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lap data stuff

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Aditya Pulipaka
2025-10-19 02:17:23 -05:00
parent d34c4828a7
commit 9f70ba7221
2 changed files with 675 additions and 0 deletions
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fetch_lap_data.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "be0c6fbf",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"FastF1 Lap-Level Data Fetcher for HPC F1 AI Strategy System\n",
"\n",
"Downloads lap-aggregated telemetry and race data from a specific F1 session.\n",
"Creates per-lap statistics instead of per-time-sample data.\n",
"\n",
"Output columns:\n",
"- lap_number: The lap number\n",
"- total_laps: Total laps in the race\n",
"- lap_time: Time taken for this lap\n",
"- average_speed: Average speed during the lap (km/h)\n",
"- max_speed: Maximum speed during the lap (km/h)\n",
"- tire_compound: Tire compound used\n",
"- tire_life_laps: Number of laps on current tires\n",
"- track_temperature: Average track temperature during the lap\n",
"- rainfall: Whether it rained during the lap\n",
"\"\"\"\n",
"import fastf1\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f757cb34",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"req WARNING \tDEFAULT CACHE ENABLED! (318.35 MB) /Users/adipu/Library/Caches/fastf1\n",
"core INFO \tLoading data for Italian Grand Prix - Race [v3.6.1]\n",
"req INFO \tUsing cached data for session_info\n",
"req INFO \tUsing cached data for driver_info\n",
"req INFO \tUsing cached data for session_status_data\n",
"req INFO \tUsing cached data for lap_count\n",
"req INFO \tUsing cached data for track_status_data\n",
"req INFO \tUsing cached data for _extended_timing_data\n",
"req INFO \tUsing cached data for timing_app_data\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tUsing cached data for car_data\n",
"req INFO \tUsing cached data for position_data\n",
"req INFO \tUsing cached data for weather_data\n",
"req INFO \tUsing cached data for race_control_messages\n",
"core WARNING \tDriver 1 completed the race distance 06:25.888000 before the recorded end of the session.\n",
"core WARNING \tDriver 11 completed the race distance 06:19.824000 before the recorded end of the session.\n",
"core WARNING \tDriver 55 completed the race distance 06:14.695000 before the recorded end of the session.\n",
"core WARNING \tDriver 16 completed the race distance 06:14.511000 before the recorded end of the session.\n",
"core WARNING \tDriver 63 completed the race distance 06:07.860000 before the recorded end of the session.\n",
"core WARNING \tDriver 44 completed the race distance 05:48.209000 before the recorded end of the session.\n",
"core WARNING \tDriver 23 completed the race distance 05:40.782000 before the recorded end of the session.\n",
"core WARNING \tDriver 4 completed the race distance 05:40.439000 before the recorded end of the session.\n",
"core WARNING \tDriver 14 completed the race distance 05:39.594000 before the recorded end of the session.\n",
"core INFO \tFinished loading data for 20 drivers: ['1', '11', '55', '16', '63', '44', '23', '4', '14', '77', '40', '81', '2', '24', '10', '18', '27', '20', '31', '22']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded session: 2023 Monza Race\n",
"Driver: ALO (Alonso)\n",
"Total laps in race: 51.0\n",
"Driver laps: 51\n"
]
}
],
"source": [
"# 1. Load the session\n",
"session = fastf1.get_session(2023, 'Monza', 'R')\n",
"session.load(telemetry=True, laps=True, weather=True)\n",
