{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 🏎️ Analyzing Formula 1 Telemetry Data and Making Lap Time Predictions Using Machine Learning\n", "\n", "## Introduction\n", "Formula 1 racing combines speed, precision, and strategy, making it a fascinating arena for data analysis. In this tutorial, we will explore how to analyze Formula 1 telemetry data and predict lap times using machine learning techniques.\n", "\n", "### Motivation\n", "\n", "Suppose you work for a racing team and your strategy engineer wants to know in advance an estimate of what the driver's lap time will be\n", "\n", "This notebook shows how to solve this problem.\n", "\n", "![image.png](./images/sector-time.png)\n", "\n", "By the end of this guide, you will understand how to:\n", "- 📊 preprocess data\n", "- 👨‍💻 engineer features \n", "- 🤖 train predictive models \n", "- 💯 interpret the results\n", " \n", "All within the dynamic world of Formula 1 🏁\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Description\n", "### Dataset Overview\n", "\n", "- Provide a brief description of the dataset being used, including the source and key variables.\n", "- Example: \"The dataset includes telemetry data from various Formula 1 races, with variables such as speed, throttle position, gear, RPM, and lap time.\"" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "import fastf1\n", "import ydf\n", "import pandas as pd\n", "import fastf1.plotting\n", "import seaborn as sns\n", "import numpy as np\n", "import plotly.io as pio\n", "import plotly.express as px\n", "\n", "fastf1.plotting.setup_mpl(misc_mpl_mods=False)\n", "pd.set_option('display.max_columns', None)\n", "pio.renderers.default = \"vscode\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variables and Data Sources\n", "\n", "- List and describe the main variables in the dataset.\n", " - Example: \"Speed (in km/h), Throttle Position (percentage), Gear (current gear), RPM (revolutions per minute), Lap Time (seconds).\"\n", "- Mention the source of the data.\n", " - Example: \"The data is sourced from Kaggle Formula 1 Telemetry Dataset.\"" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "GRAND_PRIX = 'Imola'\n", "YEAR = 2024\n", "DRIVER = 'HAM'\n", "SESSIONS = [\n", " # 'FP1', \n", " # 'Sprint', \n", " # 'Sprint Qualifying', \n", " # 'Qualifying', \n", " 'Race'\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Preprocessing\n", "\n", "- Placeholder for preprocessing steps.\n", " - Example: \"Data cleaning, handling missing values, converting units if necessary, and normalizing features.\"" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "core INFO \tLoading data for Emilia Romagna Grand Prix - Race [v3.3.6]\n", "INFO:fastf1.fastf1.core:Loading data for Emilia Romagna Grand Prix - Race [v3.3.6]\n", "req INFO \tUsing cached data for session_info\n", "INFO:fastf1.fastf1.req:Using cached data for session_info\n", "req INFO \tUsing cached data for driver_info\n", "INFO:fastf1.fastf1.req:Using cached data for driver_info\n", "req INFO \tUsing cached data for session_status_data\n", "INFO:fastf1.fastf1.req:Using cached data for session_status_data\n", "req INFO \tUsing cached data for lap_count\n", "INFO:fastf1.fastf1.req:Using cached data for lap_count\n", "req INFO \tUsing cached data for track_status_data\n", "INFO:fastf1.fastf1.req:Using cached data for track_status_data\n", "req INFO \tUsing cached data for _extended_timing_data\n", "INFO:fastf1.fastf1.req:Using cached data for _extended_timing_data\n", "req INFO \tUsing cached data for timing_app_data\n", "INFO:fastf1.fastf1.req:Using cached data for timing_app_data\n", "core INFO \tProcessing timing data...\n", "INFO:fastf1.fastf1.core:Processing timing data...\n", "req INFO \tUsing cached data for car_data\n", "INFO:fastf1.fastf1.req:Using cached data for car_data\n", "req INFO \tUsing cached data for position_data\n", "INFO:fastf1.fastf1.req:Using cached data for position_data\n", "core INFO \tFinished loading data for 20 drivers: ['1', '4', '16', '81', '55', '44', '63', '11', '18', '22', '27', '20', '3', '31', '24', '10', '2', '77', '14', '23']\n", "INFO:fastf1.fastf1.core:Finished loading data for 20 drivers: ['1', '4', '16', '81', '55', '44', '63', '11', '18', '22', '27', '20', '3', '31', '24', '10', '2', '77', '14', '23']\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
Time0 days 00:57:04.002000
DriverHAM
DriverNumber44
LapTime0 days 00:01:27.111000
LapNumber1.0
Stint1.0
PitOutTimeNaT
PitInTimeNaT
Sector1TimeNaT
Sector2Time0 days 00:00:29.112000
Sector3Time0 days 00:00:27.601000
Sector1SessionTimeNaT
Sector2SessionTime0 days 00:56:36.529000
Sector3SessionTime0 days 00:57:04.177000
SpeedI1215.0
SpeedI2254.0
SpeedFL278.0
SpeedST279.0
IsPersonalBestFalse
CompoundMEDIUM
TyreLife1.0
FreshTyreTrue
TeamMercedes
LapStartTime0 days 00:55:36.608000
LapStartDate2024-05-19 13:03:16.594000
TrackStatus1
Position7.0
DeletedNone
DeletedReason
FastF1GeneratedFalse
IsAccurateFalse
\n", "
" ], "text/plain": [ " 0\n", "Time 0 days 00:57:04.002000\n", "Driver HAM\n", "DriverNumber 44\n", "LapTime 0 days 00:01:27.111000\n", "LapNumber 1.0\n", "Stint 1.0\n", "PitOutTime NaT\n", "PitInTime NaT\n", "Sector1Time NaT\n", "Sector2Time 0 days 00:00:29.112000\n", "Sector3Time 0 days 00:00:27.601000\n", "Sector1SessionTime NaT\n", "Sector2SessionTime 0 days 00:56:36.529000\n", "Sector3SessionTime 0 days 00:57:04.177000\n", "SpeedI1 215.0\n", "SpeedI2 254.0\n", "SpeedFL 278.0\n", "SpeedST 279.0\n", "IsPersonalBest False\n", "Compound MEDIUM\n", "TyreLife 1.0\n", "FreshTyre True\n", "Team Mercedes\n", "LapStartTime 0 days 00:55:36.608000\n", "LapStartDate 2024-05-19 13:03:16.594000\n", "TrackStatus 1\n", "Position 7.0\n", "Deleted None\n", "DeletedReason \n", "FastF1Generated False\n", "IsAccurate False" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_laps = pd.DataFrame()\n", "all_laps = pd.DataFrame()\n", "for session_name in SESSIONS:\n", " session = fastf1.get_session(YEAR, GRAND_PRIX, session_name)\n", " session.load(weather=False, messages=False)\n", " laps = session.laps\n", " drivers_lap = laps.pick_driver(DRIVER)\n", " if session_name == 'Race':\n", " # let's take the last 10 laps of the race to simulate at the end of the experiment\n", " # as if we were in the race\n", " test_laps = drivers_lap[-10:]\n", " drivers_lap = drivers_lap[:-10]\n", " all_laps = pd.concat([all_laps, drivers_lap], ignore_index=True)\n", "\n", "all_laps.head(1).transpose()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some info about the data:\n", "- **SpeedI1** (float): Speedtrap sector 1 [km/h]\n", "- **SpeedI2** (float): Speedtrap sector 2 [km/h]\n", "- **SpeedFL** (float): Speedtrap at finish line [km/h]\n", "- **SpeedST** (float): Speedtrap on longest straight [km/h]\n", "- All time related data is in `timedelta` format\n", "\n", "We must drop the SpeedFL columns, since it's only obtained at the end of the lap. So its useless to lap time prediction.\n", "\n", "Let's check if any lap was deleted" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_laps['Deleted'].count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that some data is useless for our experiment, like the lap number and the driver number. We will drop them to make our life easier" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TimeDriverDriverNumberLapTimeLapNumberStintPitOutTimePitInTimeSector1TimeSector2TimeSector3TimeSector1SessionTimeSector2SessionTimeSector3SessionTimeSpeedI1SpeedI2SpeedFLSpeedSTIsPersonalBestCompoundTyreLifeFreshTyreTeamLapStartTimeLapStartDateTrackStatusPositionDeletedDeletedReasonFastF1GeneratedIsAccurate
