diff --git a/notebook.ipynb b/notebook.ipynb
index 1d743f3..396c552 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 34,
    "id": "63f43af5",
    "metadata": {},
    "outputs": [],
@@ -15,34 +15,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 35,
    "id": "b0ee2af1",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Index(['T(sec)', 'PHASE', 'HR(bpm)', 'VO2(ml/min)', 'VCO2(ml/min)', 'RER',\n",
-       "       'VE(l/min)', 'FEO2', 'FECO2', 'FETO2', 'FETCO2', 'PETO2 (mmHg)',\n",
-       "       'PETCO2(mmHg)', 'FIO2', 'FICO2', 'VT(l)', 'BF(bpm)', 'EE(kcal/day)',\n",
-       "       'EE(kcal/min)', 'CARBS(kcal)', 'CARBS(%)', 'FAT(kcal)', 'FAT(%)', 'MET',\n",
-       "       'CUMULATIVE EE(kcal)', 'BP(kPa)', 'Watts', 'Speed'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column names and data types after conversion:\n",
+      "T(sec)                 float64\n",
+      "PHASE                   object\n",
+      "HR(bpm)                float64\n",
+      "VO2(ml/min)            float64\n",
+      "VCO2(ml/min)           float64\n",
+      "RER                    float64\n",
+      "VE(l/min)              float64\n",
+      "FEO2                   float64\n",
+      "FECO2                  float64\n",
+      "FETO2                    int64\n",
+      "FETCO2                   int64\n",
+      "PETO2 (mmHg)             int64\n",
+      "PETCO2(mmHg)             int64\n",
+      "FIO2                   float64\n",
+      "FICO2                  float64\n",
+      "VT(l)                  float64\n",
+      "BF(bpm)                float64\n",
+      "EE(kcal/day)           float64\n",
+      "EE(kcal/min)           float64\n",
+      "CARBS(kcal)            float64\n",
+      "CARBS(%)               float64\n",
+      "FAT(kcal)              float64\n",
+      "FAT(%)                 float64\n",
+      "MET                    float64\n",
+      "CUMULATIVE EE(kcal)    float64\n",
+      "BP(kPa)                float64\n",
+      "Watts                  float64\n",
+      "Speed                  float64\n",
+      "dtype: object\n",
+      "\n",
+      "First few rows:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3000/3236648660.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
+      "  df = df.apply(pd.to_numeric, errors='ignore')\n"
+     ]
     }
    ],
    "source": [
     "df = pd.read_csv('data/Pnoe_20250729_1550-Moran_Keirstyn.csv', delimiter=';')\n",
-    "df.columns"
+    "# Convert all columns to numeric where possible, coercing errors to NaN\n",
+    "df = df.apply(pd.to_numeric, errors='ignore')\n",
+    "\n",
+    "# Display column names and data types to verify conversion\n",
+    "print(\"Column names and data types after conversion:\")\n",
+    "print(df.dtypes)\n",
+    "print(\"\\nFirst few rows:\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 36,
    "id": "98da3827",
    "metadata": {},
    "outputs": [
@@ -62,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 37,
    "id": "ef8bc7ac",
    "metadata": {},
    "outputs": [
@@ -115,19 +152,363 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "241194e6",
+   "execution_count": 45,
+   "id": "033cee9a",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data grouped by Speed (numeric columns only):\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>T(sec)</th>\n",
+       "      <th>HR(bpm)</th>\n",
+       "      <th>VO2(ml/min)</th>\n",
+       "      <th>VCO2(ml/min)</th>\n",
+       "      <th>RER</th>\n",
+       "      <th>VE(l/min)</th>\n",
+       "      <th>FEO2</th>\n",
+       "      <th>FECO2</th>\n",
+       "      <th>FETO2</th>\n",
+       "      <th>FETCO2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>EE(kcal/day)</th>\n",
+       "      <th>EE(kcal/min)</th>\n",
+       "      <th>CARBS(kcal)</th>\n",
+       "      <th>CARBS(%)</th>\n",
+       "      <th>FAT(kcal)</th>\n",
+       "      <th>FAT(%)</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>CUMULATIVE EE(kcal)</th>\n",
+       "      <th>BP(kPa)</th>\n",
+       "      <th>Watts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Speed</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3.5</th>\n",
+       "      <td>142.4</td>\n",
+       "      <td>96.1</td>\n",
+       "      <td>1046.2</td>\n",
+       "      <td>854.9</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>28.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7230.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>39.4</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>60.6</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>99.5</td>\n",
+       "      <td>95.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>320.8</td>\n",
+       "      <td>109.3</td>\n",
+       "      <td>1423.6</td>\n",
