f1analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.10.4\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: fastf1 in /home/codespace/.local/lib/python3.10/site-packages (2.2.8)\n",
      "Requirement already satisfied: matplotlib in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (3.6.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (1.23.4)\n",
      "Requirement already satisfied: requests-cache>=0.8.0 in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (0.9.7)\n",
      "Requirement already satisfied: thefuzz in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (0.19.0)\n",
      "Requirement already satisfied: scipy in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (1.9.3)\n",
      "Requirement already satisfied: pandas>=1.1.0 in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (1.5.1)\n",
      "Requirement already satisfied: signalr-client-aio in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (0.0.1.6.2)\n",
      "Requirement already satisfied: timple>=0.1.2 in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (0.1.5)\n",
      "Requirement already satisfied: requests in /home/codespace/.local/lib/python3.10/site-packages (from fastf1) (2.28.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/codespace/.local/lib/python3.10/site-packages (from pandas>=1.1.0->fastf1) (2022.6)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/codespace/.local/lib/python3.10/site-packages (from pandas>=1.1.0->fastf1) (2.8.2)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in /home/codespace/.local/lib/python3.10/site-packages (from requests-cache>=0.8.0->fastf1) (1.4.4)\n",
      "Requirement already satisfied: url-normalize>=1.4 in /home/codespace/.local/lib/python3.10/site-packages (from requests-cache>=0.8.0->fastf1) (1.4.3)\n",
      "Requirement already satisfied: cattrs>=22.2 in /home/codespace/.local/lib/python3.10/site-packages (from requests-cache>=0.8.0->fastf1) (22.2.0)\n",
      "Requirement already satisfied: urllib3>=1.25.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests-cache>=0.8.0->fastf1) (1.26.12)\n",
      "Requirement already satisfied: attrs>=21.2 in /home/codespace/.local/lib/python3.10/site-packages (from requests-cache>=0.8.0->fastf1) (22.1.0)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/codespace/.local/lib/python3.10/site-packages (from requests->fastf1) (2.1.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests->fastf1) (3.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.local/lib/python3.10/site-packages (from requests->fastf1) (2022.9.24)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (9.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (0.11.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (1.0.6)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (3.0.9)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib->fastf1) (4.38.0)\n",
      "Requirement already satisfied: websockets>=4.0.1 in /home/codespace/.local/lib/python3.10/site-packages (from signalr-client-aio->fastf1) (10.4)\n",
      "Requirement already satisfied: exceptiongroup in /home/codespace/.local/lib/python3.10/site-packages (from cattrs>=22.2->requests-cache>=0.8.0->fastf1) (1.0.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/codespace/.local/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas>=1.1.0->fastf1) (1.16.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install fastf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastf1 as ff1\n",
    "from fastf1 import plotting\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ff1.Cache.enable_cache(cache_dir='c:\\\\Users\\\\Rajup\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\cache') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "ff1.plotting.setup_mpl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=int(input('Enter Year'))\n",
    "gp=input('Enter the Grand Prix Location')\n",
    "sessiontypechoice=int(input('Enter 1 for qualifying or 2 for race'))\n",
    "if sessiontypechoice == 1:\n",
    "    sessiontype='Q'\n",
    "elif sessiontypechoice == 2:\n",
    "    sessiontype='R'\n",
    "else:\n",
    "    print(\"Unknown choice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "quali = ff1.get_session(year, gp,sessiontype )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Qualifying'"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quali.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-10-29 15:00:00')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quali.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tLoading data for Mexico City Grand Prix - Qualifying [v2.2.8]\n",
      "api            INFO \tFetching driver list...\n",
      "api            INFO \tFetching timing data...\n",
      "api            INFO \tParsing timing data...\n",
      "api            INFO \tFetching timing app data...\n",
      "core           INFO \tProcessing timing data...\n",
      "api            INFO \tFetching session status data...\n",
      "api            INFO \tFetching track status data...\n",
      "api            INFO \tFetching car data...\n",
      "api            INFO \tParsing car data...\n",
      "api            INFO \tFetching position data...\n",
      "api            INFO \tParsing position data...\n",
      "api            INFO \tFetching weather data...\n",
      "api            INFO \tFetching race control messages...\n",
      "core           INFO \tFinished loading data for 20 drivers: ['1', '63', '44', '11', '55', '77', '16', '4', '14', '31', '3', '24', '22', '10', '20', '47', '5', '18', '23', '6']\n"
     ]
    }
   ],
   "source": [
    "quali.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "quali_data=quali.laps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "quali_data=pd.DataFrame(quali_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(quali_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>DriverNumber</th>\n",
       "      <th>LapTime</th>\n",
       "      <th>LapNumber</th>\n",
       "      <th>Stint</th>\n",
       "      <th>PitOutTime</th>\n",
       "      <th>PitInTime</th>\n",
       "      <th>Sector1Time</th>\n",
       "      <th>Sector2Time</th>\n",
       "      <th>Sector3Time</th>\n",
       "      <th>...</th>\n",
       "      <th>IsPersonalBest</th>\n",
       "      <th>Compound</th>\n",
       "      <th>TyreLife</th>\n",
       "      <th>FreshTyre</th>\n",
       "      <th>LapStartTime</th>\n",
       "      <th>Team</th>\n",
       "      <th>Driver</th>\n",
       "      <th>TrackStatus</th>\n",
       "      <th>IsAccurate</th>\n",
       "      <th>LapStartDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:20:49.952000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:19:14.415000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:40.089000</td>\n",
       "      <td>0 days 00:00:24.639000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:19:14.415000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-10-29 20:04:16.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:22:44.419000</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:01:54.467000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:32.628000</td>\n",
       "      <td>0 days 00:00:47.431000</td>\n",