"\n",
"# 2. Pick the driver\n",
"driver_laps = session.laps.pick_drivers('ALO')\n",
"\n",
"# Get total number of laps in the race (maximum lap number from all drivers)\n",
"total_laps = session.laps['LapNumber'].max()\n",
"\n",
"print(f\"Loaded session: 2023 Monza Race\")\n",
"print(f\"Driver: ALO (Alonso)\")\n",
"print(f\"Total laps in race: {total_laps}\")\n",
"print(f\"Driver laps: {len(driver_laps)}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "aa5e5d8f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed lap 10...\n",
"Processed lap 20...\n",
"Processed lap 30...\n",
"Processed lap 40...\n",
"Processed lap 50...\n",
"\n",
"✓ Created lap-level dataframe with 51 laps\n",
"Laps covered: 1 to 51\n"
]
},
{
"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",
" vertical-align: top;\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>lap_number</th>\n",
" <th>total_laps</th>\n",
" <th>lap_time</th>\n",
" <th>average_speed</th>\n",
" <th>max_speed</th>\n",
" <th>tire_compound</th>\n",
" <th>tire_life_laps</th>\n",
" <th>track_temperature</th>\n",
" <th>rainfall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:33.340000</td>\n",
" <td>210.17</td>\n",
" <td>326.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>1</td>\n",
" <td>42.5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:28.012000</td>\n",
" <td>236.87</td>\n",
" <td>330.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>2</td>\n",
" <td>42.5</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:27.546000</td>\n",
" <td>236.40</td>\n",
" <td>331.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>3</td>\n",
" <td>43.2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:27.221000</td>\n",
" <td>240.13</td>\n",
" <td>341.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>4</td>\n",
" <td>43.2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:27.033000</td>\n",
" <td>236.09</td>\n",
" <td>345.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>5</td>\n",
" <td>43.1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:27.175000</td>\n",
" <td>236.74</td>\n",
" <td>343.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>6</td>\n",
" <td>43.3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:26.929000</td>\n",
" <td>239.72</td>\n",
" <td>340.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>7</td>\n",
" <td>43.6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:26.943000</td>\n",
" <td>238.45</td>\n",
" <td>351.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>8</td>\n",
" <td>43.6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:27.383000</td>\n",
" <td>236.81</td>\n",
" <td>330.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>9</td>\n",
" <td>43.6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>51</td>\n",
" <td>0 days 00:01:27.368000</td>\n",
" <td>232.42</td>\n",
" <td>331.0</td>\n",
" <td>MEDIUM</td>\n",
" <td>10</td>\n",
" <td>43.9</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lap_number total_laps lap_time average_speed max_speed \\\n",
"0 1 51 0 days 00:01:33.340000 210.17 326.0 \n",
"1 2 51 0 days 00:01:28.012000 236.87 330.0 \n",
"2 3 51 0 days 00:01:27.546000 236.40 331.0 \n",
"3 4 51 0 days 00:01:27.221000 240.13 341.0 \n",
"4 5 51 0 days 00:01:27.033000 236.09 345.0 \n",
"5 6 51 0 days 00:01:27.175000 236.74 343.0 \n",
"6 7 51 0 days 00:01:26.929000 239.72 340.0 \n",
"7 8 51 0 days 00:01:26.943000 238.45 351.0 \n",
"8 9 51 0 days 00:01:27.383000 236.81 330.0 \n",
"9 10 51 0 days 00:01:27.368000 232.42 331.0 \n",
