{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Save fastf1 data a to SQL database\n","\n","Useful for generating quick reports during the race.\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["req WARNING \tDEFAULT CACHE ENABLED! (1.04 GB) /Users/diegomaradona/Library/Caches/fastf1\n","core INFO \tLoading data for Spanish Grand Prix - Practice 1 [v3.3.3]\n","req INFO \tUsing cached data for session_info\n","req INFO \tUsing cached data for driver_info\n","req INFO \tUsing cached data for session_status_data\n","req INFO \tUsing cached data for track_status_data\n","req INFO \tUsing cached data for _extended_timing_data\n","req INFO \tUsing cached data for timing_app_data\n","core INFO \tProcessing timing data...\n","core WARNING \tNo lap data for driver 27\n","core WARNING \tFailed to perform lap accuracy check - all laps marked as inaccurate (driver 27)\n","req INFO \tUsing cached data for car_data\n","req INFO \tUsing cached data for position_data\n","req INFO \tUsing cached data for weather_data\n","core INFO \tFinished loading data for 21 drivers: ['1', '10', '11', '14', '16', '18', '2', '20', '22', '23', '24', '27', '3', '31', '4', '44', '50', '55', '63', '77', '81']\n"]}],"source":["import fastf1\n","from fastf1.core import Session\n","\n","GRAND_PRIX = 'Spain'\n","YEAR = 2024\n","SESSION = 'FP1'\n","\n","session = fastf1.get_session(YEAR, GRAND_PRIX, SESSION)\n","session.load(messages=False)"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/html":["
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TimeAirTempHumidityPressureRainfallTrackTempWindDirectionWindSpeed
00 days 00:00:25.64200027.537.01004.3False46.3930.0
10 days 00:01:25.66200027.536.01004.5False46.400.0
20 days 00:02:25.65300027.536.01004.5False47.32412.3
30 days 00:03:25.66000027.634.01004.4False47.52262.0
40 days 00:04:25.66700027.735.01004.4False46.42422.0
...........................
760 days 01:16:25.84600028.337.01004.1False48.01070.7
770 days 01:17:25.83500028.436.01004.2False47.82761.0
780 days 01:18:25.83200028.536.01004.2False46.12302.1
790 days 01:19:25.83900028.537.01004.2False46.22591.7
800 days 01:20:25.84800028.538.01004.2False47.02001.5
\n","

81 rows × 8 columns

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"],"text/plain":[" Time AirTemp Humidity Pressure Rainfall TrackTemp \\\n","0 0 days 00:00:25.642000 27.5 37.0 1004.3 False 46.3 \n","1 0 days 00:01:25.662000 27.5 36.0 1004.5 False 46.4 \n","2 0 days 00:02:25.653000 27.5 36.0 1004.5 False 47.3 \n","3 0 days 00:03:25.660000 27.6 34.0 1004.4 False 47.5 \n","4 0 days 00:04:25.667000 27.7 35.0 1004.4 False 46.4 \n",".. ... ... ... ... ... ... \n","76 0 days 01:16:25.846000 28.3 37.0 1004.1 False 48.0 \n","77 0 days 01:17:25.835000 28.4 36.0 1004.2 False 47.8 \n","78 0 days 01:18:25.832000 28.5 36.0 1004.2 False 46.1 \n","79 0 days 01:19:25.839000 28.5 37.0 1004.2 False 46.2 \n","80 0 days 01:20:25.848000 28.5 38.0 1004.2 False 47.0 \n","\n"," WindDirection WindSpeed \n","0 93 0.0 \n","1 0 0.0 \n","2 241 2.3 \n","3 226 2.0 \n","4 242 2.0 \n",".. ... ... \n","76 107 0.7 \n","77 276 1.0 \n","78 230 2.1 \n","79 259 1.7 \n","80 200 1.5 \n","\n","[81 rows x 8 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["session.weather_data"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["RoundNumber 