       "      <td>0 days 00:00:34.408000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:20:49.952000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:05:51.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 00:24:03.641000</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:01:19.222000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.015000</td>\n",
       "      <td>0 days 00:00:30.813000</td>\n",
       "      <td>0 days 00:00:20.394000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:22:44.419000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:07:46.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 00:25:44.611000</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:01:40.970000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:25:41.966000</td>\n",
       "      <td>0 days 00:00:39.688000</td>\n",
       "      <td>0 days 00:00:34.634000</td>\n",
       "      <td>0 days 00:00:26.648000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:24:03.641000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-10-29 20:09:05.492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 00:44:27.380000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:42:54.328000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:34.968000</td>\n",
       "      <td>0 days 00:00:26.902000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0 days 00:25:44.611000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>VER</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-10-29 20:10:46.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>0 days 00:25:31.597000</td>\n",
       "      <td>6</td>\n",
       "      <td>0 days 00:01:45.838000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:25:28.126000</td>\n",
       "      <td>0 days 00:00:42.131000</td>\n",
       "      <td>0 days 00:00:36.221000</td>\n",
       "      <td>0 days 00:00:27.486000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:23:45.759000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>LAT</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-10-29 20:08:47.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0 days 00:30:07.625000</td>\n",
       "      <td>6</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:28:34.274000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:37.759000</td>\n",
       "      <td>0 days 00:00:24.946000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:25:31.597000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>LAT</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-10-29 20:10:33.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>0 days 00:32:01.358000</td>\n",
       "      <td>6</td>\n",
       "      <td>0 days 00:01:53.733000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:32.214000</td>\n",
       "      <td>0 days 00:00:41.960000</td>\n",
       "      <td>0 days 00:00:39.559000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:30:07.625000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>LAT</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:15:09.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0 days 00:33:22.525000</td>\n",
       "      <td>6</td>\n",
       "      <td>0 days 00:01:21.167000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.219000</td>\n",
       "      <td>0 days 00:00:31.847000</td>\n",
       "      <td>0 days 00:00:21.101000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:32:01.358000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>LAT</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:17:03.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>0 days 00:35:23.041000</td>\n",
       "      <td>6</td>\n",
       "      <td>0 days 00:02:00.516000</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:35:20.252000</td>\n",
       "      <td>0 days 00:00:45.164000</td>\n",
       "      <td>0 days 00:00:45.474000</td>\n",
       "      <td>0 days 00:00:29.878000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:33:22.525000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>LAT</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-10-29 20:18:24.376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>298 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Time DriverNumber                LapTime  LapNumber  \\\n",
       "0   0 days 00:20:49.952000            1                    NaT          1   \n",
       "1   0 days 00:22:44.419000            1 0 days 00:01:54.467000          2   \n",
       "2   0 days 00:24:03.641000            1 0 days 00:01:19.222000          3   \n",
       "3   0 days 00:25:44.611000            1 0 days 00:01:40.970000          4   \n",
       "4   0 days 00:44:27.380000            1                    NaT          5   \n",
       "..                     ...          ...                    ...        ...   \n",
       "293 0 days 00:25:31.597000            6 0 days 00:01:45.838000          6   \n",
       "294 0 days 00:30:07.625000            6                    NaT          7   \n",
       "295 0 days 00:32:01.358000            6 0 days 00:01:53.733000          8   \n",
       "296 0 days 00:33:22.525000            6 0 days 00:01:21.167000          9   \n",
       "297 0 days 00:35:23.041000            6 0 days 00:02:00.516000         10   \n",
       "\n",
       "     Stint             PitOutTime              PitInTime  \\\n",
       "0        1 0 days 00:19:14.415000                    NaT   \n",
       "1        1                    NaT                    NaT   \n",
       "2        1                    NaT                    NaT   \n",
       "3        2                    NaT 0 days 00:25:41.966000   \n",
       "4        2 0 days 00:42:54.328000                    NaT   \n",
       "..     ...                    ...                    ...   \n",
       "293      2                    NaT 0 days 00:25:28.126000   \n",
       "294      2 0 days 00:28:34.274000                    NaT   \n",
       "295      2                    NaT                    NaT   \n",
       "296      2                    NaT                    NaT   \n",
       "297      2                    NaT 0 days 00:35:20.252000   \n",
       "\n",
       "               Sector1Time            Sector2Time            Sector3Time  ...  \\\n",
       "0                      NaT 0 days 00:00:40.089000 0 days 00:00:24.639000  ...   \n",
       "1   0 days 00:00:32.628000 0 days 00:00:47.431000 0 days 00:00:34.408000  ...   \n",
       "2   0 days 00:00:28.015000 0 days 00:00:30.813000 0 days 00:00:20.394000  ...   \n",
       "3   0 days 00:00:39.688000 0 days 00:00:34.634000 0 days 00:00:26.648000  ...   \n",
       "4                      NaT 0 days 00:00:34.968000 0 days 00:00:26.902000  ...   \n",
       "..                     ...                    ...                    ...  ...   \n",
       "293 0 days 00:00:42.131000 0 days 00:00:36.221000 0 days 00:00:27.486000  ...   \n",
       "294                    NaT 0 days 00:00:37.759000 0 days 00:00:24.946000  ...   \n",
       "295 0 days 00:00:32.214000 0 days 00:00:41.960000 0 days 00:00:39.559000  ...   \n",
       "296 0 days 00:00:28.219000 0 days 00:00:31.847000 0 days 00:00:21.101000  ...   \n",
       "297 0 days 00:00:45.164000 0 days 00:00:45.474000 0 days 00:00:29.878000  ...   \n",
       "\n",
       "    IsPersonalBest Compound TyreLife  FreshTyre           LapStartTime  \\\n",
       "0            False     SOFT      1.0       True 0 days 00:19:14.415000   \n",