"\n",
" tire_compound tire_life_laps track_temperature rainfall \n",
"0 MEDIUM 1 42.5 False \n",
"1 MEDIUM 2 42.5 False \n",
"2 MEDIUM 3 43.2 False \n",
"3 MEDIUM 4 43.2 False \n",
"4 MEDIUM 5 43.1 False \n",
"5 MEDIUM 6 43.3 False \n",
"6 MEDIUM 7 43.6 False \n",
"7 MEDIUM 8 43.6 False \n",
"8 MEDIUM 9 43.6 False \n",
"9 MEDIUM 10 43.9 False "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3. Get weather data for merging\n",
"weather = session.weather_data\n",
"weather['SessionTime'] = pd.to_timedelta(weather['Time'])\n",
"\n",
"# 4. Create lap-level data by aggregating telemetry\n",
"lap_data_list = []\n",
"\n",
"for lap_idx in driver_laps.index:\n",
" lap = driver_laps.loc[lap_idx]\n",
" lap_number = lap['LapNumber']\n",
" \n",
" # Skip invalid laps\n",
" if pd.isna(lap_number):\n",
" continue\n",
" \n",
" # Get telemetry for this lap\n",
" car_data = lap.get_car_data()\n",
" \n",
" if car_data is not None and len(car_data) > 0:\n",
" # Calculate speed statistics\n",
" avg_speed = car_data['Speed'].mean()\n",
" max_speed = car_data['Speed'].max()\n",
" \n",
" # Get lap time\n",
" lap_time = lap['LapTime']\n",
" \n",
" # Get tire information\n",
" tire_compound = lap['Compound']\n",
" tire_life = lap['TyreLife']\n",
" \n",
" # Get weather data for this lap (use lap start time)\n",
" lap_start_time = pd.to_timedelta(lap['LapStartTime'])\n",
" \n",
" # Find closest weather data\n",
" weather_at_lap = weather.iloc[(weather['SessionTime'] - lap_start_time).abs().argmin()]\n",
" track_temp = weather_at_lap['TrackTemp']\n",
" rainfall = weather_at_lap['Rainfall']\n",
" \n",
" # Create lap record\n",
" lap_record = {\n",
" 'lap_number': int(lap_number),\n",
" 'total_laps': int(total_laps),\n",
" 'lap_time': lap_time,\n",
" 'average_speed': round(avg_speed, 2),\n",
" 'max_speed': round(max_speed, 2),\n",
" 'tire_compound': tire_compound,\n",
" 'tire_life_laps': int(tire_life) if pd.notna(tire_life) else None,\n",
" 'track_temperature': round(track_temp, 2) if pd.notna(track_temp) else None,\n",
" 'rainfall': bool(rainfall)\n",
" }\n",
" \n",
" lap_data_list.append(lap_record)\n",
" \n",
" # Progress indicator\n",
" if lap_number % 10 == 0:\n",
" print(f\"Processed lap {int(lap_number)}...\")\n",
"\n",
"# 5. Create final dataframe\n",
"laps_df = pd.DataFrame(lap_data_list)\n",
"\n",
"print(f\"\\n✓ Created lap-level dataframe with {len(laps_df)} laps\")\n",
"print(f\"Laps covered: {laps_df['lap_number'].min()} to {laps_df['lap_number'].max()}\")\n",
"laps_df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b1086b8d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lap-Level Dataframe Info:\n",
"Total laps: 51\n",
"\n",
"Column types:\n",
"lap_number int64\n",
"total_laps int64\n",
"lap_time timedelta64[ns]\n",
"average_speed float64\n",
"max_speed float64\n",
"tire_compound object\n",
"tire_life_laps int64\n",
"track_temperature float64\n",
"rainfall bool\n",
"dtype: object\n",
"\n",
"Basic statistics:\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>lap_number</th>\n",
" <th>total_laps</th>\n",
" <th>lap_time</th>\n",
" <th>average_speed</th>\n",
" <th>max_speed</th>\n",
" <th>tire_life_laps</th>\n",
" <th>track_temperature</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>51.000000</td>\n",
" <td>51.0</td>\n",
" <td>51</td>\n",
" <td>51.000000</td>\n",
" <td>51.000000</td>\n",
" <td>51.000000</td>\n",
" <td>51.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>26.000000</td>\n",
" <td>51.0</td>\n",
" <td>0 days 00:01:27.596803921</td>\n",
