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The constructors.csv table contains information about all Formula 1 constructor teams that have participated in the championship.

Schema

FieldTypeDescription
constructorIdintegerUnique identifier for each constructor
constructorRefstringShort reference name for the constructor (URL-friendly)
namestringFull official name of the constructor team
nationalitystringNationality/country of the constructor
urlstringWikipedia URL for the constructor

Sample Data

constructorIdconstructorRefnamenationality
1mclarenMcLarenBritish
2bmw_sauberBMW SauberGerman
3williamsWilliamsBritish
4renaultRenaultFrench

Relationships

Referenced by:
  • results.constructorIdconstructors.constructorId
  • qualifying.constructorIdconstructors.constructorId
  • sprint_results.constructorIdconstructors.constructorId
  • constructor_standings.constructorIdconstructors.constructorId
  • constructor_results.constructorIdconstructors.constructorId

Dataset Statistics

  • Total Records: 214 constructors
  • Date Range: 1950 - Present
  • Active Teams: Varies by season

Example Queries

Find constructors by nationality

import pandas as pd

constructors = pd.read_csv('constructors.csv')
british_teams = constructors[constructors['nationality'] == 'British']
print(british_teams[['name', 'constructorRef']])

Get constructor details by reference

ferrari = constructors[constructors['constructorRef'] == 'ferrari']
print(ferrari)

Count constructors by nationality

nationality_counts = constructors['nationality'].value_counts()
print(nationality_counts.head(10))

Search for specific constructor

red_bull = constructors[constructors['name'].str.contains('Red Bull', case=False)]
print(red_bull)

Notes

  • Some constructors have changed names over the years (e.g., Sauber → Alfa Romeo → Sauber)
  • The same entity may appear multiple times if it competed under different names
  • Nationality reflects the team’s country of origin, not necessarily where they’re based
  • Customer teams are listed separately from their engine suppliers

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