799 lines
30 KiB
Plaintext
799 lines
30 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "be0c6fbf",
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"FastF1 Lap-Level Data Fetcher for HPC F1 AI Strategy System\n",
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"\n",
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"Downloads lap-aggregated telemetry and race data from a specific F1 session.\n",
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"Creates per-lap statistics instead of per-time-sample data.\n",
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"\n",
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"Output columns:\n",
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"- lap_number: The lap number\n",
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"- total_laps: Total laps in the race\n",
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"- lap_time: Time taken for this lap\n",
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"- average_speed: Average speed during the lap (km/h)\n",
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"- max_speed: Maximum speed during the lap (km/h)\n",
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"- tire_compound: Tire compound used\n",
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"- tire_life_laps: Number of laps on current tires\n",
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"- track_temperature: Average track temperature during the lap\n",
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"- rainfall: Whether it rained during the lap\n",
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"\"\"\"\n",
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"import fastf1\n",
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "f757cb34",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"core INFO \tLoading data for Italian Grand Prix - Race [v3.6.1]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core WARNING \tDriver 1 completed the race distance 06:25.888000 before the recorded end of the session.\n",
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"core WARNING \tDriver 11 completed the race distance 06:19.824000 before the recorded end of the session.\n",
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"core WARNING \tDriver 55 completed the race distance 06:14.695000 before the recorded end of the session.\n",
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"core WARNING \tDriver 16 completed the race distance 06:14.511000 before the recorded end of the session.\n",
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"core WARNING \tDriver 63 completed the race distance 06:07.860000 before the recorded end of the session.\n",
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"core WARNING \tDriver 44 completed the race distance 05:48.209000 before the recorded end of the session.\n",
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"core WARNING \tDriver 23 completed the race distance 05:40.782000 before the recorded end of the session.\n",
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"core WARNING \tDriver 4 completed the race distance 05:40.439000 before the recorded end of the session.\n",
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"core WARNING \tDriver 14 completed the race distance 05:39.594000 before the recorded end of the session.\n",
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"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"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded session: 2023 Monza Race\n",
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"Driver: ALO (Alonso)\n",
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"Total laps in race: 51.0\n",
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"Driver laps: 51\n"
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]
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}
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],
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"source": [
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"# 1. Load the session\n",
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"session = fastf1.get_session(2023, 'Monza', 'R')\n",
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"session.load(telemetry=True, laps=True, weather=True)\n",
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"\n",
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"# 2. Pick the driver\n",
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"driver_laps = session.laps.pick_drivers('ALO')\n",
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"\n",
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"# Get total number of laps in the race (maximum lap number from all drivers)\n",
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"total_laps = session.laps['LapNumber'].max()\n",
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"\n",
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"print(f\"Loaded session: 2023 Monza Race\")\n",
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"print(f\"Driver: ALO (Alonso)\")\n",
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"print(f\"Total laps in race: {total_laps}\")\n",
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"print(f\"Driver laps: {len(driver_laps)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "aa5e5d8f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processed lap 10...\n",
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"Processed lap 20...\n",
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"Processed lap 30...\n",
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"Processed lap 40...\n",
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"Processed lap 50...\n",
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"\n",
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"✓ Created lap-level dataframe with 51 laps\n",
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"Laps covered: 1 to 51\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>lap_number</th>\n",
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" <th>total_laps</th>\n",
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" <th>position</th>\n",
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" <th>gap_to_leader</th>\n",
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" <th>gap_to_ahead</th>\n",
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" <th>lap_time</th>\n",
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" <th>average_speed</th>\n",
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" <th>max_speed</th>\n",
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" <th>tire_compound</th>\n",
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" <th>tire_life_laps</th>\n",