       "1            False     SOFT      2.0       True 0 days 00:20:49.952000   \n",
       "2            False     SOFT      3.0       True 0 days 00:22:44.419000   \n",
       "3            False     SOFT      1.0       True 0 days 00:24:03.641000   \n",
       "4            False     SOFT      6.0      False 0 days 00:25:44.611000   \n",
       "..             ...      ...      ...        ...                    ...   \n",
       "293          False     SOFT      1.0       True 0 days 00:23:45.759000   \n",
       "294          False     SOFT      2.0       True 0 days 00:25:31.597000   \n",
       "295          False     SOFT      3.0       True 0 days 00:30:07.625000   \n",
       "296           True     SOFT      4.0       True 0 days 00:32:01.358000   \n",
       "297          False     SOFT      5.0       True 0 days 00:33:22.525000   \n",
       "\n",
       "                Team  Driver  TrackStatus IsAccurate            LapStartDate  \n",
       "0    Red Bull Racing     VER            1      False 2022-10-29 20:04:16.266  \n",
       "1    Red Bull Racing     VER            1       True 2022-10-29 20:05:51.803  \n",
       "2    Red Bull Racing     VER            1       True 2022-10-29 20:07:46.270  \n",
       "3    Red Bull Racing     VER            1      False 2022-10-29 20:09:05.492  \n",
       "4    Red Bull Racing     VER            2      False 2022-10-29 20:10:46.462  \n",
       "..               ...     ...          ...        ...                     ...  \n",
       "293         Williams     LAT            1      False 2022-10-29 20:08:47.610  \n",
       "294         Williams     LAT            1      False 2022-10-29 20:10:33.448  \n",
       "295         Williams     LAT            2       True 2022-10-29 20:15:09.476  \n",
       "296         Williams     LAT            1       True 2022-10-29 20:17:03.209  \n",
       "297         Williams     LAT            1      False 2022-10-29 20:18:24.376  \n",
       "\n",
       "[298 rows x 27 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quali_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "quali_results=quali.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "quali_results=quali_results[['DriverNumber','BroadcastName','Abbreviation','TeamName','Position','Q1','Q2','Q3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DriverNumber</th>\n",
       "      <th>BroadcastName</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TeamName</th>\n",
       "      <th>Position</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "      <td>VER</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0 days 00:01:19.222000</td>\n",
       "      <td>0 days 00:01:18.566000</td>\n",
       "      <td>0 days 00:01:17.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>G RUSSELL</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0 days 00:01:19.583000</td>\n",
       "      <td>0 days 00:01:18.565000</td>\n",
       "      <td>0 days 00:01:18.079000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>L HAMILTON</td>\n",
       "      <td>HAM</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0 days 00:01:19.169000</td>\n",
       "      <td>0 days 00:01:18.552000</td>\n",
       "      <td>0 days 00:01:18.084000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>S PEREZ</td>\n",
       "      <td>PER</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0 days 00:01:19.706000</td>\n",
       "      <td>0 days 00:01:18.615000</td>\n",
       "      <td>0 days 00:01:18.128000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>C SAINZ</td>\n",
       "      <td>SAI</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0 days 00:01:19.566000</td>\n",
       "      <td>0 days 00:01:18.560000</td>\n",
       "      <td>0 days 00:01:18.351000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>V BOTTAS</td>\n",
       "      <td>BOT</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0 days 00:01:19.523000</td>\n",
       "      <td>0 days 00:01:18.762000</td>\n",
       "      <td>0 days 00:01:18.401000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>C LECLERC</td>\n",
       "      <td>LEC</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0 days 00:01:19.505000</td>\n",
       "      <td>0 days 00:01:19.109000</td>\n",
       "      <td>0 days 00:01:18.555000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>L NORRIS</td>\n",
       "      <td>NOR</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0 days 00:01:19.857000</td>\n",
       "      <td>0 days 00:01:19.119000</td>\n",
       "      <td>0 days 00:01:18.721000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>F ALONSO</td>\n",
       "      <td>ALO</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0 days 00:01:20.006000</td>\n",
       "      <td>0 days 00:01:19.272000</td>\n",
       "      <td>0 days 00:01:18.939000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>E OCON</td>\n",
       "      <td>OCO</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0 days 00:01:19.945000</td>\n",
       "      <td>0 days 00:01:19.081000</td>\n",
       "      <td>0 days 00:01:19.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>D RICCIARDO</td>\n",
       "      <td>RIC</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0 days 00:01:20.279000</td>\n",
       "      <td>0 days 00:01:19.325000</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>G ZHOU</td>\n",
       "      <td>ZHO</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0 days 00:01:20.283000</td>\n",
       "      <td>0 days 00:01:19.476000</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Y TSUNODA</td>\n",
       "      <td>TSU</td>\n",
       "      <td>AlphaTauri</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0 days 00:01:19.907000</td>\n",
       "      <td>0 days 00:01:19.589000</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>P GASLY</td>\n",
       "      <td>GAS</td>\n",
       "      <td>AlphaTauri</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0 days 00:01:20.256000</td>\n",
       "      <td>0 days 00:01:19.672000</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>K MAGNUSSEN</td>\n",
       "      <td>MAG</td>\n",
       "      <td>Haas F1 Team</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0 days 00:01:20.293000</td>\n",
       "      <td>0 days 00:01:19.833000</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>M SCHUMACHER</td>\n",
       "      <td>MSC</td>\n",
       "      <td>Haas F1 Team</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0 days 00:01:20.419000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>S VETTEL</td>\n",
       "      <td>VET</td>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0 days 00:01:20.419000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>L STROLL</td>\n",
       "      <td>STR</td>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0 days 00:01:20.520000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>A ALBON</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Williams</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0 days 00:01:20.859000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>N LATIFI</td>\n",
       "      <td>LAT</td>\n",
       "      <td>Williams</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0 days 00:01:21.167000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DriverNumber BroadcastName Abbreviation         TeamName  Position  \\\n",