03424.002HAM440 days 00:01:27.1110001.01.0NaTNaTNaN29.11227.601NaT0 days 00:56:36.5290000 days 00:57:04.177000215.0254.0278.0279.0FalseMEDIUM1.0TrueMercedes0 days 00:55:36.6080002024-05-19 13:03:16.59417.0NoneFalseFalse
13505.843HAM440 days 00:01:21.8410002.01.0NaTNaT25.97528.53727.3290 days 00:57:29.9570000 days 00:57:58.4940000 days 00:58:25.823000NaN254.0278.0280.0TrueMEDIUM2.0TrueMercedes0 days 00:57:04.0020002024-05-19 13:04:43.98817.0NoneFalseTrue
23587.065HAM440 days 00:01:21.2220003.01.0NaTNaT25.39828.58927.2350 days 00:58:51.2210000 days 00:59:19.8100000 days 00:59:47.045000213.0251.0276.0282.0TrueMEDIUM3.0TrueMercedes0 days 00:58:25.8430002024-05-19 13:06:05.82917.0NoneFalseTrue
33668.780HAM440 days 00:01:21.7150004.01.0NaTNaT25.82628.58027.3090 days 01:00:12.8710000 days 01:00:41.4510000 days 01:01:08.760000215.0252.0276.0282.0FalseMEDIUM4.0TrueMercedes0 days 00:59:47.0650002024-05-19 13:07:27.05117.0NoneFalseTrue
43750.324HAM440 days 00:01:21.5440005.01.0NaTNaT25.53228.69527.3170 days 01:01:34.2920000 days 01:02:02.9870000 days 01:02:30.304000211.0247.0276.0280.0FalseMEDIUM5.0TrueMercedes0 days 01:01:08.7800002024-05-19 13:08:48.76617.0NoneFalseTrue
53831.893HAM440 days 00:01:21.5690006.01.0NaTNaT25.78328.50127.2850 days 01:02:56.0870000 days 01:03:24.5880000 days 01:03:51.873000213.0253.0276.0284.0FalseMEDIUM6.0TrueMercedes0 days 01:02:30.3240002024-05-19 13:10:10.31017.0NoneFalseTrue
63913.339HAM440 days 00:01:21.4460007.01.0NaTNaT25.82028.32327.3030 days 01:04:17.6930000 days 01:04:46.0160000 days 01:05:13.319000214.0251.0276.0285.0FalseMEDIUM7.0TrueMercedes0 days 01:03:51.8930002024-05-19 13:11:31.87917.0NoneFalseTrue
73994.932HAM440 days 00:01:21.5930008.01.0NaTNaT25.80328.44627.3440 days 01:05:39.1220000 days 01:06:07.5680000 days 01:06:34.912000212.0249.0277.0284.0FalseMEDIUM8.0TrueMercedes0 days 01:05:13.3390002024-05-19 13:12:53.32517.0NoneFalseTrue
84076.442HAM440 days 00:01:21.5100009.01.0NaTNaT25.86128.43127.2180 days 01:07:00.7730000 days 01:07:29.2040000 days 01:07:56.422000216.0254.0277.0286.0FalseMEDIUM9.0TrueMercedes0 days 01:06:34.9320002024-05-19 13:14:14.91817.0NoneFalseTrue
94157.986HAM440 days 00:01:21.54400010.01.0NaTNaT25.76928.40727.3680 days 01:08:22.1910000 days 01:08:50.5980000 days 01:09:17.966000213.0255.0277.0283.0FalseMEDIUM10.0TrueMercedes0 days 01:07:56.4420002024-05-19 13:15:36.42817.0NoneFalseTrue
104239.458HAM440 days 00:01:21.47200011.01.0NaTNaT25.43528.66127.3760 days 01:09:43.4010000 days 01:10:12.0620000 days 01:10:39.438000NaN251.0277.0283.0FalseMEDIUM11.0TrueMercedes0 days 01:09:17.9860002024-05-19 13:16:57.97217.0NoneFalseTrue
114321.214HAM440 days 00:01:21.75600012.01.0NaTNaT25.88028.57727.2990 days 01:11:05.3180000 days 01:11:33.8950000 days 01:12:01.194000NaN252.0277.0284.0FalseMEDIUM12.0TrueMercedes0 days 01:10:39.4580002024-05-19 13:18:19.44417.0NoneFalseTrue
124402.981HAM440 days 00:01:21.76700013.01.0NaTNaT25.89028.58127.2960 days 01:12:27.0840000 days 01:12:55.6650000 days 01:13:22.961000NaN252.0277.0289.0FalseMEDIUM13.0TrueMercedes0 days 01:12:01.2140002024-05-19 13:19:41.20017.0NoneFalseTrue
134485.061HAM440 days 00:01:22.08000014.01.0NaTNaT25.91028.53027.6400 days 01:13:48.8710000 days 01:14:17.4010000 days 01:14:45.041000208.0251.0275.0283.0FalseMEDIUM14.0TrueMercedes0 days 01:13:22.9810002024-05-19 13:21:02.96717.0NoneFalseTrue
144566.790HAM440 days 00:01:21.72900015.01.0NaTNaT25.73628.56127.4320 days 01:15:10.7770000 days 01:15:39.3380000 days 01:16:06.770000214.0255.0278.0286.0FalseMEDIUM15.0TrueMercedes0 days 01:14:45.0610002024-05-19 13:22:25.04717.0NoneFalseTrue
154648.989HAM440 days 00:01:22.19900016.01.0NaTNaT25.83728.61427.7480 days 01:16:32.6070000 days 01:17:01.2210000 days 01:17:28.969000213.0252.0278.0284.0FalseMEDIUM16.0TrueMercedes0 days 01:16:06.7900002024-05-19 13:23:46.77617.0NoneFalseTrue
164730.835HAM440 days 00:01:21.84600017.01.0NaTNaT25.74528.45927.6420 days 01:17:54.7140000 days 01:18:23.1730000 days 01:18:50.815000214.0253.0276.0285.0FalseMEDIUM17.0TrueMercedes0 days 01:17:28.9890002024-05-19 13:25:08.97517.0NoneFalseTrue
174812.855HAM440 days 00:01:22.02000018.01.0NaTNaT25.94028.62827.4520 days 01:19:16.7550000 days 01:19:45.3830000 days 01:20:12.835000211.0253.0278.0285.0FalseMEDIUM18.0TrueMercedes0 days 01:18:50.8350002024-05-19 13:26:30.82117.0NoneFalseTrue
184894.829HAM440 days 00:01:21.97400019.01.0NaTNaT25.79928.61727.5580 days 01:20:38.6340000 days 01:21:07.2510000 days 01:21:34.809000214.0253.0277.0284.0FalseMEDIUM19.0TrueMercedes0 days 01:20:12.8550002024-05-19 13:27:52.84117.0NoneFalseTrue
194977.051HAM440 days 00:01:22.22200020.01.0NaTNaT25.83728.63627.7490 days 01:22:00.6460000 days 01:22:29.2820000 days 01:22:57.031000213.0253.0275.0286.0FalseMEDIUM20.0TrueMercedes0 days 01:21:34.8290002024-05-19 13:29:14.81517.0NoneFalseTrue
205059.326HAM440 days 00:01:22.27500021.01.0NaTNaT25.98328.65027.6420 days 01:23:23.0140000 days 01:23:51.6640000 days 01:24:19.306000212.0252.0278.0287.0FalseMEDIUM21.0TrueMercedes0 days 01:22:57.0510002024-05-19 13:30:37.03716.0NoneFalseTrue
215141.172HAM440 days 00:01:21.84600022.01.0NaTNaT25.74628.63627.4640 days 01:24:45.0520000 days 01:25:13.6880000 days 01:25:41.152000NaN253.0277.0282.0FalseMEDIUM22.0TrueMercedes0 days 01:24:19.3260002024-05-19 13:31:59.31216.0NoneFalseTrue
225222.733HAM440 days 00:01:21.56100023.01.0NaTNaT25.73028.49327.3380 days 01:26:06.8820000 days 01:26:35.3750000 days 01:27:02.713000216.0254.0277.0284.0FalseMEDIUM23.0TrueMercedes0 days 01:25:41.1720002024-05-19 13:33:21.15815.0NoneFalseTrue
235305.078HAM440 days 00:01:22.34500024.01.0NaTNaT25.73529.20627.4040 days 01:27:28.4480000 days 01:27:57.6540000 days 01:28:25.058000212.0253.0277.0283.0FalseMEDIUM24.0TrueMercedes0 days 01:27:02.7330002024-05-19 13:34:42.71914.0NoneFalseTrue
245386.570HAM440 days 00:01:21.49200025.01.0NaTNaT25.73228.51627.2440 days 01:28:50.7900000 days 01:29:19.3060000 days 01:29:46.550000215.0255.0278.0285.0FalseMEDIUM25.0TrueMercedes0 days 01:28:25.0780002024-05-19 13:36:05.06413.0NoneFalseTrue
255472.916HAM440 days 00:01:26.34600026.01.0NaTNaT25.87332.14528.3280 days 01:30:12.4230000 days 01:30:44.5680000 days 01:31:12.896000213.0213.0277.0291.0FalseMEDIUM26.0TrueMercedes0 days 01:29:46.5700002024-05-19 13:37:26.55612.0NoneFalseTrue
265559.131HAM440 days 00:01:26.21500027.01.0NaT0 days 01:32:34.72900025.43928.83331.9430 days 01:31:38.3350000 days 01:32:07.1680000 days 01:32:39.111000210.0245.0NaN278.0FalseMEDIUM27.0TrueMercedes0 days 01:31:12.9160002024-05-19 13:38:52.90213.0NoneFalseFalse
275662.537HAM440 days 00:01:43.40600028.02.00 days 01:33:04.332000NaT48.22328.21926.9640 days 01:33:27.3340000 days 01:33:55.5530000 days 01:34:22.517000213.0254.0277.0280.0FalseHARD1.0TrueMercedes0 days 01:32:39.1310002024-05-19 13:40:19.11719.0NoneFalseFalse
285743.130HAM440 days 00:01:20.59300029.02.0NaTNaT25.50528.12326.9650 days 01:34:48.0220000 days 01:35:16.1450000 days 01:35:43.110000217.0253.0277.0282.0TrueHARD2.0TrueMercedes0 days 01:34:22.5370002024-05-19 13:42:02.52319.0NoneFalseTrue
