Races

Reference documentation for the races.csv table

The races.csv table contains information about every Formula 1 race, including Grand Prix events and session schedules.

Schema

Field	Type	Description
raceId	integer	Unique identifier for each race
year	integer	Season year
round	integer	Round number within the season
circuitId	integer	Foreign key to circuits.csv
name	string	Name of the Grand Prix
date	date	Date of the race (YYYY-MM-DD)
time	time	Race start time (UTC)
url	string	Wikipedia URL for the race
fp1_date	date	Free Practice 1 date
fp1_time	time	Free Practice 1 start time (UTC)
fp2_date	date	Free Practice 2 date
fp2_time	time	Free Practice 2 start time (UTC)
fp3_date	date	Free Practice 3 date
fp3_time	time	Free Practice 3 start time (UTC)
quali_date	date	Qualifying session date
quali_time	time	Qualifying session start time (UTC)
sprint_date	date	Sprint race date (if applicable)
sprint_time	time	Sprint race start time (UTC)

Practice session times (fp1_*, fp2_*, fp3_*) and qualifying times are \N for historical races where this data wasn’t recorded. Sprint fields are \N for races without sprint format.

Sample Data

raceId	year	round	circuitId	name	date	time
1	2009	1	1	Australian Grand Prix	2009-03-29	06:00:00
2	2009	2	2	Malaysian Grand Prix	2009-04-05	09:00:00
3	2009	3	17	Chinese Grand Prix	2009-04-19	07:00:00
4	2009	4	3	Bahrain Grand Prix	2009-04-26	12:00:00

Relationships

References:

• races.circuitId → circuits.circuitId

Referenced by:

• results.raceId → races.raceId
• qualifying.raceId → races.raceId
• sprint_results.raceId → races.raceId
• lap_times.raceId → races.raceId
• pit_stops.raceId → races.raceId
• driver_standings.raceId → races.raceId
• constructor_standings.raceId → races.raceId

Dataset Statistics

• Total Records: 1,173 races
• Date Range: 1950 - Present
• Races per Season: Varies (typically 15-24)

Example Queries

Get races for a specific season

import pandas as pd

races = pd.read_csv('races.csv')
season_2023 = races[races['year'] == 2023]
print(season_2023[['round', 'name', 'date']])

Find all races at a specific circuit

# Monaco (circuitId = 6)
monaco_races = races[races['circuitId'] == 6]
print(monaco_races[['year', 'date', 'name']])

Count races by year

races_per_year = races.groupby('year').size()
print(races_per_year)

Find sprint race weekends

sprint_races = races[races['sprint_date'].notna()]
print(sprint_races[['year', 'name', 'sprint_date']])

Get upcoming races (if dataset is current)

from datetime import datetime

races['date'] = pd.to_datetime(races['date'])
upcoming = races[races['date'] > datetime.now()]
print(upcoming[['name', 'date', 'time']].head())

Join with circuits for location data

circuits = pd.read_csv('circuits.csv')
races_with_location = races.merge(circuits, on='circuitId')
print(races_with_location[['year', 'name', 'location', 'country']])

Notes

• All times are stored in UTC format
• \N represents null values for missing data
• The sprint race format was introduced in 2021, so earlier races have \N for sprint fields
• Practice session data is more complete for recent seasons
• The round field resets to 1 each season
• Some races may have been cancelled or rescheduled (check results table for actual participation)