+       "      <td>1271.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>38.7</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10008.1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>64.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>35.9</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>27.3</td>\n",
+       "      <td>99.5</td>\n",
+       "      <td>112.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.5</th>\n",
+       "      <td>501.8</td>\n",
+       "      <td>139.4</td>\n",
+       "      <td>1991.2</td>\n",
+       "      <td>1862.2</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14132.4</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>52.3</td>\n",
+       "      <td>99.5</td>\n",
+       "      <td>123.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>685.6</td>\n",
+       "      <td>153.2</td>\n",
+       "      <td>2216.6</td>\n",
+       "      <td>2098.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>64.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15771.4</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>80.7</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>19.3</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>85.1</td>\n",
+       "      <td>99.5</td>\n",
+       "      <td>135.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.5</th>\n",
+       "      <td>862.6</td>\n",
+       "      <td>163.1</td>\n",
+       "      <td>2303.3</td>\n",
+       "      <td>2198.3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>69.9</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16579.3</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>86.3</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>118.3</td>\n",
+       "      <td>99.5</td>\n",
+       "      <td>151.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       T(sec)  HR(bpm)  VO2(ml/min)  VCO2(ml/min)  RER  VE(l/min)  FEO2  \\\n",
+       "Speed                                                                     \n",
+       "3.5     142.4     96.1       1046.2         854.9  0.8       28.3   0.2   \n",
+       "4.0     320.8    109.3       1423.6        1271.0  0.9       38.7   0.2   \n",
+       "4.5     501.8    139.4       1991.2        1862.2  0.9       56.0   0.2   \n",
+       "5.0     685.6    153.2       2216.6        2098.0  0.9       64.3   0.2   \n",
+       "5.5     862.6    163.1       2303.3        2198.3  1.0       69.9   0.2   \n",
+       "\n",
+       "       FECO2  FETO2  FETCO2  ...  EE(kcal/day)  EE(kcal/min)  CARBS(kcal)  \\\n",
+       "Speed                        ...                                            \n",
+       "3.5      0.0    0.0     0.0  ...        7230.0           5.0          0.2   \n",
+       "4.0      0.0    0.0     0.0  ...       10008.1           7.0          0.4   \n",
+       "4.5      0.0    0.0     0.0  ...       14132.4           9.8          0.6   \n",
+       "5.0      0.0    0.0     0.0  ...       15771.4          11.0          0.7   \n",
+       "5.5      0.0    0.0     0.0  ...       16579.3          11.5          0.7   \n",
+       "\n",
+       "       CARBS(%)  FAT(kcal)  FAT(%)   MET  CUMULATIVE EE(kcal)  BP(kPa)  Watts  \n",
+       "Speed                                                                          \n",
+       "3.5        39.4        0.3    60.6   5.5                  8.8     99.5   95.7  \n",
+       "4.0        64.1        0.2    35.9   7.5                 27.3     99.5  112.4  \n",
+       "4.5        78.0        0.2    22.0  10.5                 52.3     99.5  123.8  \n",
+       "5.0        80.7        0.2    19.3  11.7                 85.1     99.5  135.2  \n",
+       "5.5        86.3        0.1    13.7  12.2                118.3     99.5  151.9  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group by speed and calculate mean for numeric columns only\n",
+    "speed_groups = df.groupby('Speed').mean(numeric_only=True).round(1)\n",
+    "print(\"Data grouped by Speed (numeric columns only):\")\n",
+    "\n",
+    "speed_groups = speed_groups.iloc[1:-1]\n",
+    "speed_groups.head()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b7f4e117",
+   "id": "25f1a8ef",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       T(sec)  HR(bpm)  VO2(ml/min)  VCO2(ml/min)  RER  VE(l/min)  FEO2  \\\n",
+      "Speed                                                                     \n",
+      "3.5     142.4     96.1       1046.2         854.9  0.8       28.3   0.2   \n",
+      "4.0     320.8    109.3       1423.6        1271.0  0.9       38.7   0.2   \n",
+      "4.5     501.8    139.4       1991.2        1862.2  0.9       56.0   0.2   \n",
+      "5.0     685.6    153.2       2216.6        2098.0  0.9       64.3   0.2   \n",
+      "5.5     862.6    163.1       2303.3        2198.3  1.0       69.9   0.2   \n",
+      "6.0    1042.3    170.8       2282.2        2229.4  1.0       72.0   0.2   \n",