295823.716HAM440 days 00:01:20.58600030.02.0NaTNaT25.53728.08926.9600 days 01:36:08.6470000 days 01:36:36.7360000 days 01:37:03.696000215.0252.0279.0284.0TrueHARD3.0TrueMercedes0 days 01:35:43.1300002024-05-19 13:43:23.11619.0NoneFalseTrue
305905.453HAM440 days 00:01:21.73700031.02.0NaTNaT25.69228.67427.3710 days 01:37:29.3880000 days 01:37:58.0620000 days 01:38:25.433000214.0256.0276.0284.0FalseHARD4.0TrueMercedes0 days 01:37:03.7160002024-05-19 13:44:43.70218.0NoneFalseTrue
315986.613HAM440 days 00:01:21.16000032.02.0NaTNaT25.67028.30027.1900 days 01:38:51.1030000 days 01:39:19.4030000 days 01:39:46.593000NaN252.0278.0282.0FalseHARD5.0TrueMercedes0 days 01:38:25.4530002024-05-19 13:46:05.43918.0NoneFalseTrue
326067.555HAM440 days 00:01:20.94200033.02.0NaTNaT25.70528.18827.0490 days 01:40:12.2980000 days 01:40:40.4860000 days 01:41:07.535000216.0257.0279.0285.0FalseHARD6.0TrueMercedes0 days 01:39:46.6130002024-05-19 13:47:26.59918.0NoneFalseTrue
336148.519HAM440 days 00:01:20.96400034.02.0NaTNaT25.57028.26327.1310 days 01:41:33.1050000 days 01:42:01.3680000 days 01:42:28.499000218.0257.0282.0287.0FalseHARD7.0TrueMercedes0 days 01:41:07.5550002024-05-19 13:48:47.54118.0NoneFalseTrue
346230.684HAM440 days 00:01:22.16500035.02.0NaTNaT25.72828.89827.5390 days 01:42:54.2270000 days 01:43:23.1250000 days 01:43:50.664000209.0255.0282.0286.0FalseHARD8.0TrueMercedes0 days 01:42:28.5190002024-05-19 13:50:08.50518.0NoneFalseTrue
356313.742HAM440 days 00:01:23.05800036.02.0NaTNaT26.52528.96827.5650 days 01:44:17.1890000 days 01:44:46.1570000 days 01:45:13.722000211.0253.0283.0283.0FalseHARD9.0TrueMercedes0 days 01:43:50.6840002024-05-19 13:51:30.67018.0NoneFalseTrue
366394.490HAM440 days 00:01:20.74800037.02.0NaTNaT25.19128.36027.1970 days 01:45:38.9130000 days 01:46:07.2730000 days 01:46:34.470000208.0251.0278.0279.0FalseHARD10.0TrueMercedes0 days 01:45:13.7420002024-05-19 13:52:53.72817.0NoneFalseTrue
376475.036HAM440 days 00:01:20.54600038.02.0NaTNaT25.56028.10826.8780 days 01:47:00.0300000 days 01:47:28.1380000 days 01:47:55.016000219.0255.0279.0289.0TrueHARD11.0TrueMercedes0 days 01:46:34.4900002024-05-19 13:54:14.47617.0NoneFalseTrue
386555.713HAM440 days 00:01:20.67700039.02.0NaTNaT25.64228.13226.9030 days 01:48:20.6580000 days 01:48:48.7900000 days 01:49:15.693000217.0256.0280.0285.0FalseHARD12.0TrueMercedes0 days 01:47:55.0360002024-05-19 13:55:35.02217.0NoneFalseTrue
396636.550HAM440 days 00:01:20.83700040.02.0NaTNaT25.58728.16127.0890 days 01:49:41.2800000 days 01:50:09.4410000 days 01:50:36.530000214.0252.0279.0281.0FalseHARD13.0TrueMercedes0 days 01:49:15.7130002024-05-19 13:56:55.69917.0NoneFalseTrue
406717.189HAM440 days 00:01:20.63900041.02.0NaTNaT25.48428.12627.0290 days 01:51:02.0140000 days 01:51:30.1400000 days 01:51:57.169000216.0255.0278.0282.0FalseHARD14.0TrueMercedes0 days 01:50:36.5500002024-05-19 13:58:16.53617.0NoneFalseTrue
416797.623HAM440 days 00:01:20.43400042.02.0NaTNaT25.54727.98226.9050 days 01:52:22.7160000 days 01:52:50.6980000 days 01:53:17.603000NaN256.0280.0285.0TrueHARD15.0TrueMercedes0 days 01:51:57.1890002024-05-19 13:59:37.17517.0NoneFalseTrue
426877.954HAM440 days 00:01:20.33100043.02.0NaTNaT25.44327.96126.9270 days 01:53:43.0460000 days 01:54:11.0070000 days 01:54:37.934000218.0258.0281.0285.0TrueHARD16.0TrueMercedes0 days 01:53:17.6230002024-05-19 14:00:57.60917.0NoneFalseTrue
436958.797HAM440 days 00:01:20.84300044.02.0NaTNaT25.50728.26727.0690 days 01:55:03.4410000 days 01:55:31.7080000 days 01:55:58.777000206.0258.0280.0284.0FalseHARD17.0TrueMercedes0 days 01:54:37.9540002024-05-19 14:02:17.94017.0NoneFalseTrue
447039.378HAM440 days 00:01:20.58100045.02.0NaTNaT25.45428.19926.9280 days 01:56:24.2310000 days 01:56:52.4300000 days 01:57:19.358000213.0257.0280.0285.0FalseHARD18.0TrueMercedes0 days 01:55:58.7970002024-05-19 14:03:38.78317.0NoneFalseTrue
457120.322HAM440 days 00:01:20.94400046.02.0NaTNaT25.59328.29627.0550 days 01:57:44.9510000 days 01:58:13.2470000 days 01:58:40.302000211.0258.0280.0287.0FalseHARD19.0TrueMercedes0 days 01:57:19.3780002024-05-19 14:04:59.36417.0NoneFalseTrue
467201.391HAM440 days 00:01:21.06900047.02.0NaTNaT25.57828.40327.0880 days 01:59:05.8800000 days 01:59:34.2830000 days 02:00:01.371000214.0257.0280.0284.0FalseHARD20.0TrueMercedes0 days 01:58:40.3220002024-05-19 14:06:20.30817.0NoneFalseTrue
477282.312HAM440 days 00:01:20.92100048.02.0NaTNaT25.55628.34827.0170 days 02:00:26.9270000 days 02:00:55.2750000 days 02:01:22.292000212.0255.0280.0283.0FalseHARD21.0TrueMercedes0 days 02:00:01.3910002024-05-19 14:07:41.37717.0NoneFalseTrue
487363.494HAM440 days 00:01:21.18200049.02.0NaTNaT25.57628.49027.1160 days 02:01:47.8680000 days 02:02:16.3580000 days 02:02:43.474000216.0256.0280.0286.0FalseHARD22.0TrueMercedes0 days 02:01:22.3120002024-05-19 14:09:02.29817.0NoneFalseTrue
497444.192HAM440 days 00:01:20.69800050.02.0NaTNaT25.37928.22327.0960 days 02:03:08.8530000 days 02:03:37.0760000 days 02:04:04.172000NaN255.0281.0284.0FalseHARD23.0TrueMercedes0 days 02:02:43.4940002024-05-19 14:10:23.48017.0NoneFalseTrue
507524.968HAM440 days 00:01:20.77600051.02.0NaTNaT25.48128.31926.9760 days 02:04:29.6530000 days 02:04:57.9720000 days 02:05:24.948000218.0258.0281.0287.0FalseHARD24.0TrueMercedes0 days 02:04:04.1920002024-05-19 14:11:44.17817.0NoneFalseTrue
517606.144HAM440 days 00:01:21.17600052.02.0NaTNaT25.58028.33627.2600 days 02:05:50.5280000 days 02:06:18.8640000 days 02:06:46.124000215.0257.0282.0287.0FalseHARD25.0TrueMercedes0 days 02:05:24.9680002024-05-19 14:13:04.95416.0NoneFalseTrue
527687.559HAM440 days 00:01:21.41500053.02.0NaTNaT25.78128.42627.2080 days 02:07:11.9050000 days 02:07:40.3310000 days 02:08:07.539000211.0258.0280.0286.0FalseHARD26.0TrueMercedes0 days 02:06:46.1440002024-05-19 14:14:26.13016.0NoneFalseTrue
\n", "
" ], "text/plain": [ " Time Driver DriverNumber LapTime LapNumber Stint \\\n", "0 3424.002 HAM 44 0 days 00:01:27.111000 1.0 1.0 \n", "1 3505.843 HAM 44 0 days 00:01:21.841000 2.0 1.0 \n", "2 3587.065 HAM 44 0 days 00:01:21.222000 3.0 1.0 \n", "3 3668.780 HAM 44 0 days 00:01:21.715000 4.0 1.0 \n", "4 3750.324 HAM 44 0 days 00:01:21.544000 5.0 1.0 \n", "5 3831.893 HAM 44 0 days 00:01:21.569000 6.0 1.0 \n", "6 3913.339 HAM 44 0 days 00:01:21.446000 7.0 1.0 \n", "7 3994.932 HAM 44 0 days 00:01:21.593000 8.0 1.0 \n", "8 4076.442 HAM 44 0 days 00:01:21.510000 9.0 1.0 \n", "9 4157.986 HAM 44 0 days 00:01:21.544000 10.0 1.0 \n", "10 4239.458 HAM 44 0 days 00:01:21.472000 11.0 1.0 \n", "11 4321.214 HAM 44 0 days 00:01:21.756000 12.0 1.0 \n", "12 4402.981 HAM 44 0 days 00:01:21.767000 13.0 1.0 \n", "13 4485.061 HAM 44 0 days 00:01:22.080000 14.0 1.0 \n", "14 4566.790 HAM 44 0 days 00:01:21.729000 15.0 1.0 \n", "15 4648.989 HAM 44 0 days 00:01:22.199000 16.0 1.0 \n", "16 4730.835 HAM 44 0 days 00:01:21.846000 17.0 1.0 \n", "17 4812.855 HAM 44 0 days 00:01:22.020000 18.0 1.0 \n", "18 4894.829 HAM 44 0 days 00:01:21.974000 19.0 1.0 \n", "19 4977.051 HAM 44 0 days 00:01:22.222000 20.0 1.0 \n", "20 5059.326 HAM 44 0 days 00:01:22.275000 21.0 1.0 \n", "21 5141.172 HAM 44 0 days 00:01:21.846000 22.0 1.0 \n", "22 5222.733 HAM 44 0 days 00:01:21.561000 23.0 1.0 \n", "23 5305.078 HAM 44 0 days 00:01:22.345000 24.0 1.0 \n", "24 5386.570 HAM 44 0 days 00:01:21.492000 25.0 1.0 \n", "25 5472.916 HAM 44 0 days 00:01:26.346000 26.0 1.0 \n", "26 5559.131 HAM 44 0 days 00:01:26.215000 27.0 1.0 \n", "27 5662.537 HAM 44 0 days 00:01:43.406000 28.0 2.0 \n", "28 5743.130 HAM 44 0 days 