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" <th>track_temperature</th>\n",
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" <th>rainfall</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>6.223</td>\n",
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" <td>0.620</td>\n",
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" <td>0 days 00:01:33.340000</td>\n",
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" <td>210.17</td>\n",
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" <td>326.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>1</td>\n",
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" <td>42.5</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>8.229</td>\n",
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" <td>0.712</td>\n",
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" <td>0 days 00:01:28.012000</td>\n",
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" <td>236.87</td>\n",
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" <td>330.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>2</td>\n",
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" <td>42.5</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>9.799</td>\n",
|
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" <td>0.898</td>\n",
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" <td>0 days 00:01:27.546000</td>\n",
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" <td>236.40</td>\n",
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" <td>331.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>3</td>\n",
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" <td>43.2</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>10.953</td>\n",
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" <td>0.919</td>\n",
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" <td>0 days 00:01:27.221000</td>\n",
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" <td>240.13</td>\n",
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" <td>341.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>4</td>\n",
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" <td>43.2</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>11.563</td>\n",
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" <td>0.917</td>\n",
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" <td>0 days 00:01:27.033000</td>\n",
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" <td>236.09</td>\n",
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" <td>345.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>5</td>\n",
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" <td>43.1</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>6</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>12.123</td>\n",
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" <td>0.923</td>\n",
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" <td>0 days 00:01:27.175000</td>\n",
|
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" <td>236.74</td>\n",
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" <td>343.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>6</td>\n",
|
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" <td>43.3</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>7</td>\n",
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" <td>51</td>\n",
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" <td>11</td>\n",
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" <td>12.694</td>\n",
|
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" <td>0.387</td>\n",
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" <td>0 days 00:01:26.929000</td>\n",
|
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" <td>239.72</td>\n",
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" <td>340.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>7</td>\n",
|
|
" <td>43.6</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>8</td>\n",
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" <td>51</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>13.413</td>\n",
|
|
" <td>2.760</td>\n",
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" <td>0 days 00:01:26.943000</td>\n",
|
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" <td>238.45</td>\n",
|
|
" <td>351.0</td>\n",
|
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" <td>MEDIUM</td>\n",
|
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" <td>8</td>\n",
|
|
" <td>43.6</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>9</td>\n",
|
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" <td>51</td>\n",
|
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" <td>10</td>\n",
|
|
" <td>14.320</td>\n",
|
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" <td>3.387</td>\n",
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" <td>0 days 00:01:27.383000</td>\n",
|
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" <td>236.81</td>\n",
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" <td>330.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>9</td>\n",
|
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" <td>43.6</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>10</td>\n",
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" <td>51</td>\n",
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" <td>10</td>\n",
|
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" <td>15.177</td>\n",
|
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" <td>3.760</td>\n",
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" <td>0 days 00:01:27.368000</td>\n",
|
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" <td>232.42</td>\n",
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" <td>331.0</td>\n",
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" <td>MEDIUM</td>\n",
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" <td>10</td>\n",
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" <td>43.9</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" lap_number total_laps position gap_to_leader gap_to_ahead \\\n",
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"0 1 51 11 6.223 0.620 \n",
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"1 2 51 11 8.229 0.712 \n",
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"2 3 51 11 9.799 0.898 \n",
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"3 4 51 11 10.953 0.919 \n",
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"4 5 51 11 11.563 0.917 \n",
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"5 6 51 11 12.123 0.923 \n",
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"6 7 51 11 12.694 0.387 \n",
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"7 8 51 10 13.413 2.760 \n",
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"8 9 51 10 14.320 3.387 \n",