       "1             1  M VERSTAPPEN          VER  Red Bull Racing       1.0   \n",
       "63           63     G RUSSELL          RUS         Mercedes       2.0   \n",
       "44           44    L HAMILTON          HAM         Mercedes       3.0   \n",
       "11           11       S PEREZ          PER  Red Bull Racing       4.0   \n",
       "55           55       C SAINZ          SAI          Ferrari       5.0   \n",
       "77           77      V BOTTAS          BOT       Alfa Romeo       6.0   \n",
       "16           16     C LECLERC          LEC          Ferrari       7.0   \n",
       "4             4      L NORRIS          NOR          McLaren       8.0   \n",
       "14           14      F ALONSO          ALO           Alpine       9.0   \n",
       "31           31        E OCON          OCO           Alpine      10.0   \n",
       "3             3   D RICCIARDO          RIC          McLaren      11.0   \n",
       "24           24        G ZHOU          ZHO       Alfa Romeo      12.0   \n",
       "22           22     Y TSUNODA          TSU       AlphaTauri      13.0   \n",
       "10           10       P GASLY          GAS       AlphaTauri      14.0   \n",
       "20           20   K MAGNUSSEN          MAG     Haas F1 Team      15.0   \n",
       "47           47  M SCHUMACHER          MSC     Haas F1 Team      16.0   \n",
       "5             5      S VETTEL          VET     Aston Martin      17.0   \n",
       "18           18      L STROLL          STR     Aston Martin      18.0   \n",
       "23           23       A ALBON          ALB         Williams      19.0   \n",
       "6             6      N LATIFI          LAT         Williams      20.0   \n",
       "\n",
       "                       Q1                     Q2                     Q3  \n",
       "1  0 days 00:01:19.222000 0 days 00:01:18.566000 0 days 00:01:17.775000  \n",
       "63 0 days 00:01:19.583000 0 days 00:01:18.565000 0 days 00:01:18.079000  \n",
       "44 0 days 00:01:19.169000 0 days 00:01:18.552000 0 days 00:01:18.084000  \n",
       "11 0 days 00:01:19.706000 0 days 00:01:18.615000 0 days 00:01:18.128000  \n",
       "55 0 days 00:01:19.566000 0 days 00:01:18.560000 0 days 00:01:18.351000  \n",
       "77 0 days 00:01:19.523000 0 days 00:01:18.762000 0 days 00:01:18.401000  \n",
       "16 0 days 00:01:19.505000 0 days 00:01:19.109000 0 days 00:01:18.555000  \n",
       "4  0 days 00:01:19.857000 0 days 00:01:19.119000 0 days 00:01:18.721000  \n",
       "14 0 days 00:01:20.006000 0 days 00:01:19.272000 0 days 00:01:18.939000  \n",
       "31 0 days 00:01:19.945000 0 days 00:01:19.081000 0 days 00:01:19.010000  \n",
       "3  0 days 00:01:20.279000 0 days 00:01:19.325000                    NaT  \n",
       "24 0 days 00:01:20.283000 0 days 00:01:19.476000                    NaT  \n",
       "22 0 days 00:01:19.907000 0 days 00:01:19.589000                    NaT  \n",
       "10 0 days 00:01:20.256000 0 days 00:01:19.672000                    NaT  \n",
       "20 0 days 00:01:20.293000 0 days 00:01:19.833000                    NaT  \n",
       "47 0 days 00:01:20.419000                    NaT                    NaT  \n",
       "5  0 days 00:01:20.419000                    NaT                    NaT  \n",
       "18 0 days 00:01:20.520000                    NaT                    NaT  \n",
       "23 0 days 00:01:20.859000                    NaT                    NaT  \n",
       "6  0 days 00:01:21.167000                    NaT                    NaT  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quali_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#raceday=ff1.get_session(2021, 'Monaco', 'R')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#raceday.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_results=quali.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_results=race_results[['DriverNumber','BroadcastName','Abbreviation','TeamName','GridPosition','Position','Time','Status','Points']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DriverNumber</th>\n",
       "      <th>BroadcastName</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>TeamName</th>\n",
       "      <th>GridPosition</th>\n",
       "      <th>Position</th>\n",
       "      <th>Time</th>\n",
       "      <th>Status</th>\n",
       "      <th>Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M VERSTAPPEN</td>\n",
       "      <td>VER</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>G RUSSELL</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>L HAMILTON</td>\n",
       "      <td>HAM</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>S PEREZ</td>\n",
       "      <td>PER</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>C SAINZ</td>\n",
       "      <td>SAI</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>V BOTTAS</td>\n",
       "      <td>BOT</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>C LECLERC</td>\n",
       "      <td>LEC</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>L NORRIS</td>\n",
       "      <td>NOR</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>F ALONSO</td>\n",
       "      <td>ALO</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>E OCON</td>\n",
       "      <td>OCO</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>D RICCIARDO</td>\n",
       "      <td>RIC</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>G ZHOU</td>\n",
       "      <td>ZHO</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Y TSUNODA</td>\n",
       "      <td>TSU</td>\n",
       "      <td>AlphaTauri</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>P GASLY</td>\n",
       "      <td>GAS</td>\n",
       "      <td>AlphaTauri</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>K MAGNUSSEN</td>\n",
       "      <td>MAG</td>\n",
       "      <td>Haas F1 Team</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>M SCHUMACHER</td>\n",
       "      <td>MSC</td>\n",
       "      <td>Haas F1 Team</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>S VETTEL</td>\n",
       "      <td>VET</td>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>L STROLL</td>\n",
       "      <td>STR</td>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>A ALBON</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Williams</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>N LATIFI</td>\n",
       "      <td>LAT</td>\n",
       "      <td>Williams</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DriverNumber BroadcastName Abbreviation         TeamName  GridPosition  \\\n",
       "1             1  M VERSTAPPEN          VER  Red Bull Racing           0.0   \n",
       "63           63     G RUSSELL          RUS         Mercedes           0.0   \n",
       "44           44    L HAMILTON          HAM         Mercedes           0.0   \n",
       "11           11       S PEREZ          PER  Red Bull Racing           0.0   \n",
       "55           55       C SAINZ          SAI          Ferrari           0.0   \n",
       "77           77      V BOTTAS          BOT       Alfa Romeo           0.0   \n",
       "16           16     C LECLERC          LEC          Ferrari           0.0   \n",
       "4             4      L NORRIS          NOR          McLaren           0.0   \n",