00:01:20.593000 29.0 2.0 \n", "29 5823.716 HAM 44 0 days 00:01:20.586000 30.0 2.0 \n", "30 5905.453 HAM 44 0 days 00:01:21.737000 31.0 2.0 \n", "31 5986.613 HAM 44 0 days 00:01:21.160000 32.0 2.0 \n", "32 6067.555 HAM 44 0 days 00:01:20.942000 33.0 2.0 \n", "33 6148.519 HAM 44 0 days 00:01:20.964000 34.0 2.0 \n", "34 6230.684 HAM 44 0 days 00:01:22.165000 35.0 2.0 \n", "35 6313.742 HAM 44 0 days 00:01:23.058000 36.0 2.0 \n", "36 6394.490 HAM 44 0 days 00:01:20.748000 37.0 2.0 \n", "37 6475.036 HAM 44 0 days 00:01:20.546000 38.0 2.0 \n", "38 6555.713 HAM 44 0 days 00:01:20.677000 39.0 2.0 \n", "39 6636.550 HAM 44 0 days 00:01:20.837000 40.0 2.0 \n", "40 6717.189 HAM 44 0 days 00:01:20.639000 41.0 2.0 \n", "41 6797.623 HAM 44 0 days 00:01:20.434000 42.0 2.0 \n", "42 6877.954 HAM 44 0 days 00:01:20.331000 43.0 2.0 \n", "43 6958.797 HAM 44 0 days 00:01:20.843000 44.0 2.0 \n", "44 7039.378 HAM 44 0 days 00:01:20.581000 45.0 2.0 \n", "45 7120.322 HAM 44 0 days 00:01:20.944000 46.0 2.0 \n", "46 7201.391 HAM 44 0 days 00:01:21.069000 47.0 2.0 \n", "47 7282.312 HAM 44 0 days 00:01:20.921000 48.0 2.0 \n", "48 7363.494 HAM 44 0 days 00:01:21.182000 49.0 2.0 \n", "49 7444.192 HAM 44 0 days 00:01:20.698000 50.0 2.0 \n", "50 7524.968 HAM 44 0 days 00:01:20.776000 51.0 2.0 \n", "51 7606.144 HAM 44 0 days 00:01:21.176000 52.0 2.0 \n", "52 7687.559 HAM 44 0 days 00:01:21.415000 53.0 2.0 \n", "\n", " PitOutTime PitInTime Sector1Time Sector2Time \\\n", "0 NaT NaT NaN 29.112 \n", "1 NaT NaT 25.975 28.537 \n", "2 NaT NaT 25.398 28.589 \n", "3 NaT NaT 25.826 28.580 \n", "4 NaT NaT 25.532 28.695 \n", "5 NaT NaT 25.783 28.501 \n", "6 NaT NaT 25.820 28.323 \n", "7 NaT NaT 25.803 28.446 \n", "8 NaT NaT 25.861 28.431 \n", "9 NaT NaT 25.769 28.407 \n", "10 NaT NaT 25.435 28.661 \n", "11 NaT NaT 25.880 28.577 \n", "12 NaT NaT 25.890 28.581 \n", "13 NaT NaT 25.910 28.530 \n", "14 NaT NaT 25.736 28.561 \n", "15 NaT NaT 25.837 28.614 \n", "16 NaT NaT 25.745 28.459 \n", "17 NaT NaT 25.940 28.628 \n", "18 NaT NaT 25.799 28.617 \n", "19 NaT NaT 25.837 28.636 \n", "20 NaT NaT 25.983 28.650 \n", "21 NaT NaT 25.746 28.636 \n", "22 NaT NaT 25.730 28.493 \n", "23 NaT NaT 25.735 29.206 \n", "24 NaT NaT 25.732 28.516 \n", "25 NaT NaT 25.873 32.145 \n", "26 NaT 0 days 01:32:34.729000 25.439 28.833 \n", "27 0 days 01:33:04.332000 NaT 48.223 28.219 \n", "28 NaT NaT 25.505 28.123 \n", "29 NaT NaT 25.537 28.089 \n", "30 NaT NaT 25.692 28.674 \n", "31 NaT NaT 25.670 28.300 \n", "32 NaT NaT 25.705 28.188 \n", "33 NaT NaT 25.570 28.263 \n", "34 NaT NaT 25.728 28.898 \n", "35 NaT NaT 26.525 28.968 \n", "36 NaT NaT 25.191 28.360 \n", "37 NaT NaT 25.560 28.108 \n", "38 NaT NaT 25.642 28.132 \n", "39 NaT NaT 25.587 28.161 \n", "40 NaT NaT 25.484 28.126 \n", "41 NaT NaT 25.547 27.982 \n", "42 NaT NaT 25.443 27.961 \n", "43 NaT NaT 25.507 28.267 \n", "44 NaT NaT 25.454 28.199 \n", "45 NaT NaT 25.593 28.296 \n", "46 NaT NaT 25.578 28.403 \n", "47 NaT NaT 25.556 28.348 \n", "48 NaT NaT 25.576 28.490 \n", "49 NaT NaT 25.379 28.223 \n", "50 NaT NaT 25.481 28.319 \n", "51 NaT NaT 25.580 28.336 \n", "52 NaT NaT 25.781 28.426 \n", "\n", " Sector3Time Sector1SessionTime Sector2SessionTime \\\n", "0 27.601 NaT 0 days 00:56:36.529000 \n", "1 27.329 0 days 00:57:29.957000 0 days 00:57:58.494000 \n", "2 27.235 0 days 00:58:51.221000 0 days 00:59:19.810000 \n", "3 27.309 0 days 01:00:12.871000 0 days 01:00:41.451000 \n", "4 27.317 0 days 01:01:34.292000 0 days 01:02:02.987000 \n", "5 27.285 0 days 01:02:56.087000 0 days 01:03:24.588000 \n", "6 27.303 0 days 01:04:17.693000 0 days 01:04:46.016000 \n", "7 27.344 0 days 01:05:39.122000 0 days 01:06:07.568000 \n", "8 27.218 0 days 01:07:00.773000 0 days 01:07:29.204000 \n", "9 27.368 0 days 01:08:22.191000 0 days 01:08:50.598000 \n", "10 27.376 0 days 01:09:43.401000 0 days 01:10:12.062000 \n", "11 27.299 0 days 01:11:05.318000 0 days 01:11:33.895000 \n", "12 27.296 0 days 01:12:27.084000 0 days 01:12:55.665000 \n", "13 27.640 0 days 01:13:48.871000 0 days 01:14:17.401000 \n", "14 27.432 0 days 01:15:10.777000 0 days 01:15:39.338000 \n", "15 27.748 0 days 01:16:32.607000 0 days 01:17:01.221000 \n", "16 27.642 0 days 01:17:54.714000 0 days 01:18:23.173000 \n", "17 27.452 0 days 01:19:16.755000 0 days 01:19:45.383000 \n", "18 27.558 0 days 01:20:38.634000 0 days 01:21:07.251000 \n", "19 27.749 0 days 01:22:00.646000 0 days 01:22:29.282000 \n", "20 27.642 0 days 01:23:23.014000 0 days 01:23:51.664000 \n", "21 27.464 0 days 01:24:45.052000 0 days 01:25:13.688000 \n", "22 27.338 0 days 01:26:06.882000 0 days 01:26:35.375000 \n", "23 27.404 0 days 01:27:28.448000 0 days 01:27:57.654000 \n", "24 27.244 0 days 01:28:50.790000 0 days 01:29:19.306000 \n", "25 28.328 0 days 01:30:12.423000 0 days 01:30:44.568000 \n", "26 31.943 0 days 01:31:38.335000 0 days 01:32:07.168000 \n", "27 26.964 0 days 01:33:27.334000 0 days 01:33:55.553000 \n", "28 26.965 0 days 01:34:48.022000 0 days 01:35:16.145000 \n", "29 26.960 0 days 01:36:08.647000 0 days 01:36:36.736000 \n", "30 27.371 0 days 01:37:29.388000 0 days 01:37:58.062000 \n", "31 27.190 0 days 01:38:51.103000 0 days 01:39:19.403000 \n", "32 27.049 0 days 01:40:12.298000 0 days 01:40:40.486000 \n", "33 27.131 0 days 01:41:33.105000 0 days 01:42:01.368000 \n", "34 27.539 0 days 01:42:54.227000 0 days 01:43:23.125000 \n", "35 27.565 0 days 01:44:17.189000 0 days 01:44:46.157000 \n", "36 27.197 0 days 01:45:38.913000 0 days 01:46:07.273000 \n", "37 26.878 0 days 01:47:00.030000 0 days 01:47:28.138000 \n", "38 26.903 0 days 01:48:20.658000 0 days 01:48:48.790000 \n", "39 27.089 0 days 01:49:41.280000 0 days 01:50:09.441000 \n", "40 27.029 0 days 01:51:02.014000 0 days 01:51:30.140000 \n", "41 26.905 0 days 01:52:22.716000 0 days 01:52:50.698000 \n", "42 26.927 0 days 01:53:43.046000 0 days 01:54:11.007000 \n", "43 27.069 0 days 01:55:03.441000 0 days 01:55:31.708000 \n", "44 26.928 0 days 01:56:24.231000 0 days 01:56:52.430000 \n", "45 27.055 0 days 01:57:44.951000 0 days 01:58:13.247000 \n", "46 27.088 0 days 01:59:05.880000 0 days 01:59:34.283000 \n", "47 27.017 0 days 02:00:26.927000 0 days 02:00:55.275000 \n", "48 27.116 0 days 02:01:47.868000 0 days 02:02:16.358000 \n", "49 27.096 0 days 02:03:08.853000 0 days 02:03:37.076000 \n", "50 26.976 0 days 02:04:29.653000 0 days 02:04:57.972000 \n", "51 27.260 0 days 02:05:50.528000 0 days 02:06:18.864000 \n", "52 27.208 0 days 02:07:11.905000 0 days 02:07:40.331000 \n", "\n", " Sector3SessionTime SpeedI1 SpeedI2 SpeedFL SpeedST IsPersonalBest \\\n", "0 0 days 00:57:04.177000 215.0 254.0 278.0 279.0 False \n", "1 0 days 00:58:25.823000 NaN 254.0 278.0 280.0 True \n", "2 0 days 00:59:47.045000 213.0 251.0 276.0 282.0 True \n", "3 0 days 01:01:08.760000 215.0 252.0 276.0 282.0 False \n", "4 0 days 01:02:30.304000 211.0 247.0 276.0 280.0 False \n", "5 0 days 01:03:51.873000 213.0 253.0 276.0 284.0 False \n", "6 0 days 01:05:13.319000 214.0 251.0 276.0 285.0 False \n", "7 0 days 01:06:34.912000 212.0 249.0 277.0 284.0 False \n", "8 0 days 01:07:56.422000 216.0 254.0 277.0 286.0 False \n", "9 0 days 01:09:17.966000 213.0 255.0 277.0 283.0 False \n", "10 0 days 01:10:39.438000 NaN 251.0 277.0 283.0 False \n", "11 0 days 01:12:01.194000 NaN 252.0 277.0 284.0 False \n", "12 0 days 01:13:22.961000 NaN 252.0 277.0 289.0 False \n", "13 0 days 01:14:45.041000 208.0 251.0 275.0 283.0 False \n", "14 0 days 01:16:06.770000 214.0 255.0 278.0 286.0 False \n", "15 0 days 01:17:28.969000 213.0 252.0 278.0 284.0 False \n", "16 0 days 01:18:50.815000 214.0 253.0 276.0 285.0 False \n", "17 0 days 01:20:12.835000 211.0 253.0 278.0 285.0 False \n", "18 0 days 01:21:34.809000 214.0 253.0 277.0 284.0 False \n", "19 0 days 01:22:57.031000 213.0 253.0 275.0 286.0 False \n", "20 0 days 01:24:19.306000 212.0 252.0 278.0 287.0 False \n", "21 0 days 01:25:41.152000 NaN 253.0 277.0 282.0 False \n", "22 0 days 01:27:02.713000 216.0 254.0 277.0 284.0 False \n", "23 0 days 01:28:25.058000 212.0 253.0 277.0 283.0 False \n", "24 0 days 01:29:46.550000 215.0 255.0 278.0 285.0 False \n", "25 0 days 01:31:12.896000 213.0 213.0 277.0 291.0 False \n", "26 0 days 01:32:39.111000 210.0 245.0 NaN 278.0 False \n", "27 0 days 01:34:22.517000 213.0 254.0 277.0 280.0 False \n", "28 0 days 01:35:43.110000 217.0 253.0 277.0 282.0 True \n", "29 0 days 01:37:03.696000 215.0 252.0 279.0 284.0 True \n", "30 0 days 01:38:25.433000 214.0 256.0 276.0 284.0 False \n", "31 0 days 01:39:46.593000 NaN 252.0 278.0 282.0 False \n", "32 0 days 01:41:07.535000 216.0 257.0 279.0 285.0 False \n", "33 0 days 01:42:28.499000 218.0 257.0 282.0 287.0 False \n", "34 0 days 01:43:50.664000 209.0 255.0 282.0 286.0 False \n", "35 0 days 01:45:13.722000 211.0 253.0 283.0 283.0 False \n", "36 0 days 01:46:34.470000 208.0 251.0 278.0 279.0 False \n", "37 0 days 01:47:55.016000 219.0 255.0 279.0 289.0 True \n", "38 0 days 01:49:15.693000 217.0 256.0 280.0 285.0 False \n", "39 0 days 01:50:36.530000 214.0 252.0 279.0 281.0 False \n", "40 0 days 01:51:57.169000 216.0 255.0 278.0 282.0 False \n", "41 0 days 01:53:17.603000 NaN 256.0 280.0 285.0 True \n", "42 0 days 01:54:37.934000 218.0 258.0 281.0 285.0 True \n", "43 0 days 01:55:58.777000 206.0 258.0 280.0 284.0 False \n", "44 0 days 01:57:19.358000 213.0 257.0 280.0 285.0 False \n", "45 0 days 01:58:40.302000 211.0 258.0 280.0 287.0 False \n", "46 0 days 02:00:01.371000 214.0 257.0 280.0 284.0 False \n", "47 0 days 02:01:22.292000 212.0 255.0 280.0 283.0 False \n", "48 0 days 02:02:43.474000 216.0 256.0 280.0 286.0 False \n", "49 0 days 02:04:04.172000 NaN 255.0 281.0 284.0 False \n", "50 0 days 02:05:24.948000 218.0 258.0 281.0 287.0 False \n", "51 0 days 02:06:46.124000 215.0 257.0 282.0 287.0 False \n", "52 0 days 02:08:07.539000 211.0 258.0 280.0 286.0 False \n", "\n", " Compound TyreLife FreshTyre Team LapStartTime \\\n", "0 MEDIUM 1.0 True Mercedes 0 days 00:55:36.608000 \n", "1 MEDIUM 2.0 True Mercedes 0 days 00:57:04.002000 \n", "2 MEDIUM 3.0 True Mercedes 0 days 00:58:25.843000 \n", "3 MEDIUM 4.0 True Mercedes 0 days 00:59:47.065000 \n", "4 MEDIUM 5.0 True Mercedes 0 days 01:01:08.780000 \n", "5 MEDIUM 6.0 True Mercedes 0 days 01:02:30.324000 \n", "6 MEDIUM 7.0 True Mercedes 0 days 01:03:51.893000 \n", "7 MEDIUM 8.0 True Mercedes 0 days 01:05:13.339000 \n", "8 MEDIUM 9.0 True Mercedes 0 days 01:06:34.932000 \n", "9 MEDIUM 10.0 True Mercedes 0 days 01:07:56.442000 \n", "10 MEDIUM 11.0 True Mercedes 0 days 01:09:17.986000 \n", "11 MEDIUM 12.0 True Mercedes 0 days 01:10:39.458000 \n", "12 MEDIUM 13.0 True Mercedes 0 days 01:12:01.214000 \n", "13 MEDIUM 14.0 True Mercedes 0 days 01:13:22.981000 \n", "14 MEDIUM 15.0 True Mercedes 0 days 01:14:45.061000 \n", "15 MEDIUM 16.0 True Mercedes 0 days 01:16:06.790000 \n", "16 MEDIUM 17.0 True Mercedes 0 days 01:17:28.989000 \n", "17 MEDIUM 18.0 True Mercedes 0 days 01:18:50.835000 \n", "18 MEDIUM 19.0 True Mercedes 0 days 01:20:12.855000 \n", "19 MEDIUM 20.0 True Mercedes 0 days 01:21:34.829000 \n", "20 MEDIUM 21.0 True Mercedes 0 days 01:22:57.051000 \n", "21 MEDIUM 22.0 True Mercedes 0 days 01:24:19.326000 \n", "22 MEDIUM 23.0 True Mercedes 0 days 01:25:41.172000 \n", "23 MEDIUM 24.0 True Mercedes 0 days 01:27:02.733000 \n", "24 MEDIUM 25.0 True Mercedes 0 days 01:28:25.078000 \n", "25 MEDIUM 26.0 True Mercedes 0 days 01:29:46.570000 \n", "26 MEDIUM 27.0 True Mercedes 0 days 01:31:12.916000 \n", "27 HARD 1.0 True Mercedes 0 days 01:32:39.131000 \n", "28 HARD 2.0 True Mercedes 0 days 01:34:22.537000 \n", "29 HARD 3.0 True Mercedes 0 days 01:35:43.130000 \n", "30 HARD 4.0 True Mercedes 0 days 01:37:03.716000 \n", "31 HARD 5.0 True Mercedes 0 days 01:38:25.453000 \n", "32 HARD 6.0 True Mercedes 0 days 01:39:46.613000 \n", "33 HARD 7.0 True Mercedes 0 days 01:41:07.555000 \n", "34 HARD 8.0 True Mercedes 0 days 01:42:28.519000 \n", "35 HARD 9.0 True Mercedes 0 days 01:43:50.684000 \n", "36 HARD 10.0 True Mercedes 0 days 01:45:13.742000 \n", "37 HARD 11.0 True Mercedes 0 days 01:46:34.490000 \n", "38 HARD 12.0 True Mercedes 0 days 01:47:55.036000 \n", "39 HARD 13.0 True Mercedes 0 days 01:49:15.713000 \n", "40 HARD 14.0 True Mercedes 0 days 01:50:36.550000 \n", "41 HARD 15.0 True Mercedes 0 days 01:51:57.189000 \n", "42 HARD 16.0 True Mercedes 0 days 01:53:17.623000 \n", "43 HARD 17.0 True Mercedes 0 days 01:54:37.954000 \n", "44 HARD 18.0 True Mercedes 0 days 01:55:58.797000 \n", "45 HARD 19.0 True Mercedes 0 days 01:57:19.378000 \n", "46 HARD 20.0 True Mercedes 0 days 01:58:40.322000 \n", "47 HARD 21.0 True Mercedes 0 days 02:00:01.391000 \n", "48 HARD 22.0 True Mercedes 0 days 02:01:22.312000 \n", "49 HARD 23.0 True Mercedes 0 days 02:02:43.494000 \n", "50 HARD 24.0 True Mercedes 0 days 02:04:04.192000 \n", "51 HARD 25.0 True Mercedes 0 days 02:05:24.968000 \n", "52 HARD 26.0 True Mercedes 0 days 02:06:46.144000 \n", "\n", " LapStartDate TrackStatus Position Deleted DeletedReason \\\n", "0 2024-05-19 13:03:16.594 1 7.0 None \n", "1 2024-05-19 13:04:43.988 1 7.0 None \n", "2 2024-05-19 13:06:05.829 1 7.0 None \n", "3 2024-05-19 13:07:27.051 1 7.0 None \n", "4 2024-05-19 13:08:48.766 1 7.0 None \n", "5 2024-05-19 13:10:10.310 1 7.0 None \n", "6 2024-05-19 13:11:31.879 1 7.0 None \n", "7 2024-05-19 13:12:53.325 1 7.0 None \n", "8 2024-05-19 13:14:14.918 1 7.0 None \n", "9 2024-05-19 13:15:36.428 1 7.0 None \n", "10 2024-05-19 13:16:57.972 1 7.0 None \n", "11 2024-05-19 13:18:19.444 1 7.0 None \n", "12 2024-05-19 13:19:41.200 1 7.0 None \n", "13 2024-05-19 13:21:02.967 1 7.0 None \n", "14 2024-05-19 13:22:25.047 1 7.0 None \n", "15 2024-05-19 13:23:46.776 1 7.0 None \n", "16 2024-05-19 13:25:08.975 1 7.0 None \n", "17 2024-05-19 13:26:30.821 1 7.0 None \n", "18 2024-05-19 13:27:52.841 1 7.0 None \n", "19 2024-05-19 13:29:14.815 1 7.0 None \n", "20 2024-05-19 13:30:37.037 1 6.0 None \n", "21 2024-05-19 13:31:59.312 1 6.0 None \n", "22 2024-05-19 13:33:21.158 1 5.0 None \n", "23 2024-05-19 13:34:42.719 1 4.0 None \n", "24 2024-05-19 13:36:05.064 1 3.0 None \n", "25 2024-05-19 13:37:26.556 1 2.0 None \n", "26 2024-05-19 13:38:52.902 1 3.0 None \n", "27 2024-05-19 13:40:19.117 1 9.0 None \n", "28 2024-05-19 13:42:02.523 1 9.0 None \n", "29 2024-05-19 13:43:23.116 1 9.0 None \n", "30 2024-05-19 13:44:43.702 1 8.0 None \n", "31 2024-05-19 13:46:05.439 1 8.0 None \n", "32 2024-05-19 13:47:26.599 1 8.0 None \n", "33 2024-05-19 13:48:47.541 1 8.0 None \n", "34 2024-05-19 13:50:08.505 1 8.0 None \n", "35 2024-05-19 13:51:30.670 1 8.0 None \n", "36 2024-05-19 13:52:53.728 1 7.0 None \n", "37 2024-05-19 13:54:14.476 1 7.0 None \n", "38 2024-05-19 13:55:35.022 1 7.0 None \n", "39 2024-05-19 13:56:55.699 1 7.0 None \n", "40 2024-05-19 13:58:16.536 1 7.0 None \n", "41 2024-05-19 13:59:37.175 1 7.0 None \n", "42 2024-05-19 14:00:57.609 