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"9 10 51 10 15.177 3.760 \n",
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"\n",
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" lap_time average_speed max_speed tire_compound \\\n",
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"0 0 days 00:01:33.340000 210.17 326.0 MEDIUM \n",
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"1 0 days 00:01:28.012000 236.87 330.0 MEDIUM \n",
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"2 0 days 00:01:27.546000 236.40 331.0 MEDIUM \n",
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"3 0 days 00:01:27.221000 240.13 341.0 MEDIUM \n",
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"4 0 days 00:01:27.033000 236.09 345.0 MEDIUM \n",
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"5 0 days 00:01:27.175000 236.74 343.0 MEDIUM \n",
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"6 0 days 00:01:26.929000 239.72 340.0 MEDIUM \n",
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"7 0 days 00:01:26.943000 238.45 351.0 MEDIUM \n",
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"8 0 days 00:01:27.383000 236.81 330.0 MEDIUM \n",
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"9 0 days 00:01:27.368000 232.42 331.0 MEDIUM \n",
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"\n",
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" tire_life_laps track_temperature rainfall \n",
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"0 1 42.5 False \n",
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"1 2 42.5 False \n",
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"2 3 43.2 False \n",
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"3 4 43.2 False \n",
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"4 5 43.1 False \n",
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"5 6 43.3 False \n",
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"6 7 43.6 False \n",
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"7 8 43.6 False \n",
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"8 9 43.6 False \n",
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"9 10 43.9 False "
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"# 3. Get weather data for merging\n",
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"weather = session.weather_data\n",
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"weather['SessionTime'] = pd.to_timedelta(weather['Time'])\n",
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"\n",
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"# 4. Get all laps for position calculation\n",
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"all_laps = session.laps\n",
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"\n",
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"# 5. Create lap-level data by aggregating telemetry\n",
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"lap_data_list = []\n",
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"\n",
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"for lap_idx in driver_laps.index:\n",
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" lap = driver_laps.loc[lap_idx]\n",
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" lap_number = lap['LapNumber']\n",
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" \n",
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" # Skip invalid laps\n",
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" if pd.isna(lap_number):\n",
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" continue\n",
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" \n",
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" # Get telemetry for this lap\n",
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" car_data = lap.get_car_data()\n",
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" \n",
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" if car_data is not None and len(car_data) > 0:\n",
|
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" # Calculate speed statistics\n",
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" avg_speed = car_data['Speed'].mean()\n",
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" max_speed = car_data['Speed'].max()\n",
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" \n",
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" # Get lap time\n",
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" lap_time = lap['LapTime']\n",
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" \n",
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" # Get tire information\n",
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" tire_compound = lap['Compound']\n",
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" tire_life = lap['TyreLife']\n",
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" \n",
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" # Get position data for this lap\n",
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" position = lap['Position']\n",
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" \n",
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" # Calculate gaps: get all drivers' data for this lap\n",
|
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" this_lap_all_drivers = all_laps[all_laps['LapNumber'] == lap_number].copy()\n",
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" \n",
|
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" # Sort by position to calculate gaps\n",
|
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" this_lap_all_drivers = this_lap_all_drivers.sort_values('Position')\n",
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" \n",
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" # Calculate cumulative race time for gap calculations\n",
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" gap_to_leader = 0.0\n",
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" gap_to_ahead = 0.0\n",
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" \n",
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" if pd.notna(position) and position > 1:\n",
|
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" # Get our driver's data\n",
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" our_data = this_lap_all_drivers[this_lap_all_drivers['Driver'] == 'ALO']\n",
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" \n",
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" if not our_data.empty:\n",
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" # Time at the end of this lap (cumulative)\n",
|
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" # Note: FastF1 provides 'Time' which is cumulative race time\n",
|
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" our_time = our_data['Time'].values[0]\n",
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" \n",