       "14           14      F ALONSO          ALO           Alpine           0.0   \n",
       "31           31        E OCON          OCO           Alpine           0.0   \n",
       "3             3   D RICCIARDO          RIC          McLaren           0.0   \n",
       "24           24        G ZHOU          ZHO       Alfa Romeo           0.0   \n",
       "22           22     Y TSUNODA          TSU       AlphaTauri           0.0   \n",
       "10           10       P GASLY          GAS       AlphaTauri           0.0   \n",
       "20           20   K MAGNUSSEN          MAG     Haas F1 Team           0.0   \n",
       "47           47  M SCHUMACHER          MSC     Haas F1 Team           0.0   \n",
       "5             5      S VETTEL          VET     Aston Martin           0.0   \n",
       "18           18      L STROLL          STR     Aston Martin           0.0   \n",
       "23           23       A ALBON          ALB         Williams           0.0   \n",
       "6             6      N LATIFI          LAT         Williams           0.0   \n",
       "\n",
       "    Position Time Status  Points  \n",
       "1        1.0  NaT            0.0  \n",
       "63       2.0  NaT            0.0  \n",
       "44       3.0  NaT            0.0  \n",
       "11       4.0  NaT            0.0  \n",
       "55       5.0  NaT            0.0  \n",
       "77       6.0  NaT            0.0  \n",
       "16       7.0  NaT            0.0  \n",
       "4        8.0  NaT            0.0  \n",
       "14       9.0  NaT            0.0  \n",
       "31      10.0  NaT            0.0  \n",
       "3       11.0  NaT            0.0  \n",
       "24      12.0  NaT            0.0  \n",
       "22      13.0  NaT            0.0  \n",
       "10      14.0  NaT            0.0  \n",
       "20      15.0  NaT            0.0  \n",
       "47      16.0  NaT            0.0  \n",
       "5       17.0  NaT            0.0  \n",
       "18      18.0  NaT            0.0  \n",
       "23      19.0  NaT            0.0  \n",
       "6       20.0  NaT            0.0  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time                      0 days 01:14:31.531000\n",
       "DriverNumber                                   1\n",
       "LapTime                   0 days 00:01:17.775000\n",
       "LapNumber                                     15\n",
       "Stint                                          5\n",
       "PitOutTime                                   NaT\n",
       "PitInTime                                    NaT\n",
       "Sector1Time               0 days 00:00:27.490000\n",
       "Sector2Time               0 days 00:00:30.244000\n",
       "Sector3Time               0 days 00:00:20.041000\n",
       "Sector1SessionTime        0 days 01:13:41.246000\n",
       "Sector2SessionTime        0 days 01:14:11.490000\n",
       "Sector3SessionTime        0 days 01:14:31.531000\n",
       "SpeedI1                                    314.0\n",
       "SpeedI2                                    295.0\n",
       "SpeedFL                                    252.0\n",
       "SpeedST                                    349.0\n",
       "IsPersonalBest                              True\n",
       "Compound                                    SOFT\n",
       "TyreLife                                     3.0\n",
       "FreshTyre                                   True\n",
       "LapStartTime              0 days 01:13:13.756000\n",
       "Team                             Red Bull Racing\n",
       "Driver                                       VER\n",
       "TrackStatus                                    1\n",
       "IsAccurate                                  True\n",
       "LapStartDate          2022-10-29 20:58:15.607000\n",
       "Name: 14, dtype: object"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quali.laps.pick_fastest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So lets check the visualizatiion of quali pace difference between drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver=pd.unique(quali_data['Driver'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(driver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_laps_driver=list()\n",
    "for drivers in driver:\n",
    "    fastest_lap=quali.laps.pick_driver(drivers).pick_fastest()\n",
    "    fastest_laps_driver.append(fastest_lap)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(fastest_laps_driver))\n",
    "path=\"D:/Python Projects/f1analysis/Visualisations/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastf1.core import Laps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_laps = Laps(fastest_laps_driver).sort_values(by='LapTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_laps=fastest_laps.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_laps.drop('index', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "def  getdelta(index):      \n",
    "    times=fastest_lapdf['LapTime'].iloc[index]\n",
    "    times=times.total_seconds()\n",
    "    delta_cars=times-fastest_time_temp\n",
    "    return delta_cars\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_lapdf=pd.DataFrame(fastest_laps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3089999999999975"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing out for the function to get the delta between two drivers during qualifiers\n",
    "fastest_time=fastest_lapdf['LapTime'].iloc[0]\n",
    "fastest_time\n",
    "fastest_time_temp=fastest_time.total_seconds()\n",
    "fastest_time_temp\n",
    "secondfastest_time=fastest_lapdf['LapTime'].iloc[2]\n",
    "secondfastest_time\n",
    "secondfastest_time_temp=secondfastest_time.total_seconds()\n",
    "secondfastest_time_temp\n",
    "delta=secondfastest_time_temp-fastest_time_temp\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.945999999999998"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function call to get delta (Args to be passed is the row index)\n",
    "getdelta(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>DriverNumber</th>\n",
       "      <th>LapTime</th>\n",
       "      <th>LapNumber</th>\n",
       "      <th>Stint</th>\n",
       "      <th>PitOutTime</th>\n",
       "      <th>PitInTime</th>\n",
       "      <th>Sector1Time</th>\n",
       "      <th>Sector2Time</th>\n",
       "      <th>Sector3Time</th>\n",
       "      <th>...</th>\n",
       "      <th>IsPersonalBest</th>\n",
       "      <th>Compound</th>\n",
       "      <th>TyreLife</th>\n",
       "      <th>FreshTyre</th>\n",
       "      <th>LapStartTime</th>\n",
       "      <th>Team</th>\n",
       "      <th>Driver</th>\n",
       "      <th>TrackStatus</th>\n",
       "      <th>IsAccurate</th>\n",
       "      <th>LapStartDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 01:14:31.531000</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:01:17.775000</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.490000</td>\n",
       "      <td>0 days 00:00:30.244000</td>\n",
       "      <td>0 days 00:00:20.041000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:13:13.756000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:58:15.607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 01:07:17.150000</td>\n",
       "      <td>63</td>\n",
