testing.ipynb

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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import fastf1 as ff1\n",
    "import pandas as pd\n"
   ],
   "metadata": {
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    "ExecuteTime": {
     "end_time": "2023-09-04T09:51:18.137208041Z",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tLoading data for Italian Grand Prix - Qualifying [v3.0.7]\n",
      "req            INFO \tUsing cached data for driver_info\n",
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tNo cached data found for weather_data. Loading data...\n",
      "_api           INFO \tFetching weather data...\n",
      "req            INFO \tData has been written to cache!\n",
      "req            INFO \tNo cached data found for race_control_messages. Loading data...\n",
      "_api           INFO \tFetching race control messages...\n",
      "req            INFO \tData has been written to cache!\n",
      "core           INFO \tFinished loading data for 20 drivers: ['55', '1', '16', '63', '11', '23', '81', '44', '4', '14', '22', '40', '27', '77', '2', '24', '10', '31', '20', '18']\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "# cache \n",
    "ff1.Cache.enable_cache('cache')\n",
    "session = ff1.get_session(2023, 'Monza', 'Q')\n",
    "session.load(laps=True, telemetry=True, weather=True, messages=True)"
   ],
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   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "Timedelta('0 days 00:01:22.129400')"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1, q2, q3 = session.laps.split_qualifying_sessions()\n",
    "q1.groupby('Driver').agg('LapTime').min().mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-04T09:58:22.087589974Z",
     "start_time": "2023-09-04T09:58:22.078441549Z"
    }
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