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" # Get leader's time (P1)\n",
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" leader_data = this_lap_all_drivers[this_lap_all_drivers['Position'] == 1]\n",
|
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" if not leader_data.empty:\n",
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" leader_time = leader_data['Time'].values[0]\n",
|
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" # Convert numpy.timedelta64 to seconds\n",
|
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" gap_to_leader = (our_time - leader_time) / np.timedelta64(1, 's')\n",
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" \n",
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" # Get car ahead's time (Position - 1)\n",
|
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" ahead_position = position - 1\n",
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" ahead_data = this_lap_all_drivers[this_lap_all_drivers['Position'] == ahead_position]\n",
|
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" if not ahead_data.empty:\n",
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" ahead_time = ahead_data['Time'].values[0]\n",
|
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" # Convert numpy.timedelta64 to seconds\n",
|
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" gap_to_ahead = (our_time - ahead_time) / np.timedelta64(1, 's')\n",
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" \n",
|
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" # Get weather data for this lap (use lap start time)\n",
|
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" lap_start_time = pd.to_timedelta(lap['LapStartTime'])\n",
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" \n",
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|
" # 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",
|
|
" 'position': int(position) if pd.notna(position) else None,\n",
|
|
" 'gap_to_leader': round(gap_to_leader, 3) if gap_to_leader > 0 else 0.0,\n",
|
|
" 'gap_to_ahead': round(gap_to_ahead, 3) if gap_to_ahead > 0 else 0.0,\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",
|
|
"# 6. 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": 7,
|
|
"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",
|
|
"position int64\n",
|
|
"gap_to_leader float64\n",
|
|
"gap_to_ahead float64\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"
|
|
]
|
|
},
|
|
{
|
|
"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>lap_number</th>\n",
|
|
" <th>total_laps</th>\n",
|
|
" <th>position</th>\n",
|
|
" <th>gap_to_leader</th>\n",
|
|
" <th>gap_to_ahead</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>51.000000</td>\n",
|
|
" <td>51.000000</td>\n",
|
|
" <td>51.000000</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>9.803922</td>\n",
|
|
" <td>29.418314</td>\n",
|
|
" <td>2.889235</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>1.058671</td>\n",
|
|
" <td>13.842909</td>\n",
|
|
" <td>1.707825</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>6.000000</td>\n",
|
|
" <td>6.223000</td>\n",
|
|
" <td>0.387000</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>9.000000</td>\n",
|
|
" <td>16.928500</td>\n",
|
|
" <td>1.123000</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>10.000000</td>\n",
|
|
" <td>29.113000</td>\n",
|
|
" <td>2.799000</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>10.000000</td>\n",
|
|
" <td>43.405500</td>\n",
|
|
" <td>4.136500</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>12.000000</td>\n",
|
|
" <td>48.171000</td>\n",
|
|
" <td>8.135000</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 position gap_to_leader gap_to_ahead \\\n",
|
|
"count 51.000000 51.0 51.000000 51.000000 51.000000 \n",
|
|
"mean 26.000000 51.0 9.803922 29.418314 2.889235 \n",
|
|
"std 14.866069 0.0 1.058671 13.842909 1.707825 \n",
|
|
"min 1.000000 51.0 6.000000 6.223000 0.387000 \n",
|
|
"25% 13.500000 51.0 9.000000 16.928500 1.123000 \n",
|
|
"50% 26.000000 51.0 10.000000 29.113000 2.799000 \n",
|
|
"75% 38.500000 51.0 10.000000 43.405500 4.136500 \n",
|
|
"max 51.000000 51.0 12.000000 48.171000 8.135000 \n",
|
|
"\n",
|
|
" lap_time average_speed max_speed tire_life_laps \\\n",
|
|
"count 51 51.000000 51.000000 51.000000 \n",
|
|
"mean 0 days 00:01:27.596803921 235.797059 333.686275 15.411765 \n",
|
|
"std 0 days 00:00:03.069690434 7.855085 4.921342 8.616673 \n",
|
|
"min 0 days 00:01:26.105000 191.140000 322.000000 1.000000 \n",
|
|
"25% 0 days 00:01:26.715000 236.105000 331.000000 8.500000 \n",
|
|
"50% 0 days 00:01:26.943000 237.130000 332.000000 15.000000 \n",
|
|
"75% 0 days 00:01:27.328500 238.655000 334.000000 21.000000 \n",
|
|
"max 0 days 00:01:47.272000 241.700000 351.000000 33.000000 \n",
|
|
"\n",
|
|
" track_temperature \n",
|
|
"count 51.000000 \n",
|
|
"mean 42.898039 \n",
|
|
"std 0.876924 \n",
|
|
"min 40.800000 \n",
|
|
"25% 42.500000 \n",
|
|
"50% 43.100000 \n",
|
|
"75% 43.600000 \n",
|
|
"max 44.300000 "
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"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": 8,
|
|
"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": 9,
|
|
"id": "efcff166",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Position and Gap Analysis:\n",
|
|
"Starting position: P11\n",
|
|
"Final position: P9\n",
|
|
"Average gap to leader: 29.42s\n",
|
|
"Average gap to car ahead: 2.89s\n",
|
|
"\n",
|
|
"Position changes over race:\n",
|
|
" lap_number position gap_to_leader gap_to_ahead\n",
|
|
"0 1 11 6.223 0.620\n",
|
|
"1 2 11 8.229 0.712\n",
|
|
"2 3 11 9.799 0.898\n",
|
|
"3 4 11 10.953 0.919\n",
|
|
"4 5 11 11.563 0.917\n",
|
|
"5 6 11 12.123 0.923\n",
|
|
"6 7 11 12.694 0.387\n",
|
|
"7 8 10 13.413 2.760\n",
|
|
"8 9 10 14.320 3.387\n",
|
|
"9 10 10 15.177 3.760\n",
|
|
"10 11 10 15.829 3.984\n",
|
|
"11 12 10 16.760 4.129\n",
|
|
"12 13 10 17.031 3.995\n",
|
|
"13 14 10 17.666 4.076\n",
|
|
"14 15 10 17.366 0.469\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Display position and gap information\n",
|
|
"print(\"\\nPosition and Gap Analysis:\")\n",
|
|
"print(f\"Starting position: P{laps_df['position'].iloc[0]}\")\n",
|
|
"print(f\"Final position: P{laps_df['position'].iloc[-1]}\")\n",
|
|
"print(f\"Average gap to leader: {laps_df['gap_to_leader'].mean():.2f}s\")\n",
|
|
"print(f\"Average gap to car ahead: {laps_df['gap_to_ahead'].mean():.2f}s\")\n",
|
|
"print(f\"\\nPosition changes over race:\")\n",
|
|
"print(laps_df[['lap_number', 'position', 'gap_to_leader', 'gap_to_ahead']].head(15))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"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)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "base",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|