       "      <td>0 days 00:01:18.079000</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.718000</td>\n",
       "      <td>0 days 00:00:30.138000</td>\n",
       "      <td>0 days 00:00:20.223000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:05:59.071000</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>RUS</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:51:00.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 01:14:42.322000</td>\n",
       "      <td>44</td>\n",
       "      <td>0 days 00:01:18.084000</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.715000</td>\n",
       "      <td>0 days 00:00:30.069000</td>\n",
       "      <td>0 days 00:00:20.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:13:24.238000</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>HAM</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:58:26.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 01:13:44.748000</td>\n",
       "      <td>11</td>\n",
       "      <td>0 days 00:01:18.128000</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.585000</td>\n",
       "      <td>0 days 00:00:30.283000</td>\n",
       "      <td>0 days 00:00:20.260000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:12:26.620000</td>\n",
       "      <td>Red Bull Racing</td>\n",
       "      <td>PER</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:57:28.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 01:06:28.122000</td>\n",
       "      <td>55</td>\n",
       "      <td>0 days 00:01:18.351000</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.662000</td>\n",
       "      <td>0 days 00:00:30.479000</td>\n",
       "      <td>0 days 00:00:20.210000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:05:09.771000</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>SAI</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:50:11.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0 days 01:14:36.347000</td>\n",
       "      <td>77</td>\n",
       "      <td>0 days 00:01:18.401000</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.667000</td>\n",
       "      <td>0 days 00:00:30.338000</td>\n",
       "      <td>0 days 00:00:20.396000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:13:17.946000</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>BOT</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:58:19.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0 days 01:14:08.098000</td>\n",
       "      <td>16</td>\n",
       "      <td>0 days 00:01:18.555000</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.943000</td>\n",
       "      <td>0 days 00:00:30.330000</td>\n",
       "      <td>0 days 00:00:20.282000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:12:49.543000</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>LEC</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:57:51.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0 days 01:14:13.421000</td>\n",
       "      <td>4</td>\n",
       "      <td>0 days 00:01:18.721000</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.767000</td>\n",
       "      <td>0 days 00:00:30.544000</td>\n",
       "      <td>0 days 00:00:20.410000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:12:54.700000</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>NOR</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:57:56.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0 days 01:09:19.341000</td>\n",
       "      <td>14</td>\n",
       "      <td>0 days 00:01:18.939000</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.049000</td>\n",
       "      <td>0 days 00:00:30.393000</td>\n",
       "      <td>0 days 00:00:20.497000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:08:00.402000</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>ALO</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:53:02.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0 days 01:14:19.790000</td>\n",
       "      <td>31</td>\n",
       "      <td>0 days 00:01:19.010000</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.966000</td>\n",
       "      <td>0 days 00:00:30.574000</td>\n",
       "      <td>0 days 00:00:20.470000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 01:13:00.780000</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>OCO</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:58:02.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0 days 00:45:58.523000</td>\n",
       "      <td>3</td>\n",
       "      <td>0 days 00:01:19.325000</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.867000</td>\n",
       "      <td>0 days 00:00:30.954000</td>\n",
       "      <td>0 days 00:00:20.504000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:44:39.198000</td>\n",
       "      <td>McLaren</td>\n",
       "      <td>RIC</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:29:41.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0 days 00:54:57.879000</td>\n",
       "      <td>24</td>\n",
       "      <td>0 days 00:01:19.476000</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.119000</td>\n",
       "      <td>0 days 00:00:30.864000</td>\n",
       "      <td>0 days 00:00:20.493000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:53:38.403000</td>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>ZHO</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:38:40.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0 days 00:55:18.687000</td>\n",
       "      <td>22</td>\n",
       "      <td>0 days 00:01:19.589000</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.960000</td>\n",
       "      <td>0 days 00:00:30.860000</td>\n",
       "      <td>0 days 00:00:20.769000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:53:59.098000</td>\n",
       "      <td>AlphaTauri</td>\n",
       "      <td>TSU</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:39:00.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0 days 00:55:08.392000</td>\n",
       "      <td>10</td>\n",
       "      <td>0 days 00:01:19.672000</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.929000</td>\n",
       "      <td>0 days 00:00:30.971000</td>\n",
       "      <td>0 days 00:00:20.772000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:53:48.720000</td>\n",
       "      <td>AlphaTauri</td>\n",
       "      <td>GAS</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:38:50.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0 days 00:54:53.407000</td>\n",
       "      <td>20</td>\n",
       "      <td>0 days 00:01:19.833000</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.004000</td>\n",
       "      <td>0 days 00:00:31.144000</td>\n",
       "      <td>0 days 00:00:20.685000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:53:33.574000</td>\n",
       "      <td>Haas F1 Team</td>\n",
       "      <td>MAG</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:38:35.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0 days 00:33:05.271000</td>\n",
       "      <td>47</td>\n",
       "      <td>0 days 00:01:20.419000</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.038000</td>\n",
       "      <td>0 days 00:00:31.524000</td>\n",
       "      <td>0 days 00:00:20.857000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:31:44.852000</td>\n",
       "      <td>Haas F1 Team</td>\n",