" <td>235.797059</td>\n",
" <td>333.686275</td>\n",
" <td>15.411765</td>\n",
" <td>42.898039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>14.866069</td>\n",
" <td>0.0</td>\n",
" <td>0 days 00:00:03.069690434</td>\n",
" <td>7.855085</td>\n",
" <td>4.921342</td>\n",
" <td>8.616673</td>\n",
" <td>0.876924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>51.0</td>\n",
" <td>0 days 00:01:26.105000</td>\n",
" <td>191.140000</td>\n",
" <td>322.000000</td>\n",
" <td>1.000000</td>\n",
" <td>40.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>13.500000</td>\n",
" <td>51.0</td>\n",
" <td>0 days 00:01:26.715000</td>\n",
" <td>236.105000</td>\n",
" <td>331.000000</td>\n",
" <td>8.500000</td>\n",
" <td>42.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>26.000000</td>\n",
" <td>51.0</td>\n",
" <td>0 days 00:01:26.943000</td>\n",
" <td>237.130000</td>\n",
" <td>332.000000</td>\n",
" <td>15.000000</td>\n",
" <td>43.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>38.500000</td>\n",
" <td>51.0</td>\n",
" <td>0 days 00:01:27.328500</td>\n",
" <td>238.655000</td>\n",
" <td>334.000000</td>\n",
" <td>21.000000</td>\n",
" <td>43.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>51.000000</td>\n",
" <td>51.0</td>\n",
" <td>0 days 00:01:47.272000</td>\n",
" <td>241.700000</td>\n",
" <td>351.000000</td>\n",
" <td>33.000000</td>\n",
" <td>44.300000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lap_number total_laps lap_time average_speed \\\n",
"count 51.000000 51.0 51 51.000000 \n",
"mean 26.000000 51.0 0 days 00:01:27.596803921 235.797059 \n",
"std 14.866069 0.0 0 days 00:00:03.069690434 7.855085 \n",
"min 1.000000 51.0 0 days 00:01:26.105000 191.140000 \n",
"25% 13.500000 51.0 0 days 00:01:26.715000 236.105000 \n",
"50% 26.000000 51.0 0 days 00:01:26.943000 237.130000 \n",
"75% 38.500000 51.0 0 days 00:01:27.328500 238.655000 \n",
"max 51.000000 51.0 0 days 00:01:47.272000 241.700000 \n",
"\n",
" max_speed tire_life_laps track_temperature \n",
"count 51.000000 51.000000 51.000000 \n",
"mean 333.686275 15.411765 42.898039 \n",
"std 4.921342 8.616673 0.876924 \n",
"min 322.000000 1.000000 40.800000 \n",
"25% 331.000000 8.500000 42.500000 \n",
"50% 332.000000 15.000000 43.100000 \n",
"75% 334.000000 21.000000 43.600000 \n",
"max 351.000000 33.000000 44.300000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display dataframe info and statistics\n",
"print(\"Lap-Level Dataframe Info:\")\n",
"print(f\"Total laps: {len(laps_df)}\")\n",
"print(f\"\\nColumn types:\")\n",
"print(laps_df.dtypes)\n",
"print(f\"\\nBasic statistics:\")\n",
"laps_df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b2a3b878",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✓ Saved lap-level data to scripts/ALONSO_2023_MONZA_LAPS.csv\n"
]
}
],
"source": [
"# Save to CSV\n",
"laps_df.to_csv(\"scripts/ALONSO_2023_MONZA_LAPS.csv\", index=False)\n",
"print(\"✓ Saved lap-level data to scripts/ALONSO_2023_MONZA_LAPS.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0202372e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tire Strategy:\n",
" First Lap Last Lap Laps on Compound Avg Speed Max Tire Life\n",
"tire_compound \n",
"HARD 22 51 30 236.42 33\n",
"MEDIUM 1 21 21 234.91 21\n"
]
}
],
"source": [
"# Show tire strategy\n",
"print(\"\\nTire Strategy:\")\n",
"tire_stints = laps_df.groupby(['tire_compound']).agg({\n",
" 'lap_number': ['min', 'max', 'count'],\n",
" 'average_speed': 'mean',\n",
" 'tire_life_laps': 'max'\n",
"}).round(2)\n",