10\n","Country Spain\n","Location Barcelona\n","OfficialEventName FORMULA 1 ARAMCO GRAN PREMIO DE ESPAÑA 2024\n","EventDate 2024-06-23 00:00:00\n","EventName Spanish Grand Prix\n","EventFormat conventional\n","Session1 Practice 1\n","Session1Date 2024-06-21 13:30:00+02:00\n","Session1DateUtc 2024-06-21 11:30:00\n","Session2 Practice 2\n","Session2Date 2024-06-21 17:00:00+02:00\n","Session2DateUtc 2024-06-21 15:00:00\n","Session3 Practice 3\n","Session3Date 2024-06-22 12:30:00+02:00\n","Session3DateUtc 2024-06-22 10:30:00\n","Session4 Qualifying\n","Session4Date 2024-06-22 16:00:00+02:00\n","Session4DateUtc 2024-06-22 14:00:00\n","Session5 Race\n","Session5Date 2024-06-23 15:00:00+02:00\n","Session5DateUtc 2024-06-23 13:00:00\n","F1ApiSupport True\n","Name: 10, dtype: object"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["session.event"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[],"source":["from pandas import DataFrame\n","from sqlalchemy import create_engine\n","from typing import cast\n","\n","\n","class Formula1Databases:\n"," \"\"\"\n"," Quickly fetch, extract features and save `fastf1` data to a SQL database\n"," \"\"\"\n","\n"," def __init__(self, session: Session) -> None:\n"," self.laps = session.laps.copy()\n"," self.weather_data = cast(DataFrame, session.weather_data).copy()\n"," self.telemetry = session.car_data.copy()\n"," self.event = session.event.copy()\n"," self._session_start_date = session.session_info['StartDate']\n","\n"," self.sql_engine = None\n"," self.df_laps: DataFrame | None = None\n"," self.df_weather: DataFrame | None = None\n","\n"," self._rename_laps_columns()\n"," self._rename_weather_columns()\n","\n"," def get_weather_df(self) -> DataFrame:\n"," df = self.weather_data.copy()\n"," df = self._calc_timedelta_from_session_start(df, ['time'])\n","\n"," self.df_weather = df\n","\n"," return df\n","\n"," def save_to_SQL_database(self, database_name: str):\n"," \"\"\"\n"," @param database_name str must contain `.db` at the end\n"," \"\"\"\n"," if not self.sql_engine:\n"," self.sql_engine = create_engine(f'sqlite:///{database_name}')\n","\n"," if self.df_laps is not None:\n"," self.df_laps.to_sql(name=\"Laps\", con=self.sql_engine)\n"," print(f'> table \"Laps\" saved to {database_name}')\n","\n"," if self.df_weather is not None:\n"," self.df_weather.to_sql(name=\"Weather\", con=self.sql_engine)\n"," print(f'> table \"Weather\" saved to {database_name}')\n","\n"," def get_laps_df(self) -> DataFrame:\n"," \"\"\"Return the `laps` data related to the given session as a pandas DataFrame\"\"\"\n"," df = self.laps.copy()\n"," df = self._convert_timedelta_to_seconds(df, ['lap_time',\n"," 'sector_1_time', 'sector_2_time', 'sector_3_time'])\n"," df = self._calc_timedelta_from_session_start(\n"," df, ['pit_out', 'pit_in', 'lap_end'])\n"," unused_columns = ['DeletedReason', 'FastF1Generated', 'IsAccurate', 'LapStartTime',\n"," 'Sector1SessionTime', 'Sector2SessionTime', 'Sector3SessionTime']\n"," df.drop(columns=[col for col in unused_columns if col in df.columns],\n"," inplace=True,\n"," axis=1)\n","\n"," self.df_laps = df\n","\n"," return df\n","\n"," def _rename_weather_columns(self) -> None:\n"," weather_new_column_names = {\n"," \"Time\": \"time\",\n"," \"AirTemp\": \"air_temperature_in_C\",\n"," \"Humidity\": \"relative_humidity_in_percents\",\n"," \"Pressure\": \"air_pressure_in_mbar\",\n"," \"Rainfall\": \"is_raining\",\n"," \"TrackTemp\": \"track_temperature_in_C\",\n"," \"WindDirection\": \"wind_direction_in_grads\",\n"," \"WindSpeed\": \"wind_speed_in_ms\"\n"," }\n"," self.weather_data.rename(\n"," columns=weather_new_column_names, inplace=True)\n","\n"," def _rename_laps_columns(self) -> None:\n"," laps_new_column_names = {\n"," 'Driver': 'driver_name',\n"," 'DriverNumber': 'driver_number',\n"," 'LapNumber': 'lap_number',\n"," 'Stint': 'stint',\n"," 'SpeedI1': 'sector_1_speed_trap_in_km',\n"," 'SpeedI2': 'sector_2_speed_trap_in_km',\n"," 'SpeedFL': 'finish_line_speed_trap_in_km',\n"," 'SpeedST': 'longest_strait_speed_trap_in_km',\n"," 'IsPersonalBest': 'is_personal_best',\n"," 'Compound': 'tyre_compound',\n"," 'TyreLife': 'tyre_life_in_laps',\n"," 'FreshTyre': 'is_fresh_tyre',\n"," 'Position': 'position',\n"," 'LapTime': 'lap_time',\n"," 'Sector1Time': 'sector_1_time',\n"," 'Sector2Time': 'sector_2_time',\n"," 'Sector3Time': 'sector_3_time',\n"," 'LapStartDate': 'lap_start_datetime',\n"," 'Time': 'lap_end',\n"," 'PitInTime': 'pit_in',\n"," 'PitOutTime': 'pit_out',\n"," 'Team': 'team_name',\n"," 'TrackStatus': 'track_status',\n"," 'Deleted': 'is_lap_deleted'\n"," }\n"," self.laps.rename(columns=laps_new_column_names, inplace=True)\n","\n"," def _convert_timedelta_to_seconds(self, old_df: DataFrame, columns: list[str]) -> DataFrame:\n"," df = old_df.copy()\n","\n"," for col in columns:\n"," if not col in df.columns:\n"," continue\n"," df[col + '_in_seconds'] = df[col].dt.total_seconds()\n"," df.drop(columns=[col], inplace=True, axis=1)\n","\n"," return df\n","\n"," def _calc_timedelta_from_session_start(self, old_df: DataFrame, columns: list[str]) -> DataFrame:\n"," df = old_df.copy()\n","\n"," for col in columns:\n"," if not col in df.columns:\n"," continue\n"," df[col + '_datetime'] = self._session_start_date + df[col]\n"," df.drop(columns=[col], inplace=True, axis=1)\n","\n"," return df"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[],"source":["spanish_session = Formula1Databases(session=session)"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/html":["
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driver_namedriver_numberlap_numberstintsector_1_speed_trap_in_kmsector_2_speed_trap_in_kmfinish_line_speed_trap_in_kmlongest_strait_speed_trap_in_kmis_personal_besttyre_compound...track_statuspositionis_lap_deletedlap_time_in_secondssector_1_time_in_secondssector_2_time_in_secondssector_3_time_in_secondspit_out_datetimepit_in_datetimelap_end_datetime
0VER11.01.0263.0213.0282.0225.0FalseHARD...1NaNNoneNaNNaN35.70426.3532024-06-21 13:47:35.427NaT2024-06-21 13:49:02.676
1VER12.01.0283.0287.0281.0315.0TrueHARD...1NaNNone76.25422.50030.77022.984NaTNaT2024-06-21 13:50:18.930
2VER13.01.0183.0174.0NaN148.0FalseHARD...1NaNNone113.99537.08943.41333.493NaT2024-06-21 13:52:10.2522024-06-21 13:52:12.925
3VER14.02.087.0143.0286.0223.0FalseHARD...1NaNNone129.37053.81950.33225.2192024-06-21 13:52:33.412NaT2024-06-21 13:54:22.295
4VER15.02.0288.0303.0284.0317.0TrueHARD...1NaNNone75.42422.22930.45822.737NaTNaT2024-06-21 13:55:37.719
..................................................................
535PIA8125.06.0272.0262.0NaN304.0FalseMEDIUM...1NaNNone83.30523.51131.97627.818NaT2024-06-21 14:41:44.0362024-06-21 14:41:46.718
536PIA8126.07.0268.0258.0282.0250.0FalseSOFT...1NaNNone103.61147.18532.57923.8472024-06-21 14:42:11.042NaT2024-06-21 14:43:30.329
537PIA8127.07.0272.0253.0282.0313.0FalseSOFT...1NaNNone79.72323.06232.37724.284NaTNaT2024-06-21 14:44:50.052
538PIA8128.07.0207.0177.040.0263.0FalseSOFT...1NaNNone109.68528.97838.69042.017NaTNaT2024-06-21 14:46:39.737
539PIA8129.07.0159.0230.0NaN283.0FalseSOFT...1NaNNone129.87950.08534.87744.917NaT2024-06-21 14:48:46.3312024-06-21 14:48:49.616
\n","

540 rows × 24 columns

\n","
"],"text/plain":[" driver_name driver_number lap_number stint sector_1_speed_trap_in_km \\\n","0 VER 1 1.0 1.0 263.0 \n","1 VER 1 2.0 1.0 283.0 \n","2 VER 1 3.0 1.0 183.0 \n","3 VER 1 4.0 2.0 87.0 \n","4 VER 1 5.0 2.0 288.0 \n",".. ... ... ... ... ... \n","535 PIA 81 25.0 6.0 272.0 \n","536 PIA 81 26.0 7.0 268.0 \n","537 PIA 81 27.0 7.0 272.0 \n","538 PIA 81 28.0 7.0 207.0 \n","539 PIA 81 29.0 7.0 159.0 \n","\n"," sector_2_speed_trap_in_km finish_line_speed_trap_in_km \\\n","0 213.0 282.0 \n","1 287.0 281.0 \n","2 174.0 NaN \n","3 143.0 286.0 \n","4 303.0 284.0 \n",".. ... ... \n","535 262.0 NaN \n","536 258.0 282.0 \n","537 253.0 282.0 \n","538 177.0 40.0 \n","539 230.0 NaN \n","\n"," longest_strait_speed_trap_in_km is_personal_best tyre_compound ... \\\n","0 225.0 False HARD ... \n","1 315.0 True HARD ... \n","2 148.0 False HARD ... \n","3 223.0 False HARD ... \n","4 317.0 True HARD ... \n",".. ... ... ... ... \n","535 304.0 False MEDIUM ... \n","536 250.0 False SOFT ... \n","537 313.0 False SOFT ... \n","538 263.0 False SOFT ... \n","539 283.0 False SOFT ... \n","\n"," track_status position is_lap_deleted lap_time_in_seconds \\\n","0 1 NaN None NaN \n","1 1 NaN None 76.254 \n","2 1 NaN None 113.995 \n","3 1 NaN None 129.370 \n","4 1 NaN None 75.424 \n",".. ... ... ... ... \n","535 1 NaN None 83.305 \n","536 1 NaN None 103.611 \n","537 1 NaN None 79.723 \n","538 1 NaN None 109.685 \n","539 1 NaN None 129.879 \n","\n"," sector_1_time_in_seconds sector_2_time_in_seconds \\\n","0 NaN 35.704 \n","1 22.500 30.770 \n","2 37.089 43.413 \n","3 53.819 50.332 \n","4 22.229 30.458 \n",".. ... ... \n","535 23.511 31.976 \n","536 47.185 32.579 \n","537 23.062 32.377 \n","538 28.978 38.690 \n","539 50.085 34.877 \n","\n"," sector_3_time_in_seconds pit_out_datetime pit_in_datetime \\\n","0 26.353 2024-06-21 13:47:35.427 NaT \n","1 22.984 NaT NaT \n","2 33.493 NaT 2024-06-21 13:52:10.252 \n","3 25.219 2024-06-21 13:52:33.412 NaT \n","4 22.737 NaT NaT \n",".. ... ... ... \n","535 27.818 NaT 2024-06-21 14:41:44.036 \n","536 23.847 2024-06-21 14:42:11.042 NaT \n","537 24.284 NaT NaT \n","538 42.017 NaT NaT \n","539 44.917 NaT 2024-06-21 14:48:46.331 \n","\n"," lap_end_datetime \n","0 2024-06-21 13:49:02.676 \n","1 2024-06-21 13:50:18.930 \n","2 2024-06-21 13:52:12.925 \n","3 2024-06-21 13:54:22.295 \n","4 2024-06-21 13:55:37.719 \n",".. ... \n","535 2024-06-21 14:41:46.718 \n","536 2024-06-21 14:43:30.329 \n","537 2024-06-21 14:44:50.052 \n","538 2024-06-21 14:46:39.737 \n","539 2024-06-21 14:48:49.616 \n","\n","[540 rows x 24 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["\n","spanish_session.get_laps_df()"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["spanish_session.save_to_SQL_database(\"Spanish_Session\")"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"data":{"text/html":["
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air_temperature_in_Crelative_humidity_in_percentsair_pressure_in_mbaris_rainingtrack_temperature_in_Cwind_direction_in_gradswind_speed_in_mstime_datetime
027.537.01004.3False46.3930.02024-06-21 13:30:25.642
127.536.01004.5False46.400.02024-06-21 13:31:25.662
227.536.01004.5False47.32412.32024-06-21 13:32:25.653
327.634.01004.4False47.52262.02024-06-21 13:33:25.660
427.735.01004.4False46.42422.02024-06-21 13:34:25.667
...........................
7628.337.01004.1False48.01070.72024-06-21 14:46:25.846
7728.436.01004.2False47.82761.02024-06-21 14:47:25.835
7828.536.01004.2False46.12302.12024-06-21 14:48:25.832
7928.537.01004.2False46.22591.72024-06-21 14:49:25.839
8028.538.01004.2False47.02001.52024-06-21 14:50:25.848
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81 rows × 8 columns

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"],"text/plain":[" air_temperature_in_C relative_humidity_in_percents air_pressure_in_mbar \\\n","0 27.5 37.0 1004.3 \n","1 27.5 36.0 1004.5 \n","2 27.5 36.0 1004.5 \n","3 27.6 34.0 1004.4 \n","4 27.7 35.0 1004.4 \n",".. ... ... ... \n","76 28.3 37.0 1004.1 \n","77 28.4 36.0 1004.2 \n","78 28.5 36.0 1004.2 \n","79 28.5 37.0 1004.2 \n","80 28.5 38.0 1004.2 \n","\n"," is_raining track_temperature_in_C wind_direction_in_grads \\\n","0 False 46.3 93 \n","1 False 46.4 0 \n","2 False 47.3 241 \n","3 False 47.5 226 \n","4 False 46.4 242 \n",".. ... ... ... \n","76 False 48.0 107 \n","77 False 47.8 276 \n","78 False 46.1 230 \n","79 False 46.2 259 \n","80 False 47.0 200 \n","\n"," wind_speed_in_ms time_datetime \n","0 0.0 2024-06-21 13:30:25.642 \n","1 0.0 2024-06-21 13:31:25.662 \n","2 2.3 2024-06-21 13:32:25.653 \n","3 2.0 2024-06-21 13:33:25.660 \n","4 2.0 2024-06-21 13:34:25.667 \n",".. ... ... \n","76 0.7 2024-06-21 14:46:25.846 \n","77 1.0 2024-06-21 14:47:25.835 \n","78 2.1 2024-06-21 14:48:25.832 \n","79 1.7 2024-06-21 14:49:25.839 \n","80 1.5 2024-06-21 14:50:25.848 \n","\n","[81 rows x 8 columns]"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["spanish_session.get_weather_df()"]}],"metadata":{"kernelspec":{"display_name":"formula-1","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":"3.11.0"}},"nbformat":4,"nbformat_minor":2}