       "      <td>MSC</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:16:46.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0 days 00:33:30.896000</td>\n",
       "      <td>5</td>\n",
       "      <td>0 days 00:01:20.419000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.367000</td>\n",
       "      <td>0 days 00:00:31.224000</td>\n",
       "      <td>0 days 00:00:20.828000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:32:10.477000</td>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>VET</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:17:12.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0 days 00:33:38.516000</td>\n",
       "      <td>18</td>\n",
       "      <td>0 days 00:01:20.520000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.298000</td>\n",
       "      <td>0 days 00:00:31.189000</td>\n",
       "      <td>0 days 00:00:21.033000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:32:17.996000</td>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>STR</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:17:19.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0 days 00:20:39.090000</td>\n",
       "      <td>23</td>\n",
       "      <td>0 days 00:01:20.859000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:27.963000</td>\n",
       "      <td>0 days 00:00:31.907000</td>\n",
       "      <td>0 days 00:00:20.989000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:19:18.231000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:04:20.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0 days 00:33:22.525000</td>\n",
       "      <td>6</td>\n",
       "      <td>0 days 00:01:21.167000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0 days 00:00:28.219000</td>\n",
       "      <td>0 days 00:00:31.847000</td>\n",
       "      <td>0 days 00:00:21.101000</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>SOFT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0 days 00:32:01.358000</td>\n",
       "      <td>Williams</td>\n",
       "      <td>LAT</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2022-10-29 20:17:03.209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Time DriverNumber                LapTime  LapNumber  \\\n",
       "0  0 days 01:14:31.531000            1 0 days 00:01:17.775000         15   \n",
       "1  0 days 01:07:17.150000           63 0 days 00:01:18.079000         14   \n",
       "2  0 days 01:14:42.322000           44 0 days 00:01:18.084000         18   \n",
       "3  0 days 01:13:44.748000           11 0 days 00:01:18.128000         17   \n",
       "4  0 days 01:06:28.122000           55 0 days 00:01:18.351000         13   \n",
       "5  0 days 01:14:36.347000           77 0 days 00:01:18.401000         17   \n",
       "6  0 days 01:14:08.098000           16 0 days 00:01:18.555000         17   \n",
       "7  0 days 01:14:13.421000            4 0 days 00:01:18.721000         18   \n",
       "8  0 days 01:09:19.341000           14 0 days 00:01:18.939000         14   \n",
       "9  0 days 01:14:19.790000           31 0 days 00:01:19.010000         17   \n",
       "10 0 days 00:45:58.523000            3 0 days 00:01:19.325000         10   \n",
       "11 0 days 00:54:57.879000           24 0 days 00:01:19.476000         11   \n",
       "12 0 days 00:55:18.687000           22 0 days 00:01:19.589000         12   \n",
       "13 0 days 00:55:08.392000           10 0 days 00:01:19.672000         14   \n",
       "14 0 days 00:54:53.407000           20 0 days 00:01:19.833000         16   \n",
       "15 0 days 00:33:05.271000           47 0 days 00:01:20.419000         10   \n",
       "16 0 days 00:33:30.896000            5 0 days 00:01:20.419000          9   \n",
       "17 0 days 00:33:38.516000           18 0 days 00:01:20.520000          9   \n",
       "18 0 days 00:20:39.090000           23 0 days 00:01:20.859000          3   \n",
       "19 0 days 00:33:22.525000            6 0 days 00:01:21.167000          9   \n",
       "\n",
       "    Stint PitOutTime PitInTime            Sector1Time            Sector2Time  \\\n",
       "0       5        NaT       NaT 0 days 00:00:27.490000 0 days 00:00:30.244000   \n",
       "1       5        NaT       NaT 0 days 00:00:27.718000 0 days 00:00:30.138000   \n",
       "2       6        NaT       NaT 0 days 00:00:27.715000 0 days 00:00:30.069000   \n",
       "3       6        NaT       NaT 0 days 00:00:27.585000 0 days 00:00:30.283000   \n",
       "4       4        NaT       NaT 0 days 00:00:27.662000 0 days 00:00:30.479000   \n",
       "5       6        NaT       NaT 0 days 00:00:27.667000 0 days 00:00:30.338000   \n",
       "6       5        NaT       NaT 0 days 00:00:27.943000 0 days 00:00:30.330000   \n",
       "7       6        NaT       NaT 0 days 00:00:27.767000 0 days 00:00:30.544000   \n",
       "8       5        NaT       NaT 0 days 00:00:28.049000 0 days 00:00:30.393000   \n",
       "9       6        NaT       NaT 0 days 00:00:27.966000 0 days 00:00:30.574000   \n",
       "10      3        NaT       NaT 0 days 00:00:27.867000 0 days 00:00:30.954000   \n",
       "11      4        NaT       NaT 0 days 00:00:28.119000 0 days 00:00:30.864000   \n",
       "12      4        NaT       NaT 0 days 00:00:27.960000 0 days 00:00:30.860000   \n",
       "13      4        NaT       NaT 0 days 00:00:27.929000 0 days 00:00:30.971000   \n",
       "14      4        NaT       NaT 0 days 00:00:28.004000 0 days 00:00:31.144000   \n",
       "15      2        NaT       NaT 0 days 00:00:28.038000 0 days 00:00:31.524000   \n",
       "16      2        NaT       NaT 0 days 00:00:28.367000 0 days 00:00:31.224000   \n",
       "17      2        NaT       NaT 0 days 00:00:28.298000 0 days 00:00:31.189000   \n",
       "18      1        NaT       NaT 0 days 00:00:27.963000 0 days 00:00:31.907000   \n",
       "19      2        NaT       NaT 0 days 00:00:28.219000 0 days 00:00:31.847000   \n",
       "\n",
       "              Sector3Time  ... IsPersonalBest Compound TyreLife  FreshTyre  \\\n",
       "0  0 days 00:00:20.041000  ...           True     SOFT      3.0       True   \n",
       "1  0 days 00:00:20.223000  ...           True     SOFT      3.0       True   \n",
       "2  0 days 00:00:20.300000  ...           True     SOFT      3.0       True   \n",
       "3  0 days 00:00:20.260000  ...           True     SOFT      3.0       True   \n",
       "4  0 days 00:00:20.210000  ...           True     SOFT      3.0       True   \n",
       "5  0 days 00:00:20.396000  ...           True     SOFT      3.0       True   \n",
       "6  0 days 00:00:20.282000  ...           True     SOFT      3.0       True   \n",
       "7  0 days 00:00:20.410000  ...           True     SOFT      3.0       True   \n",
       "8  0 days 00:00:20.497000  ...           True     SOFT      3.0       True   \n",
       "9  0 days 00:00:20.470000  ...           True     SOFT      3.0       True   \n",
       "10 0 days 00:00:20.504000  ...           True     SOFT      3.0       True   \n",
       "11 0 days 00:00:20.493000  ...           True     SOFT      3.0       True   \n",
       "12 0 days 00:00:20.769000  ...           True     SOFT      3.0       True   \n",
       "13 0 days 00:00:20.772000  ...           True     SOFT      3.0       True   \n",