"tire_stints.columns = ['First Lap', 'Last Lap', 'Laps on Compound', 'Avg Speed', 'Max Tire Life']\n",
"print(tire_stints)"
]
}
],
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"display_name": "base",
"language": "python",
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},
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

52
scripts/ALONSO_2023_MONZA_LAPS.csv Normal file
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@@ -0,0 +1,52 @@
lap_number,total_laps,lap_time,average_speed,max_speed,tire_compound,tire_life_laps,track_temperature,rainfall
1,51,0 days 00:01:33.340000,210.17,326.0,MEDIUM,1,42.5,False
2,51,0 days 00:01:28.012000,236.87,330.0,MEDIUM,2,42.5,False
3,51,0 days 00:01:27.546000,236.4,331.0,MEDIUM,3,43.2,False
4,51,0 days 00:01:27.221000,240.13,341.0,MEDIUM,4,43.2,False
5,51,0 days 00:01:27.033000,236.09,345.0,MEDIUM,5,43.1,False
6,51,0 days 00:01:27.175000,236.74,343.0,MEDIUM,6,43.3,False
7,51,0 days 00:01:26.929000,239.72,340.0,MEDIUM,7,43.6,False
8,51,0 days 00:01:26.943000,238.45,351.0,MEDIUM,8,43.6,False
9,51,0 days 00:01:27.383000,236.81,330.0,MEDIUM,9,43.6,False
10,51,0 days 00:01:27.368000,232.42,331.0,MEDIUM,10,43.9,False
11,51,0 days 00:01:27.343000,237.18,331.0,MEDIUM,11,43.9,False
12,51,0 days 00:01:27.339000,236.76,332.0,MEDIUM,12,43.3,False
13,51,0 days 00:01:27.158000,235.47,330.0,MEDIUM,13,43.1,False
14,51,0 days 00:01:27.607000,235.97,332.0,MEDIUM,14,43.7,False
15,51,0 days 00:01:26.929000,237.04,332.0,MEDIUM,15,43.7,False
16,51,0 days 00:01:27.164000,238.88,333.0,MEDIUM,16,43.9,False
17,51,0 days 00:01:27.156000,235.35,333.0,MEDIUM,17,43.7,False
18,51,0 days 00:01:27.275000,238.66,333.0,MEDIUM,18,43.5,False
19,51,0 days 00:01:27.318000,234.62,333.0,MEDIUM,19,43.6,False
20,51,0 days 00:01:28.284000,235.31,333.0,MEDIUM,20,44.3,False
21,51,0 days 00:01:32.377000,224.11,332.0,MEDIUM,21,44.3,False
22,51,0 days 00:01:47.272000,191.14,322.0,HARD,4,43.7,False
23,51,0 days 00:01:26.849000,236.5,329.0,HARD,5,43.7,False
24,51,0 days 00:01:26.949000,238.49,331.0,HARD,6,43.5,False
25,51,0 days 00:01:26.899000,237.13,331.0,HARD,7,43.5,False
26,51,0 days 00:01:26.792000,238.18,331.0,HARD,8,43.4,False
27,51,0 days 00:01:26.666000,238.7,331.0,HARD,9,43.4,False
28,51,0 days 00:01:26.723000,234.46,331.0,HARD,10,43.3,False
29,51,0 days 00:01:26.997000,236.12,332.0,HARD,11,42.9,False
30,51,0 days 00:01:26.915000,238.87,333.0,HARD,12,42.5,False
31,51,0 days 00:01:26.848000,240.21,335.0,HARD,13,42.8,False
32,51,0 days 00:01:26.747000,235.57,332.0,HARD,14,42.6,False
33,51,0 days 00:01:26.769000,238.33,332.0,HARD,15,42.4,False
34,51,0 days 00:01:26.563000,236.92,332.0,HARD,16,42.5,False
35,51,0 days 00:01:26.517000,239.63,333.0,HARD,17,42.6,False
36,51,0 days 00:01:26.457000,237.1,332.0,HARD,18,42.5,False
37,51,0 days 00:01:26.380000,238.39,333.0,HARD,19,42.5,False
38,51,0 days 00:01:26.574000,237.75,333.0,HARD,20,42.8,False
39,51,0 days 00:01:26.707000,238.65,332.0,HARD,21,42.8,False
40,51,0 days 00:01:26.610000,235.8,333.0,HARD,22,42.8,False
41,51,0 days 00:01:26.815000,236.82,334.0,HARD,23,42.9,False
42,51,0 days 00:01:26.403000,239.47,332.0,HARD,24,42.5,False
43,51,0 days 00:01:26.105000,240.15,334.0,HARD,25,42.5,False
44,51,0 days 00:01:26.155000,241.7,332.0,HARD,26,41.7,False
45,51,0 days 00:01:26.277000,241.25,333.0,HARD,27,41.7,False
46,51,0 days 00:01:26.453000,238.02,335.0,HARD,28,41.8,False
47,51,0 days 00:01:26.912000,236.31,336.0,HARD,29,41.2,False
48,51,0 days 00:01:27.183000,237.42,340.0,HARD,30,41.2,False
49,51,0 days 00:01:27.245000,238.99,335.0,HARD,31,41.0,False
50,51,0 days 00:01:27.360000,237.3,342.0,HARD,32,40.8,False
51,51,0 days 00:01:27.395000,237.13,345.0,HARD,33,40.8,False
1 lap_number total_laps lap_time average_speed max_speed tire_compound tire_life_laps track_temperature rainfall
2 1 51 0 days 00:01:33.340000 210.17 326.0 MEDIUM 1 42.5 False
3 2 51 0 days 00:01:28.012000 236.87 330.0 MEDIUM 2 42.5 False
4 3 51 0 days 00:01:27.546000 236.4 331.0 MEDIUM 3 43.2 False
5 4 51 0 days 00:01:27.221000 240.13 341.0 MEDIUM 4 43.2 False
6 5 51 0 days 00:01:27.033000 236.09 345.0 MEDIUM 5 43.1 False
7 6 51 0 days 00:01:27.175000 236.74 343.0 MEDIUM 6 43.3 False
8 7 51 0 days 00:01:26.929000 239.72 340.0 MEDIUM 7 43.6 False
9 8 51 0 days 00:01:26.943000 238.45 351.0 MEDIUM 8 43.6 False
10 9 51 0 days 00:01:27.383000 236.81 330.0 MEDIUM 9 43.6 False
11 10 51 0 days 00:01:27.368000 232.42 331.0 MEDIUM 10 43.9 False
12 11 51 0 days 00:01:27.343000 237.18 331.0 MEDIUM 11 43.9 False
13 12 51 0 days 00:01:27.339000 236.76 332.0 MEDIUM 12 43.3 False
14 13 51 0 days 00:01:27.158000 235.47 330.0 MEDIUM 13 43.1 False
15 14 51 0 days 00:01:27.607000 235.97 332.0 MEDIUM 14 43.7 False
16 15 51 0 days 00:01:26.929000 237.04 332.0 MEDIUM 15 43.7 False
17 16 51 0 days 00:01:27.164000 238.88 333.0 MEDIUM 16 43.9 False
18 17 51 0 days 00:01:27.156000 235.35 333.0 MEDIUM 17 43.7 False
19 18 51 0 days 00:01:27.275000 238.66 333.0 MEDIUM 18 43.5 False
20 19 51 0 days 00:01:27.318000 234.62 333.0 MEDIUM 19 43.6 False
21 20 51 0 days 00:01:28.284000 235.31 333.0 MEDIUM 20 44.3 False
22 21 51 0 days 00:01:32.377000 224.11 332.0 MEDIUM 21 44.3 False
23 22 51 0 days 00:01:47.272000 191.14 322.0 HARD 4 43.7 False
24 23 51 0 days 00:01:26.849000 236.5 329.0 HARD 5 43.7 False
25 24 51 0 days 00:01:26.949000 238.49 331.0 HARD 6 43.5 False
26 25 51 0 days 00:01:26.899000 237.13 331.0 HARD 7 43.5 False
27 26 51 0 days 00:01:26.792000 238.18 331.0 HARD 8 43.4 False
28 27 51 0 days 00:01:26.666000 238.7 331.0 HARD 9 43.4 False
29 28 51 0 days 00:01:26.723000 234.46 331.0 HARD 10 43.3 False
30 29 51 0 days 00:01:26.997000 236.12 332.0 HARD 11 42.9 False
31 30 51 0 days 00:01:26.915000 238.87 333.0 HARD 12 42.5 False
32 31 51 0 days 00:01:26.848000 240.21 335.0 HARD 13 42.8 False
33 32 51 0 days 00:01:26.747000 235.57 332.0 HARD 14 42.6 False
34 33 51 0 days 00:01:26.769000 238.33 332.0 HARD 15 42.4 False
35 34 51 0 days 00:01:26.563000 236.92 332.0 HARD 16 42.5 False
36 35 51 0 days 00:01:26.517000 239.63 333.0 HARD 17 42.6 False
37 36 51 0 days 00:01:26.457000 237.1 332.0 HARD 18 42.5 False
38 37 51 0 days 00:01:26.380000 238.39 333.0 HARD 19 42.5 False
39 38 51 0 days 00:01:26.574000 237.75 333.0 HARD 20 42.8 False
40 39 51 0 days 00:01:26.707000 238.65 332.0 HARD 21 42.8 False
41 40 51 0 days 00:01:26.610000 235.8 333.0 HARD 22 42.8 False
42 41 51 0 days 00:01:26.815000 236.82 334.0 HARD 23 42.9 False
43 42 51 0 days 00:01:26.403000 239.47 332.0 HARD 24 42.5 False
44 43 51 0 days 00:01:26.105000 240.15 334.0 HARD 25 42.5 False
45 44 51 0 days 00:01:26.155000 241.7 332.0 HARD 26 41.7 False
46 45 51 0 days 00:01:26.277000 241.25 333.0 HARD 27 41.7 False
47 46 51 0 days 00:01:26.453000 238.02 335.0 HARD 28 41.8 False
48 47 51 0 days 00:01:26.912000 236.31 336.0 HARD 29 41.2 False
49 48 51 0 days 00:01:27.183000 237.42 340.0 HARD 30 41.2 False
50 49 51 0 days 00:01:27.245000 238.99 335.0 HARD 31 41.0 False
51 50 51 0 days 00:01:27.360000 237.3 342.0 HARD 32 40.8 False
52 51 51 0 days 00:01:27.395000 237.13 345.0 HARD 33 40.8 False