1 7.0 None \n", "43 2024-05-19 14:02:17.940 1 7.0 None \n", "44 2024-05-19 14:03:38.783 1 7.0 None \n", "45 2024-05-19 14:04:59.364 1 7.0 None \n", "46 2024-05-19 14:06:20.308 1 7.0 None \n", "47 2024-05-19 14:07:41.377 1 7.0 None \n", "48 2024-05-19 14:09:02.298 1 7.0 None \n", "49 2024-05-19 14:10:23.480 1 7.0 None \n", "50 2024-05-19 14:11:44.178 1 7.0 None \n", "51 2024-05-19 14:13:04.954 1 6.0 None \n", "52 2024-05-19 14:14:26.130 1 6.0 None \n", "\n", " FastF1Generated IsAccurate \n", "0 False False \n", "1 False True \n", "2 False True \n", "3 False True \n", "4 False True \n", "5 False True \n", "6 False True \n", "7 False True \n", "8 False True \n", "9 False True \n", "10 False True \n", "11 False True \n", "12 False True \n", "13 False True \n", "14 False True \n", "15 False True \n", "16 False True \n", "17 False True \n", "18 False True \n", "19 False True \n", "20 False True \n", "21 False True \n", "22 False True \n", "23 False True \n", "24 False True \n", "25 False True \n", "26 False False \n", "27 False False \n", "28 False True \n", "29 False True \n", "30 False True \n", "31 False True \n", "32 False True \n", "33 False True \n", "34 False True \n", "35 False True \n", "36 False True \n", "37 False True \n", "38 False True \n", "39 False True \n", "40 False True \n", "41 False True \n", "42 False True \n", "43 False True \n", "44 False True \n", "45 False True \n", "46 False True \n", "47 False True \n", "48 False True \n", "49 False True \n", "50 False True \n", "51 False True \n", "52 False True " ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_in_timedelta = [\n", " 'Time',\n", " 'Sector1Time',\n", " 'Sector2Time',\n", " 'Sector3Time'\n", "]\n", "\n", "# all_laps_copy = all_laps.copy()\n", "# test_laps_copy = test_laps.copy()\n", "for col in columns_in_timedelta:\n", " col_total_seconds = all_laps[col].dt.total_seconds()\n", " all_laps[col] = None\n", " all_laps[col] = all_laps[col].astype(float)\n", " all_laps.loc[:, col] = col_total_seconds\n", "\n", " # col_total_seconds = test_laps_copy[col].dt.total_seconds()\n", " # test_laps_copy[col] = None\n", " # test_laps_copy[col] = test_laps_copy[col].astype(float)\n", " # test_laps_copy.loc[:, col] = col_total_seconds\n", "\n", "\n", "\n", "# all_laps = all_laps_copy\n", "# test_laps = test_laps_copy\n", "# all_laps.describe().transpose()\n", "all_laps" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "name": "lines", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], "y": [ null, 25.975, 25.398, 25.826, 25.532, 25.783, 25.82, 25.803, 25.861, 25.769, 25.435, 25.88, 25.89, 25.91, 25.736, 25.837, 25.745, 25.94, 25.799, 25.837, 25.983, 25.746, 25.73, 25.735, 25.732, 25.873, 25.439, 48.223, 25.505, 25.537, 25.692, 25.67, 25.705, 25.57, 25.728, 26.525, 25.191, 25.56, 25.642, 25.587, 25.484, 25.547, 25.443, 25.507, 25.454, 25.593, 25.578, 25.556, 25.576, 25.379, 25.481, 25.58, 25.781 ] }, { "mode": "lines+markers", "name": "lines+markers", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], "y": [ 29.112, 28.537, 28.589, 28.58, 28.695, 28.501, 28.323, 28.446, 28.431, 28.407, 28.661, 28.577, 28.581, 28.53, 28.561, 28.614, 28.459, 28.628, 28.617, 28.636, 28.65, 28.636, 28.493, 29.206, 28.516, 32.145, 28.833, 28.219, 28.123, 28.089, 28.674, 28.3, 28.188, 28.263, 28.898, 28.968, 28.36, 28.108, 28.132, 28.161, 28.126, 27.982, 27.961, 28.267, 28.199, 28.296, 28.403, 28.348, 28.49, 28.223, 28.319, 28.336, 28.426 ] }, { "mode": "markers", "name": "markers", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], "y": [ 27.601, 27.329, 27.235, 27.309, 27.317, 27.285, 27.303, 27.344, 27.218, 27.368, 27.376, 27.299, 27.296, 27.64, 27.432, 27.748, 27.642, 27.452, 27.558, 27.749, 27.642, 27.464, 27.338, 27.404, 27.244, 28.328, 31.943, 26.964, 26.965, 26.96, 27.371, 27.19, 27.049, 27.131, 27.539, 27.565, 27.197, 26.878, 26.903, 27.089, 27.029, 26.905, 26.927, 27.069, 26.928, 27.055, 27.088, 27.017, 27.116, 27.096, 26.976, 27.26, 27.208 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "\n", "fig = go.Figure()\n", "fig.add_trace(go.Scatter(x=all_laps[\"LapNumber\"], y=all_laps[\"Sector1Time\"],\n", " mode='lines',\n", " name='lines'))\n", "fig.add_trace(go.Scatter(x=all_laps[\"LapNumber\"], y=all_laps[\"Sector2Time\"],\n", " mode='lines+markers',\n", " name='lines+markers'))\n", "fig.add_trace(go.Scatter(x=all_laps[\"LapNumber\"], y=all_laps[\"Sector3Time\"],\n", " mode='markers', name='markers'))\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se can see that there is a anomaly in the data since the safety car was deployed, this will hurt our model since these lap times are not relevant to the model. So let's drop those laps:" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "x+y", "stackgroup": "one", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], "y": [ null, 25.975, 25.398, 25.826, 25.532, 25.783, 25.82, 25.803, 25.861, 25.769, 25.435, 25.88, 25.89, 25.91, 25.736, 25.837, 25.745, 25.94, 25.799, 25.837, 25.983, 25.746, 25.73, 25.735, 25.505, 25.537, 25.692, 25.67, 25.705, 25.57, 25.728, 26.525, 25.191, 25.56, 25.642, 25.587, 25.484, 25.547, 25.443, 25.507, 25.454, 25.593, 25.578, 25.556, 25.576, 25.379, 25.481, 25.58, 25.781 ] }, { "stackgroup": "one", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], "y": [ 29.112, 28.537, 28.589, 28.58, 28.695, 28.501, 28.323, 28.446, 28.431, 28.407, 28.661, 28.577, 28.581, 28.53, 28.561, 28.614, 28.459, 28.628, 28.617, 28.636, 28.65, 28.636, 28.493, 29.206, 28.123, 28.089, 28.674, 28.3, 28.188, 28.263, 28.898, 28.968, 28.36, 28.108, 28.132, 28.161, 28.126, 27.982, 27.961, 28.267, 28.199, 28.296, 28.403, 28.348, 28.49, 28.223, 28.319, 28.336, 28.426 ] }, { "stackgroup": "one", "type": "scatter", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53 ], "y": [ 27.601, 27.329, 27.235, 27.309, 27.317, 27.285, 27.303, 27.344, 27.218, 27.368, 27.376, 27.299, 27.296, 27.64, 27.432, 27.748, 27.642, 27.452, 27.558, 27.749, 27.642, 27.464, 27.338, 27.404, 26.965, 26.96, 27.371, 27.19, 27.049, 27.131, 27.539, 27.565, 27.197, 26.878, 26.903, 27.089, 27.029, 26.905, 26.927, 27.069, 26.928, 27.055, 27.088, 27.017, 27.116, 27.096, 26.976, 27.26, 27.208 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = all_laps.drop(all_laps[(all_laps.LapNumber < 29) & (all_laps.LapNumber > 24)].index)\n", "\n", "fig = go.Figure()\n", "fig.add_trace(go.Scatter(x=df[\"LapNumber\"], y=df[\"Sector1Time\"],\n", " hoverinfo='x+y',\n", " stackgroup='one'))\n", "fig.add_trace(go.Scatter(x=df[\"LapNumber\"], y=df[\"Sector2Time\"],\n", " stackgroup='one'))\n", "fig.add_trace(go.Scatter(x=df[\"LapNumber\"], y=df[\"Sector3Time\"],\n", " stackgroup='one'))\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
Stint1.0
Sector1TimeNaT
Sector2Time0 days 00:00:29.112000
Sector3Time0 days 00:00:27.601000
SpeedI1215.0
SpeedI2254.0
CompoundMEDIUM
TyreLife1.0
FreshTyreTrue
TrackStatus1
\n", "
" ], "text/plain": [ " 0\n", "Stint 1.0\n", "Sector1Time NaT\n", "Sector2Time 0 days 00:00:29.112000\n", "Sector3Time 0 days 00:00:27.601000\n", "SpeedI1 215.0\n", "SpeedI2 254.0\n", "Compound MEDIUM\n", "TyreLife 1.0\n", "FreshTyre True\n", "TrackStatus 1" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_to_drop = [\n", " 'LapTime',\n", " 'Time',\n", " 'LapNumber',\n", " 'Driver',\n", " 'DriverNumber',\n", " 'Sector1SessionTime',\n", " 'Sector2SessionTime',\n", " 'Sector3SessionTime',\n", " 'PitInTime',\n", " 'PitOutTime',\n", " 'Team',\n", " 'Position',\n", " 'LapStartTime',\n", " 'LapStartDate',\n", " 'IsPersonalBest',\n", " 'Deleted',\n", " 'DeletedReason',\n", " 'FastF1Generated',\n", " 'IsAccurate',\n", " 'SpeedFL',\n", " 'SpeedST'\n", "]\n", "\n", "all_laps = all_laps.drop(columns_to_drop, axis=1)\n", "all_laps.head(1).transpose()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clean the data\n", "\n", "The dataset contains a few unknown values:" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Stint 0\n", "Sector1Time 1\n", "Sector2Time 0\n", "Sector3Time 0\n", "SpeedI1 8\n", "SpeedI2 0\n", "Compound 0\n", "TyreLife 0\n", "FreshTyre 0\n", "TrackStatus 0\n", "dtype: int64" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_laps.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drop those rows to keep this initial tutorial simple:" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Stint 0\n", "Sector1Time 0\n", "Sector2Time 0\n", "Sector3Time 0\n", "SpeedI1 0\n", "SpeedI2 0\n", "Compound 0\n", "TyreLife 0\n", "FreshTyre 0\n", "TrackStatus 0\n", "dtype: int64" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_laps.dropna(inplace=True)\n", "all_laps.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some columns represent the time in `timedelta` format, let's convert them to seconds" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "Can only use .dt accessor with datetimelike values", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[108], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m test_laps_copy \u001b[38;5;241m=\u001b[39m test_laps\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m columns_in_timedelta:\n\u001b[0;32m---> 10\u001b[0m col_total_seconds \u001b[38;5;241m=\u001b[39m \u001b[43mall_laps_copy\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[38;5;241m.\u001b[39mtotal_seconds()\n\u001b[1;32m 11\u001b[0m all_laps_copy[col] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 12\u001b[0m all_laps_copy[col] \u001b[38;5;241m=\u001b[39m all_laps_copy[col]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mfloat\u001b[39m)\n", "File \u001b[0;32m~/miniforge3/envs/decision-forest/lib/python3.9/site-packages/pandas/core/generic.py:6299\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 6293\u001b[0m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m 6294\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m 6295\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m 6296\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 6299\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniforge3/envs/decision-forest/lib/python3.9/site-packages/pandas/core/accessor.py:224\u001b[0m, in \u001b[0;36mCachedAccessor.__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 222\u001b[0m \u001b[38;5;66;03m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[39;00m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessor\n\u001b[0;32m--> 224\u001b[0m accessor_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_accessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# Replace the property with the accessor object. Inspired by:\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# https://www.pydanny.com/cached-property.html\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;66;03m# We need to use object.__setattr__ because we overwrite __setattr__ on\u001b[39;00m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# NDFrame\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name, accessor_obj)\n", "File \u001b[0;32m~/miniforge3/envs/decision-forest/lib/python3.9/site-packages/pandas/core/indexes/accessors.py:643\u001b[0m, in \u001b[0;36mCombinedDatetimelikeProperties.__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data\u001b[38;5;241m.\u001b[39mdtype, PeriodDtype):\n\u001b[1;32m 641\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m PeriodProperties(data, orig)\n\u001b[0;32m--> 643\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan only use .dt accessor with datetimelike values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values" ] } ], "source": [ "\n", "columns_in_timedelta = [\n", " 'Sector1Time',\n", " 'Sector2Time',\n", " 'Sector3Time'\n", "]\n", "\n", "all_laps_copy = all_laps.copy()\n", "test_laps_copy = test_laps.copy()\n", "for col in columns_in_timedelta:\n", " col_total_seconds = all_laps_copy[col].dt.total_seconds()\n", " all_laps_copy[col] = None\n", " all_laps_copy[col] = all_laps_copy[col].astype(float)\n", " all_laps_copy.loc[:, col] = col_total_seconds\n", "\n", " col_total_seconds = test_laps_copy[col].dt.total_seconds()\n", " test_laps_copy[col] = None\n", " test_laps_copy[col] = test_laps_copy[col].astype(float)\n", " test_laps_copy.loc[:, col] = col_total_seconds\n", "\n", "\n", "\n", "all_laps = all_laps_copy\n", "test_laps = test_laps_copy\n", "all_laps.describe().transpose()\n", "all_laps" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([25.723, 25.601, 25.713, 25.636, 25.608, 25.62 , 25.679, 25.65 ,\n", " 25.741, 25.701])" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_laps['Sector3Time'].values" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# fig, axs = plt.subplots(ncols=5, figsize=(30,5))\n", "# sns.displot(data=all_laps, x=\"Sector1Time\", hue=\"Compound\", kde=True)\n", "# sns.displot(data=all_laps, x=\"Sector2Time\", hue=\"Compound\", kde=True)\n", "# sns.displot(data=all_laps, x=\"Sector3Time\", hue=\"Compound\", kde=True)\n", "# sns.pointplot(data=all_laps, x=\"TyreLife\", y=\"Sector3Time\", hue=\"Compound\")\n", "sns.violinplot(data=all_laps, x=\"Stint\", y=\"Sector3Time\", hue=\"Compound\")\n" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(all_laps, hue=\"Compound\")" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "83 40\n" ] } ], "source": [ "# Use the ~10% of the examples as the testing set\n", "# and the remaining ~90% of the examples as the training set.\n", "\n", "# np.random.seed(1)\n", "# is_test = np.random.rand(len(all_laps)) < 0.3\n", "\n", "# train_ds = all_laps[~is_test]\n", "# test_ds = all_laps[is_test]\n", "\n", "# print(len(train_ds), len(test_ds))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train model on 42 examples\n", "Model trained in 0:00:00.032711\n" ] } ], "source": [ "model = ydf.GradientBoostedTreesLearner(label=\"Sector3Time\",\n", " task=ydf.Task.REGRESSION).train(all_laps)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 0.331636\n", "num examples: 10\n", "num examples (weighted): 10\n", "\n" ] } ], "source": [ "evaluation = model.evaluate(test_laps)\n", "\n", "print(evaluation)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", "
\n", "
\n", "
RMSE:
\n", "
0.331636
\n", "
\n", " num examples:\n", "
\n", "
10
\n", "
\n", " num examples (weighted):\n", "
\n", "
10
\n", "
\n", "
\n", "
\n", "
\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n" ], "text/plain": [ "Evaluation()" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import plotly.io as pio\n", "# pio.renderers.default = \"notebook\"\n", "# pio.renderers.default = \"notebook_connected\"\n", "evaluation" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
Partial Dependence PlotConditional Expectation PlotVariable Importances
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "\n", "

Variable importances measure the importance of an input feature for a model.

    1. "Sector2Time"  0.002657 ################\n",
       "    2.     "SpeedI1"  0.000462 ###\n",
       "    3.     "SpeedI2" -0.000000 \n",
       "    4.       "Stint" -0.000000 \n",
       "    5.    "Compound" -0.000000 \n",
       "    6.    "TyreLife" -0.000000 \n",
       "    7.   "FreshTyre" -0.000000 \n",
       "    8. "TrackStatus" -0.000000 \n",
       "    9. "Sector1Time" -0.000139 \n",
       "
    1. "Sector2Time"  0.532258 ################\n",
       "    2.     "SpeedI2"  0.342222 #####\n",
       "    3.    "Compound"  0.322176 ####\n",
       "    4.    "TyreLife"  0.280340 #\n",
       "    5.     "SpeedI1"  0.268761 #\n",
       "    6.       "Stint"  0.260135 \n",
       "    7.   "FreshTyre"  0.260135 \n",
       "    8. "Sector1Time"  0.249865 \n",
       "
    1. "Sector2Time"  7.000000 ################\n",
       "    2.     "SpeedI2"  4.000000 \n",
       "
    1. "Sector2Time" 18.000000 ################\n",
       "    2.    "TyreLife" 16.000000 ##############\n",
       "    3.     "SpeedI1"  7.000000 #####\n",
       "    4.    "Compound"  6.000000 ####\n",
       "    5. "Sector1Time"  5.000000 ###\n",
       "    6.     "SpeedI2"  5.000000 ###\n",
       "    7.       "Stint"  2.000000 \n",
       "    8.   "FreshTyre"  2.000000 \n",
       "
    1. "Sector2Time" 681.742196 ################\n",
       "    2.     "SpeedI2" 197.385498 ####\n",
       "    3.    "Compound"  7.201797 \n",
       "    4.       "Stint"  4.074383 \n",
       "    5.   "FreshTyre"  2.347848 \n",
       "    6.    "TyreLife"  1.730018 \n",
       "    7. "Sector1Time"  1.391905 \n",
       "    8.     "SpeedI1"  1.181605 \n",
       "
" ], "text/plain": [ "" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.analyze(test_laps)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([25.723, 25.601, 25.713, 25.636, 25.608, 25.62 , 25.679, 25.65 ,\n", " 25.741, 25.701])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = test_laps['Sector3Time'].values\n", "y" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([25.993, 25.963, 26.002, 26.002, 26.017, 25.993, 25.97 , 25.993,\n", " 26.032, 25.993], dtype=float32)" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_hat = np.round(model.predict(test_laps), decimals=3)\n", "y_hat" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.27 , 0.362, 0.289, 0.366, 0.409, 0.373, 0.291, 0.343, 0.291,\n", " 0.292])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "error = np.around(y_hat - y, decimals=3)\n", "error" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.32859999999999995" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "error.mean()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pio.renderers.default = \"vscode\"\n", "# pio.renderers\n", "model.plot_tree()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
ModelDataspecTrainingVariable importancesStructure
Name : GRADIENT_BOOSTED_TREES
Task : REGRESSION
Label : Sector3TimeInSeconds
Features (10) : Driver Stint SpeedI1 SpeedI2 SpeedFL SpeedST Compound TyreLife Sector1TimeInSeconds Sector2TimeInSeconds
Weights : None
Trained with tuner : No
Model size : 235 kB
Number of records: 79\n",
       "Number of columns: 11\n",
       "\n",
       "Number of columns by type:\n",
       "\tNUMERICAL: 9 (81.8182%)\n",
       "\tCATEGORICAL: 2 (18.1818%)\n",
       "\n",
       "Columns:\n",
       "\n",
       "NUMERICAL: 9 (81.8182%)\n",
       "\t0: "Sector3TimeInSeconds" NUMERICAL mean:26.9429 min:25.65 max:41.716 sd:3.4253\n",
       "\t2: "Stint" NUMERICAL mean:1.62025 min:1 max:2 sd:0.485324\n",
       "\t3: "SpeedI1" NUMERICAL num-nas:13 (16.4557%) mean:202.212 min:83 max:216 sd:27.5563\n",
       "\t4: "SpeedI2" NUMERICAL mean:182.646 min:119 max:188 sd:10.0732\n",
       "\t5: "SpeedFL" NUMERICAL num-nas:2 (2.53165%) mean:270.091 min:93 max:281 sd:26.9782\n",
       "\t6: "SpeedST" NUMERICAL mean:302.19 min:107 max:327 sd:41.3672\n",
       "\t8: "TyreLife" NUMERICAL mean:15.7722 min:1 max:33 sd:8.40727\n",
       "\t9: "Sector1TimeInSeconds" NUMERICAL num-nas:1 (1.26582%) mean:32.7961 min:30.328 max:59.211 sd:5.92089\n",
       "\t10: "Sector2TimeInSeconds" NUMERICAL mean:36.5636 min:34.729 max:58.821 sd:4.73604\n",
       "\n",
       "CATEGORICAL: 2 (18.1818%)\n",
       "\t1: "Driver" CATEGORICAL has-dict vocab-size:3 zero-ood-items most-frequent:"VER" 40 (50.6329%)\n",
       "\t7: "Compound" CATEGORICAL has-dict vocab-size:3 zero-ood-items most-frequent:"HARD" 49 (62.0253%)\n",
       "\n",
       "Terminology:\n",
       "\tnas: Number of non-available (i.e. missing) values.\n",
       "\tood: Out of dictionary.\n",
       "\tmanually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.\n",
       "\ttokenized: The attribute value is obtained through tokenization.\n",
       "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n",
       "\tvocab-size: Number of unique values.\n",
       "

The following evaluation is computed on the validation or out-of-bag dataset.

Number of predictions (with weights): 1\n",
       "Task: REGRESSION\n",
       "Loss (SQUARED_ERROR): 3.21751\n",
       "\n",
       "RMSE: 1.79374\n",
       "Default RMSE: : 0\n",
       "
\n", "
\n", "\n", "
\n", "
\n", "\n", "

Variable importances measure the importance of an input feature for a model.

    1. "Sector2TimeInSeconds"  0.436139 ################\n",
       "    2.              "SpeedST"  0.327422 #######\n",
       "    3. "Sector1TimeInSeconds"  0.284140 ####\n",
       "    4.              "SpeedI2"  0.270955 ###\n",
       "    5.             "TyreLife"  0.256089 ##\n",
       "    6.              "SpeedI1"  0.253327 ##\n",
       "    7.              "SpeedFL"  0.240055 #\n",
       "    8.             "Compound"  0.219535 \n",
       "    9.               "Driver"  0.218823 \n",
       "
    1. "Sector2TimeInSeconds" 21.000000 ################\n",
       "    2.              "SpeedST" 13.000000 #########\n",
       "    3.             "TyreLife" 11.000000 ########\n",
       "    4.              "SpeedI1" 10.000000 #######\n",
       "    5.              "SpeedI2"  9.000000 ######\n",
       "    6. "Sector1TimeInSeconds"  1.000000 \n",
       "
    1. "Sector2TimeInSeconds" 136.000000 ################\n",
       "    2.              "SpeedST" 85.000000 #########\n",
       "    3. "Sector1TimeInSeconds" 58.000000 ######\n",
       "    4.             "TyreLife" 36.000000 ####\n",
       "    5.              "SpeedI1" 33.000000 ###\n",
       "    6.              "SpeedI2" 29.000000 ###\n",
       "    7.              "SpeedFL" 25.000000 ##\n",
       "    8.               "Driver"  7.000000 \n",
       "    9.             "Compound"  1.000000 \n",
       "
    1.              "SpeedI2" 1242.888427 ################\n",
       "    2.              "SpeedST" 1137.078549 ##############\n",
       "    3. "Sector2TimeInSeconds" 1070.926676 #############\n",
       "    4. "Sector1TimeInSeconds" 53.249495 \n",
       "    5.             "TyreLife" 29.913660 \n",
       "    6.              "SpeedI1" 26.164205 \n",
       "    7.              "SpeedFL" 19.199108 \n",
       "    8.               "Driver"  1.230373 \n",
       "    9.             "Compound"  0.443175 \n",
       "

Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.

Num trees : 65

Only printing the first tree.

Tree #0:\n",
       "    "SpeedI2">=175 [s:7.84383 n:73 np:68 miss:1] ; pred:6.00945e-08\n",
       "        ├─(pos)─ "Sector1TimeInSeconds">=31.832 [s:0.0939225 n:68 np:5 miss:1] ; pred:-0.0759441\n",
       "        |        ├─(pos)─ pred:0.0328411\n",
       "        |        └─(neg)─ "Sector1TimeInSeconds">=31.0385 [s:0.0217802 n:63 np:25 miss:1] ; pred:-0.0845779\n",
       "        |                 ├─(pos)─ "SpeedST">=312 [s:0.00747993 n:25 np:9 miss:0] ; pred:-0.0663828\n",
       "        |                 |        ├─(pos)─ pred:-0.0779144\n",
       "        |                 |        └─(neg)─ "TyreLife">=4.5 [s:0.00197849 n:16 np:11 miss:1] ; pred:-0.0598963\n",
       "        |                 |                 ├─(pos)─ pred:-0.0628952\n",
       "        |                 |                 └─(neg)─ pred:-0.0532989\n",
       "        |                 └─(neg)─ "SpeedI2">=185.5 [s:0.00164387 n:38 np:31 miss:0] ; pred:-0.0965483\n",
       "        |                          ├─(pos)─ "SpeedST">=316.5 [s:0.000383631 n:31 np:7 miss:0] ; pred:-0.098475\n",
       "        |                          |        ├─(pos)─ pred:-0.102102\n",
       "        |                          |        └─(neg)─ pred:-0.0974172\n",
       "        |                          └─(neg)─ pred:-0.088016\n",
       "        └─(neg)─ pred:1.03284\n",
       "
" ], "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.describe()" ] } ], "metadata": { "kernelspec": { "display_name": "decision-forest", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 2 }