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.raceIdqualifying.raceId → races.raceIdsprint_results.raceId → races.raceIdlap_times.raceId → races.raceIdpit_stops.raceId → races.raceIddriver_standings.raceId → races.raceIdconstructor_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)