       "14 0 days 00:00:20.685000  ...           True     SOFT      3.0       True   \n",
       "15 0 days 00:00:20.857000  ...           True     SOFT      5.0       True   \n",
       "16 0 days 00:00:20.828000  ...           True     SOFT      NaN       True   \n",
       "17 0 days 00:00:21.033000  ...           True     SOFT      4.0       True   \n",
       "18 0 days 00:00:20.989000  ...           True     SOFT      3.0       True   \n",
       "19 0 days 00:00:21.101000  ...           True     SOFT      4.0       True   \n",
       "\n",
       "             LapStartTime             Team  Driver  TrackStatus IsAccurate  \\\n",
       "0  0 days 01:13:13.756000  Red Bull Racing     VER            1       True   \n",
       "1  0 days 01:05:59.071000         Mercedes     RUS            1       True   \n",
       "2  0 days 01:13:24.238000         Mercedes     HAM            1       True   \n",
       "3  0 days 01:12:26.620000  Red Bull Racing     PER            1       True   \n",
       "4  0 days 01:05:09.771000          Ferrari     SAI            1       True   \n",
       "5  0 days 01:13:17.946000       Alfa Romeo     BOT            1       True   \n",
       "6  0 days 01:12:49.543000          Ferrari     LEC            1       True   \n",
       "7  0 days 01:12:54.700000          McLaren     NOR            1       True   \n",
       "8  0 days 01:08:00.402000           Alpine     ALO            1       True   \n",
       "9  0 days 01:13:00.780000           Alpine     OCO            1       True   \n",
       "10 0 days 00:44:39.198000          McLaren     RIC            1       True   \n",
       "11 0 days 00:53:38.403000       Alfa Romeo     ZHO            1       True   \n",
       "12 0 days 00:53:59.098000       AlphaTauri     TSU            1       True   \n",
       "13 0 days 00:53:48.720000       AlphaTauri     GAS            1       True   \n",
       "14 0 days 00:53:33.574000     Haas F1 Team     MAG            1       True   \n",
       "15 0 days 00:31:44.852000     Haas F1 Team     MSC            1       True   \n",
       "16 0 days 00:32:10.477000     Aston Martin     VET            1       True   \n",
       "17 0 days 00:32:17.996000     Aston Martin     STR            1       True   \n",
       "18 0 days 00:19:18.231000         Williams     ALB            1       True   \n",
       "19 0 days 00:32:01.358000         Williams     LAT            1       True   \n",
       "\n",
       "              LapStartDate  \n",
       "0  2022-10-29 20:58:15.607  \n",
       "1  2022-10-29 20:51:00.922  \n",
       "2  2022-10-29 20:58:26.089  \n",
       "3  2022-10-29 20:57:28.471  \n",
       "4  2022-10-29 20:50:11.622  \n",
       "5  2022-10-29 20:58:19.797  \n",
       "6  2022-10-29 20:57:51.394  \n",
       "7  2022-10-29 20:57:56.551  \n",
       "8  2022-10-29 20:53:02.253  \n",
       "9  2022-10-29 20:58:02.631  \n",
       "10 2022-10-29 20:29:41.049  \n",
       "11 2022-10-29 20:38:40.254  \n",
       "12 2022-10-29 20:39:00.949  \n",
       "13 2022-10-29 20:38:50.571  \n",
       "14 2022-10-29 20:38:35.425  \n",
       "15 2022-10-29 20:16:46.703  \n",
       "16 2022-10-29 20:17:12.328  \n",
       "17 2022-10-29 20:17:19.847  \n",
       "18 2022-10-29 20:04:20.082  \n",
       "19 2022-10-29 20:17:03.209  \n",
       "\n",
       "[20 rows x 27 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastest_lapdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranges=len(fastest_lapdf)\n",
    "ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting all the deltas wrt to pole position\n",
    "deltas=[]\n",
    "for i in range(1,ranges):\n",
    "    time_diff=getdelta(i)\n",
    "    deltas.append(time_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(deltas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.30399999999998784,\n",
       " 0.3089999999999975,\n",
       " 0.35299999999999443,\n",
       " 0.5759999999999934,\n",
       " 0.6259999999999906,\n",
       " 0.7800000000000011,\n",
       " 0.945999999999998,\n",
       " 1.1639999999999873,\n",
       " 1.2349999999999994,\n",
       " 1.5499999999999972,\n",
       " 1.7009999999999934,\n",
       " 1.813999999999993,\n",
       " 1.8969999999999914,\n",
       " 2.0579999999999927,\n",
       " 2.6439999999999912,\n",
       " 2.6439999999999912,\n",
       " 2.7449999999999903,\n",
       " 3.083999999999989,\n",
       " 3.391999999999996]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "pole=fastest_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "fastest_laps['LapTimeDelta'] = fastest_laps['LapTime'] - pole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Driver                LapTime           LapTimeDelta\n",
      "0     VER 0 days 00:01:17.775000        0 days 00:00:00\n",
      "1     RUS 0 days 00:01:18.079000 0 days 00:00:00.304000\n",
      "2     HAM 0 days 00:01:18.084000 0 days 00:00:00.309000\n",
      "3     PER 0 days 00:01:18.128000 0 days 00:00:00.353000\n",
      "4     SAI 0 days 00:01:18.351000 0 days 00:00:00.576000\n",
      "5     BOT 0 days 00:01:18.401000 0 days 00:00:00.626000\n",
      "6     LEC 0 days 00:01:18.555000 0 days 00:00:00.780000\n",
      "7     NOR 0 days 00:01:18.721000 0 days 00:00:00.946000\n",
      "8     ALO 0 days 00:01:18.939000 0 days 00:00:01.164000\n",
      "9     OCO 0 days 00:01:19.010000 0 days 00:00:01.235000\n",
      "10    RIC 0 days 00:01:19.325000 0 days 00:00:01.550000\n",
      "11    ZHO 0 days 00:01:19.476000 0 days 00:00:01.701000\n",
      "12    TSU 0 days 00:01:19.589000 0 days 00:00:01.814000\n",
      "13    GAS 0 days 00:01:19.672000 0 days 00:00:01.897000\n",
      "14    MAG 0 days 00:01:19.833000 0 days 00:00:02.058000\n",
      "15    MSC 0 days 00:01:20.419000 0 days 00:00:02.644000\n",
      "16    VET 0 days 00:01:20.419000 0 days 00:00:02.644000\n",
      "17    STR 0 days 00:01:20.520000 0 days 00:00:02.745000\n",
      "18    ALB 0 days 00:01:20.859000 0 days 00:00:03.084000\n",
      "19    LAT 0 days 00:01:21.167000 0 days 00:00:03.392000\n"
     ]
    }
   ],
   "source": [
    "print(fastest_laps[['Driver', 'LapTime', 'LapTimeDelta']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_colors = list()\n",
    "for index, lap in fastest_laps.iterlaps():\n",
    "    color = ff1.plotting.team_color(lap['Team'])\n",
    "    team_colors.append(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "from timple.timedelta import strftimedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.barh(fastest_laps.index, fastest_laps['LapTimeDelta'],\n",
    "        color=team_colors, edgecolor='grey')\n",
    "ax.set_yticks(fastest_laps.index)\n",
    "ax.set_yticklabels(fastest_laps['Driver'])\n",
    "\n",
    "# show fastest at the top\n",
    "ax.invert_yaxis()\n",
    "\n",
    "# draw vertical lines behind the bars\n",
    "ax.set_axisbelow(True)\n",
    "ax.xaxis.grid(True, which='major', linestyle='--', color='black', zorder=-1000)\n",
    "lap_time_string = strftimedelta(pole, '%m:%s.%ms')\n",
    "\n",
    "plt.suptitle(f\"{quali.event['EventName']} {quali.event.year} Qualifying\\n\"\n",
    "             f\"Fastest Lap: {lap_time_string} ({pole})\")\n",
    "#fig.savefig(path+\"quali.jpg\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "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": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}