+      "6.5    1225.8    179.6       2381.8        2345.0  1.0       81.2   0.2   \n",
+      "7.0    1412.0    187.2       2348.1        2409.4  1.1       88.5   0.2   \n",
+      "7.5    1596.7    194.0       2519.0        2734.9  1.1      107.0   0.2   \n",
+      "\n",
+      "       FECO2  FETO2  FETCO2  ...  EE(kcal/day)  EE(kcal/min)  CARBS(kcal)  \\\n",
+      "Speed                        ...                                            \n",
+      "3.5      0.0    0.0     0.0  ...        7230.0           5.0          0.2   \n",
+      "4.0      0.0    0.0     0.0  ...       10008.1           7.0          0.4   \n",
+      "4.5      0.0    0.0     0.0  ...       14132.4           9.8          0.6   \n",
+      "5.0      0.0    0.0     0.0  ...       15771.4          11.0          0.7   \n",
+      "5.5      0.0    0.0     0.0  ...       16579.3          11.5          0.7   \n",
+      "6.0      0.0    0.0     0.0  ...       16763.2          11.6          0.8   \n",
+      "6.5      0.0    0.0     0.0  ...       17690.0          12.3          0.8   \n",
+      "7.0      0.0    0.0     0.0  ...       17600.5          12.2          0.9   \n",
+      "7.5      0.0    0.0     0.0  ...       18976.5          13.2          0.9   \n",
+      "\n",
+      "       CARBS(%)  FAT(kcal)  FAT(%)   MET  CUMULATIVE EE(kcal)  BP(kPa)  Watts  \n",
+      "Speed                                                                          \n",
+      "3.5        39.4        0.3    60.6   5.5                  8.8     99.5   95.7  \n",
+      "4.0        64.1        0.2    35.9   7.5                 27.3     99.5  112.4  \n",
+      "4.5        78.0        0.2    22.0  10.5                 52.3     99.5  123.8  \n",
+      "5.0        80.7        0.2    19.3  11.7                 85.1     99.5  135.2  \n",
+      "5.5        86.3        0.1    13.7  12.2                118.3     99.5  151.9  \n",
+      "6.0        93.9        0.1     6.1  12.1                152.8     99.5  163.3  \n",
+      "6.5        93.8        0.1     6.2  12.6                189.8     99.5  180.0  \n",
+      "7.0        97.3        0.0     2.7  12.4                227.9     99.5  191.4  \n",
+      "7.5        99.7        0.0     0.3  13.3                267.2     99.5  208.1  \n",
+      "\n",
+      "[9 rows x 26 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "speed_groups = df.groupby('Speed').mean(numeric_only=True).round(1)\n",
+    "# Plot Speed vs Heart Rate from the grouped data\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "# Drop the first and last row from speed_groups\n",
+    "speed_groups = speed_groups.iloc[1:-1]\n",
+    "print(speed_groups)\n",
+    "# Create speed bins from 3.5 to 7.5 mph\n",
+    "speed_bins = np.arange(3.5, 8.0, 0.5)  # Creates bins: 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5\n",
+    "stage_labels = [f'Stage {i}' for i in range(1, len(speed_bins) + 1)]\n",
+    "\n",
+    "# Filter data to only include speeds in the desired range\n",
+    "filtered_data = speed_groups[(speed_groups.index >= 3.5) & (speed_groups.index <= 7.5)]\n",
+    "\n",
+    "# Create the main axis for heart rate line plot\n",
+    "ax1 = plt.gca()\n",
+    "line = ax1.plot(filtered_data.index, filtered_data['HR(bpm)'], marker='o', linewidth=2, markersize=6, color='red', label='Heart Rate')\n",
+    "ax1.set_xlabel('Speed (mph)', fontsize=12)\n",
+    "ax1.set_ylabel('Heart Rate (bpm)', fontsize=12, color='red')\n",
+    "ax1.tick_params(axis='y', labelcolor='red')\n",
+    "\n",
+    "# Add heart rate values as text labels on the points\n",
+    "for i, (speed, hr) in enumerate(zip(filtered_data.index, filtered_data['HR(bpm)'])):\n",
+    "    ax1.text(speed, hr + 2, f'{int(hr)}', ha='center', va='bottom', fontsize=9, fontweight='bold', color='red')\n",
+    "\n",
+    "# Create secondary y-axis for EE bar chart\n",
+    "ax2 = ax1.twinx()\n",
+    "bars = ax2.bar(filtered_data.index, filtered_data['EE(kcal/min)'], alpha=0.6, width=0.2, color='blue', label='Energy Expenditure')\n",
+    "ax2.set_ylabel('Energy Expenditure (kcal/min)', fontsize=12, color='blue')\n",
+    "ax2.tick_params(axis='y', labelcolor='blue')\n",
+    "\n",
+    "# Add EE values as text labels on the bars\n",
+    "for i, (speed, ee) in enumerate(zip(filtered_data.index, filtered_data['EE(kcal/min)'])):\n",
+    "    ax2.text(speed, ee + 0.3, f'{ee:.1f}', ha='center', va='bottom', fontsize=9, fontweight='bold', color='blue')\n",
+    "\n",
+    "# Update x-axis with stage labels\n",
+    "plt.xticks(filtered_data.index, stage_labels)\n",
+    "plt.title('Heart Rate vs Speed with Energy Expenditure', fontsize=14, fontweight='bold')\n",
+    "\n",
+    "# Combine legends\n",
+    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')\n",
+    "\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {