Getting started with the basics#
FastF1 is built mainly around Pandas DataFrame and Series objects. If you are familiar with Pandas you’ll immediately recognize this and working with the data will be fairly straightforward. (If you’re not familiar with Pandas at all, it might be helpful to check out a short tutorial.)
Loading a session or an event#
The fastf1.core.Session object is an important starting point for
everything you do with FastF1. Usually, the first thing you want to do
is load a session. For this, you should use
fastf1.get_session().
For example, let’s load the Qualifying of the 7th race of the 2021 season:
>>> import fastf1
>>> session = fastf1.get_session(2021, 7, 'Q')
>>> session.name
'Qualifying'
>>> session.date
Timestamp('2021-06-19 13:00:00')
Now, which race weekend are we actually looking at here?
For this we have the Event object which holds
information about each event. An event can be a race weekend or a testing
event and usually consists of multiple sessions. It is accessible through the
session object.
>>> session.event
RoundNumber 7
Country France
Location Le Castellet
OfficialEventName FORMULA 1 EMIRATES GRAND PRIX DE FRANCE 2021
EventDate 2021-06-20 00:00:00
EventName French Grand Prix
EventFormat conventional
Session1 Practice 1
Session1Date 2021-06-18 11:30:00+02:00
Session1DateUtc 2021-06-18 09:30:00
Session2 Practice 2
Session2Date 2021-06-18 15:00:00+02:00
Session2DateUtc 2021-06-18 13:00:00
Session3 Practice 3
Session3Date 2021-06-19 12:00:00+02:00
Session3DateUtc 2021-06-19 10:00:00
Session4 Qualifying
Session4Date 2021-06-19 15:00:00+02:00
Session4DateUtc 2021-06-19 13:00:00
Session5 Race
Session5Date 2021-06-20 15:00:00+02:00
Session5DateUtc 2021-06-20 13:00:00
F1ApiSupport True
Name: 7, dtype: object
The Event object is a subclass of a
pandas.Series. The individual values can therefore be accessed as it
is common for pandas objects:
>>> session.event['EventName']
'French Grand Prix'
>>> session.event['EventDate'] # this is the date of the race day
Timestamp('2021-06-20 00:00:00')
You can also load an event directly, by using the function
fastf1.get_event(). The Event object in turn
provides methods for accessing the individual associated sessions.
>>> event = fastf1.get_event(2021, 7)
>>> event
RoundNumber 7
Country France
Location Le Castellet
OfficialEventName FORMULA 1 EMIRATES GRAND PRIX DE FRANCE 2021
EventDate 2021-06-20 00:00:00
EventName French Grand Prix
EventFormat conventional
Session1 Practice 1
Session1Date 2021-06-18 11:30:00+02:00
Session1DateUtc 2021-06-18 09:30:00
Session2 Practice 2
Session2Date 2021-06-18 15:00:00+02:00
Session2DateUtc 2021-06-18 13:00:00
Session3 Practice 3
Session3Date 2021-06-19 12:00:00+02:00
Session3DateUtc 2021-06-19 10:00:00
Session4 Qualifying
Session4Date 2021-06-19 15:00:00+02:00
Session4DateUtc 2021-06-19 13:00:00
Session5 Race
Session5Date 2021-06-20 15:00:00+02:00
Session5DateUtc 2021-06-20 13:00:00
F1ApiSupport True
Name: 7, dtype: object
>>> session = event.get_race()
>>> session.name
'Race'
Loading a session or an event by name#
As an alternative to specifying an event number you can also load events by using a clearly identifying name.
>>> event = fastf1.get_event(2021, 'French Grand Prix')
>>> event['EventName']
'French Grand Prix'
You do not need to provide the exact event name. FastF1 will return the event (or session) that matches your provided name best. Even if you don’t specify the correct name chances are high that FastF1 will find the event you are looking for.
>>> event = fastf1.get_event(2021, 'Spain')
>>> event['EventName']
'Spanish Grand Prix'
But be aware that this does not always work. Sometimes another name just matches the provided string better. For example, what we actually want is the ‘Emilia Romagna Grand Prix’ but we get the ‘Belgian Grand Prix’ if we don’t specify the name fully and/or correct enough. Why? Because FastF1 is not a proper intelligent search engine. So check your results.
>>> event = fastf1.get_event(2021, 'Emilian')
>>> event['EventName']
'Belgian Grand Prix'
We need to be a bit more precise here.
>>> event = fastf1.get_event(2021, 'Emilia Romagna')
>>> event['EventName']
'Emilia Romagna Grand Prix'
Events and sessions can also be loaded by their country or location.
>>> session = fastf1.get_session(2021, 'Silverstone', 'Q')
>>> session.event['EventName']
'British Grand Prix'
Working with the event schedule#
Instead of loading a specific session or event, it is possible to load the
full event schedule for a season. The EventSchedule
is a subclass of a pandas.DataFrame. Detailed information about
the data that is available in the event schedule can be found in
events.
>>> schedule = fastf1.get_event_schedule(2021)
>>> schedule
RoundNumber Country ... Session5DateUtc F1ApiSupport
0 0 Bahrain ... NaT False
1 1 Bahrain ... 2021-03-28 15:00:00 True
2 2 Italy ... 2021-04-18 13:00:00 True
3 3 Portugal ... 2021-05-02 14:00:00 True
4 4 Spain ... 2021-05-09 13:00:00 True
5 5 Monaco ... 2021-05-23 13:00:00 True
6 6 Azerbaijan ... 2021-06-06 12:00:00 True
7 7 France ... 2021-06-20 13:00:00 True
8 8 Austria ... 2021-06-27 13:00:00 True
9 9 Austria ... 2021-07-04 13:00:00 True
10 10 Great Britain ... 2021-07-18 14:00:00 True
11 11 Hungary ... 2021-08-01 13:00:00 True
12 12 Belgium ... 2021-08-29 13:00:00 True
13 13 Netherlands ... 2021-09-05 13:00:00 True
14 14 Italy ... 2021-09-12 13:00:00 True
15 15 Russia ... 2021-09-26 12:00:00 True
16 16 Turkey ... 2021-10-10 12:00:00 True
17 17 United States ... 2021-10-24 19:00:00 True
18 18 Mexico ... 2021-11-07 19:00:00 True
19 19 Brazil ... 2021-11-14 17:00:00 True
20 20 Qatar ... 2021-11-21 14:00:00 True
21 21 Saudi Arabia ... 2021-12-05 17:30:00 True
22 22 Abu Dhabi ... 2021-12-12 13:00:00 True
[23 rows x 23 columns]
>>> schedule.columns
Index(['RoundNumber', 'Country', 'Location', 'OfficialEventName', 'EventDate',
'EventName', 'EventFormat', 'Session1', 'Session1Date',
'Session1DateUtc', 'Session2', 'Session2Date', 'Session2DateUtc',
'Session3', 'Session3Date', 'Session3DateUtc', 'Session4',
'Session4Date', 'Session4DateUtc', 'Session5', 'Session5Date',
'Session5DateUtc', 'F1ApiSupport'],
dtype='object')
The event schedule provides methods for selecting specific events:
>>> gp_12 = schedule.get_event_by_round(12)
>>> gp_12['Country']
'Belgium'
>>> gp_austin = schedule.get_event_by_name('Austin')
>>> gp_austin['Country']
'United States'
Displaying driver info and session results#
We have created a session now but everything has been rather boring so far.
So let’s make it a bit more interesting by taking a look at the results of
this session. For this, it is first necessary to call
Session.load. This will load all available data for the
session from various APIs. Downloading and processing of the data may take a
few seconds. It is highly recommended to utilize FastF1’s built-in caching
functionality to speed up data loading and prevent excessive API requests.
>>> session = fastf1.get_session(2021, 'French Grand Prix', 'Q')
>>> session.load()
>>> session.results
DriverNumber BroadcastName Abbreviation ... Time Status Points
33 33 M VERSTAPPEN VER ... NaT NaN
44 44 L HAMILTON HAM ... NaT NaN
77 77 V BOTTAS BOT ... NaT NaN
11 11 S PEREZ PER ... NaT NaN
55 55 C SAINZ SAI ... NaT NaN
10 10 P GASLY GAS ... NaT NaN
16 16 C LECLERC LEC ... NaT NaN
4 4 L NORRIS NOR ... NaT NaN
14 14 F ALONSO ALO ... NaT NaN
3 3 D RICCIARDO RIC ... NaT NaN
31 31 E OCON OCO ... NaT NaN
5 5 S VETTEL VET ... NaT NaN
99 99 A GIOVINAZZI GIO ... NaT NaN
63 63 G RUSSELL RUS ... NaT NaN
47 47 M SCHUMACHER MSC ... NaT NaN
6 6 N LATIFI LAT ... NaT NaN
7 7 K RAIKKONEN RAI ... NaT NaN
9 9 N MAZEPIN MAZ ... NaT NaN
18 18 L STROLL STR ... NaT NaN
22 22 Y TSUNODA TSU ... NaT NaN
[20 rows x 21 columns]
The results object (fastf1.core.SessionResults) is a subclass of a
pandas.DataFrame. Therefore, we can take a look at what data columns
there are:
>>> session.results.columns
Index(['DriverNumber', 'BroadcastName', 'Abbreviation', 'DriverId', 'TeamName',
'TeamColor', 'TeamId', 'FirstName', 'LastName', 'FullName',
'HeadshotUrl', 'CountryCode', 'Position', 'ClassifiedPosition',
'GridPosition', 'Q1', 'Q2', 'Q3', 'Time', 'Status', 'Points'],
dtype='object')
As an example, let’s display the top ten drivers and their respective Q3 times. The results are sorted by finishing position, therefore, this is easy.
>>> session.results.iloc[0:10].loc[:, ['Abbreviation', 'Q3']]
Abbreviation Q3
33 VER 0 days 00:01:29.990000
44 HAM 0 days 00:01:30.248000
77 BOT 0 days 00:01:30.376000
11 PER 0 days 00:01:30.445000
55 SAI 0 days 00:01:30.840000
10 GAS 0 days 00:01:30.868000
16 LEC 0 days 00:01:30.987000
4 NOR 0 days 00:01:31.252000
14 ALO 0 days 00:01:31.340000
3 RIC 0 days 00:01:31.382000
Working with laps and lap times#
All individual laps of a session can be accessed through the property
Session.laps. The laps are represented
as Laps object which again is a subclass of a
pandas.DataFrame.
>>> session = fastf1.get_session(2021, 'French Grand Prix', 'Q')
>>> session.load()
>>> session.laps
Time Driver ... FastF1Generated IsAccurate
0 0 days 00:17:35.479000 GAS ... False False
1 0 days 00:27:42.702000 GAS ... False False
2 0 days 00:30:15.038000 GAS ... False False
3 0 days 00:31:46.936000 GAS ... False True
4 0 days 00:34:20.695000 GAS ... False False
.. ... ... ... ... ...
265 0 days 00:54:22.881000 GIO ... False True
266 0 days 01:00:32.369000 GIO ... False False
267 0 days 01:03:24.940000 GIO ... False False
268 0 days 01:04:56.753000 GIO ... False True
269 0 days 01:06:42.885000 GIO ... False False
[270 rows x 31 columns]
That’s more than 250 laps right there and 26 columns of information.
The following data columns are available:
>>> session.laps.columns
Index(['Time', 'Driver', 'DriverNumber', 'LapTime', 'LapNumber', 'Stint',
'PitOutTime', 'PitInTime', 'Sector1Time', 'Sector2Time', 'Sector3Time',
'Sector1SessionTime', 'Sector2SessionTime', 'Sector3SessionTime',
'SpeedI1', 'SpeedI2', 'SpeedFL', 'SpeedST', 'IsPersonalBest',
'Compound', 'TyreLife', 'FreshTyre', 'Team', 'LapStartTime',
'LapStartDate', 'TrackStatus', 'Position', 'Deleted', 'DeletedReason',
'FastF1Generated', 'IsAccurate'],
dtype='object')
A detailed explanation for all these columns can be found in the
documentation of the Laps class.
The Laps object is not a simple DataFrame though.
Like FastF1’s other data objects, it provides some more features specifically
for working with Formula 1 data.
One of these additional features are methods for selecting specific laps. So let’s see what the fastest lap time was and who is on pole.
>>> fastest_lap = session.laps.pick_fastest()
>>> fastest_lap['LapTime']
Timedelta('0 days 00:01:29.990000')
>>> fastest_lap['Driver']
'VER'
Check out this example that shows how you can plot lap times: Qualifying results overview