ruheherzfrequenz']]\n",
-    "df_sleep_filtered.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "8876cc1f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Kalorien</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-21 09:53:57</td>\n",
-       "      <td>2,817</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-20 15:18:50</td>\n",
-       "      <td>246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-19 12:31:00</td>\n",
-       "      <td>210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-15 11:16:13</td>\n",
-       "      <td>350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-13 10:51:36</td>\n",
-       "      <td>549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Datum Kalorien\n",
-       "0  2025-09-21 09:53:57    2,817\n",
-       "1  2025-09-20 15:18:50      246\n",
-       "2  2025-09-19 12:31:00      210\n",
-       "3  2025-09-15 11:16:13      350\n",
-       "4  2025-09-13 10:51:36      549"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Pull the Datum and Kalorien from all-activities.csv\n",
-    "df_activities_filtered = df_activities[['Datum', 'Kalorien']]\n",
-    "df_activities_filtered.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "54d3116d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\hizlanarif\\AppData\\Local\\Temp\\ipykernel_3384\\2850544847.py:2: 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",
-      "  df_activities_filtered['Datum'] = pd.to_datetime(df_activities_filtered['Datum']).dt.date\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Datum</th>\n",
-       "      <th>Kalorien</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-21</td>\n",
-       "      <td>2,817</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-20</td>\n",
-       "      <td>246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-19</td>\n",
-       "      <td>210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-15</td>\n",
-       "      <td>350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-13</td>\n",
-       "      <td>549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Datum Kalorien\n",
-       "0  2025-09-21    2,817\n",
-       "1  2025-09-20      246\n",
-       "2  2025-09-19      210\n",
-       "3  2025-09-15      350\n",
-       "4  2025-09-13      549"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# for the Datum column, get rid of the time part and keep only the date part\n",
-    "df_activities_filtered['Datum'] = pd.to_datetime(df_activities_filtered['Datum']).dt.date\n",
-    "df_activities_filtered.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "f26dc1cf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\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>Datum</th>\n",
-       "      <th>Kalorien</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-21</td>\n",
-       "      <td>2817</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-20</td>\n",
-       "      <td>246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-19</td>\n",
-       "      <td>210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-15</td>\n",
-       "      <td>350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-13</td>\n",
-       "      <td>549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Datum  Kalorien\n",
-       "0 2025-09-21      2817\n",
-       "1 2025-09-20       246\n",
-       "2 2025-09-19       210\n",
-       "3 2025-09-15       350\n",
-       "4 2025-09-13       549"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_activities_filtered.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "2caa80da",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Datum       object\n",
-       "Kalorien    object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check the data types\n",
-    "df_activities_filtered.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "503b1ae9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\hizlanarif\\AppData\\Local\\Temp\\ipykernel_3384\\2726110581.py:2: 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",
-      "  df_activities_filtered['Datum'] = pd.to_datetime(df_activities_filtered['Datum'])\n",
-      "C:\\Users\\hizlanarif\\AppData\\Local\\Temp\\ipykernel_3384\\2726110581.py:4: 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",
-      "  df_activities_filtered['Kalorien'] = df_activities_filtered['Kalorien'].str.replace(',', '')\n",
-      "C:\\Users\\hizlanarif\\AppData\\Local\\Temp\\ipykernel_3384\\2726110581.py:6: 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",
-      "  df_activities_filtered['Kalorien'] = df_activities_filtered['Kalorien'].replace('--', '0')\n",
-      "C:\\Users\\hizlanarif\\AppData\\Local\\Temp\\ipykernel_3384\\2726110581.py:8: 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",
-      "  df_activities_filtered['Kalorien'] = df_activities_filtered['Kalorien'].astype(int)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Datum       datetime64[ns]\n",
-       "Kalorien             int64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# change the data type of Datum to datetime\n",
-    "df_activities_filtered['Datum'] = pd.to_datetime(df_activities_filtered['Datum'])\n",
-    "#get rid of commas in Kalorien column\n",
-    "df_activities_filtered['Kalorien'] = df_activities_filtered['Kalorien'].str.replace(',', '')\n",
-    "# replace all \"--\" values with 0\n",
-    "df_activities_filtered['Kalorien'] = df_activities_filtered['Kalorien'].replace('--', '0')\n",
-    "# change the data type of Kalorien to integer\n",
-    "df_activities_filtered['Kalorien'] = df_activities_filtered['Kalorien'].astype(int)\n",
-    "df_activities_filtered.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "3ca1d34a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#write this cleaned data to a new csv file\n",
-    "df_activities_filtered.to_csv('cleaned_activities.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "8e6a8924",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#write the cleaned sleep data to new csv files\n",
-    "df_sleep_filtered.to_csv('cleaned_sleep.csv', index=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "c19c4423",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check if each date is unique in df_activities_filtered\n",
-    "df_activities_filtered['Datum'].is_unique"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "8c8dfaa6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\hizlanarif\\AppData\\Local\\Temp\\ipykernel_3384\\2358408937.py:2: 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",
-      "  df_activities_filtered['Datum'] = pd.to_datetime(df_activities_filtered['Datum'], format='%d.%m.%Y').dt.date\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>Datum</th>\n",
-       "      <th>Kalorien</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-09-21</td>\n",
-       "      <td>2817</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-09-20</td>\n",
-       "      <td>246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-09-19</td>\n",
-       "      <td>210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-09-15</td>\n",
-       "      <td>350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-09-13</td>\n",
-       "      <td>549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Datum  Kalorien\n",
-       "0  2025-09-21      2817\n",
-       "1  2025-09-20       246\n",
-       "2  2025-09-19       210\n",
-       "3  2025-09-15       350\n",
-       "4  2025-09-13       549"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# change the Datum column to day month year format\n",
-    "df_activities_filtered['Datum'] = pd.to_datetime(df_activities_filtered['Datum'], format='%d.%m.%Y').dt.date\n",
-    "df_activities_filtered.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d0dd445",
-   "metadata": {},
-   "source": [
-    "We see that each value is not unique in Datum which suggests that for some days there are more than one entry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "31a6f98d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sum the Kalorien values per Date in all-activities.csv to get daily total activity\n",
-    "df_activities_daily = df_activities_filtered.groupby('Datum').sum().reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "864c302b",
-   "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>Datum</th>\n",
-       "      <th>Kalorien</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-07-07</td>\n",
-       "      <td>432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2021-07-08</td>\n",
-       "      <td>544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2021-07-12</td>\n",
-       "      <td>441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2021-07-13</td>\n",
-       "      <td>384</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2021-08-20</td>\n",
-       "      <td>891</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Datum  Kalorien\n",
-       "0 2021-07-07       432\n",
-       "1 2021-07-08       544\n",
-       "2 2021-07-12       441\n",
-       "3 2021-07-13       384\n",
-       "4 2021-08-20       891"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_activities_daily.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "b4c67f8e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#check if the dates are unique now\n",
-    "df_activities_daily['Datum'].is_unique"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3965ac4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sum the Calorie values per Date in all-activities.csv to get daily total activity\n",
-    "df_activities_daily = df_activities_filtered.resample('D', on='Datum').sum().reset_index()\n",
-    "# Merge the daily activity data with the RHR data from sleep.csv on the Date\n",
-    "df_combined = pd.merge(df_activities_daily, df_sleep[['Datum', 'Ruheherzfrequenz']], left_on='Datum', right_on='Datum', how='inner')\n",
-    "df_combined.head()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
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- "nbformat_minor": 5
-}