pip install fastf1

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Note: you may need to restart the kernel to use updated packages.

# Import necessary libraries

import fastf1  # Access F1 telemetry and lap data
import numpy as np
import pandas as pd  # For structured data manipulation
import matplotlib.pyplot as plt  # Static plots
import seaborn as sns  # Statistical plots
import plotly.graph_objects as go  # Interactive charts like Sankey

sns.set_theme()

fastf1.Cache.enable_cache("fastf1_cache_dir")

# Enable caching to prevent repeated downloads
fastf1.Cache.enable_cache("fastf1_cache_dir")

year = 2025
gp = "São Paulo"
session_name = "R"   # 'Q' for qualifying

session = fastf1.get_session(year, gp, session_name)
session.load()
session

# # Check Race schedule
# schedule= fastf1.get_event_schedule(2025)
# schedule

core           INFO 	Loading data for São Paulo Grand Prix - Race [v3.7.0]
req            INFO 	Using cached data for session_info
req            INFO 	Using cached data for driver_info
req            INFO 	Using cached data for session_status_data
req            INFO 	Using cached data for lap_count
req            INFO 	Using cached data for track_status_data
req            INFO 	Using cached data for _extended_timing_data
req            INFO 	Using cached data for timing_app_data
core           INFO 	Processing timing data...
req            INFO 	Using cached data for car_data
req            INFO 	Using cached data for position_data
req            INFO 	Using cached data for weather_data
req            INFO 	Using cached data for race_control_messages
core        WARNING 	Driver 4 completed the race distance 00:00.010000 before the recorded end of the session.
core           INFO 	Finished loading data for 20 drivers: ['4', '12', '1', '63', '81', '87', '30', '6', '27', '10', '23', '31', '55', '14', '43', '18', '22', '44', '16', '5']

2025 Season Round 21: São Paulo Grand Prix - Race

event = session.event
event

RoundNumber                                                         21
Country                                                         Brazil
Location                                                     São Paulo
OfficialEventName    FORMULA 1 MSC CRUISES GRANDE PRÊMIO DE SÃO PAU...
EventDate                                          2025-11-09 00:00:00
EventName                                         São Paulo Grand Prix
EventFormat                                          sprint_qualifying
Session1                                                    Practice 1
Session1Date                                 2025-11-07 11:30:00-03:00
Session1DateUtc                                    2025-11-07 14:30:00
Session2                                             Sprint Qualifying
Session2Date                                 2025-11-07 15:30:00-03:00
Session2DateUtc                                    2025-11-07 18:30:00
Session3                                                        Sprint
Session3Date                                 2025-11-08 11:00:00-03:00
Session3DateUtc                                    2025-11-08 14:00:00
Session4                                                    Qualifying
Session4Date                                 2025-11-08 15:00:00-03:00
Session4DateUtc                                    2025-11-08 18:00:00
Session5                                                          Race
Session5Date                                 2025-11-09 14:00:00-03:00
Session5DateUtc                                    2025-11-09 17:00:00
F1ApiSupport                                                      True
Name: 21, dtype: object

weather = getattr(session, "weather_data", None)
weather.head() if weather is not None else "No weather_data attribute found in this FastF1 session."

laps = session.laps
laps.head()

laps.columns.tolist()

['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']

# laps = session.laps.pick_driver("VER"): pick_driver is deprecated. Use pick_drivers instead. There's dirver and then there's drivers.

laps = session.laps.pick_drivers("VER")
stints = laps.groupby("Stint")
stints.head()

session.load()
session.track_status

core           INFO 	Loading data for São Paulo Grand Prix - Race [v3.7.0]
req            INFO 	Using cached data for session_info
req            INFO 	Using cached data for driver_info
req            INFO 	Using cached data for session_status_data
req            INFO 	Using cached data for lap_count
req            INFO 	Using cached data for track_status_data
req            INFO 	Using cached data for _extended_timing_data
req            INFO 	Using cached data for timing_app_data
core           INFO 	Processing timing data...
req            INFO 	Using cached data for car_data
req            INFO 	Using cached data for position_data
req            INFO 	Using cached data for weather_data
req            INFO 	Using cached data for race_control_messages
core        WARNING 	Driver 4 completed the race distance 00:00.010000 before the recorded end of the session.
core           INFO 	Finished loading data for 20 drivers: ['4', '12', '1', '63', '81', '87', '30', '6', '27', '10', '23', '31', '55', '14', '43', '18', '22', '44', '16', '5']

driver = session.drivers[2]
driver

# driver_laps = session.laps.pick_driver(driver): pick_driver is deprecated... but for some reason I keep forgetting.
driver_laps = session.laps.pick_drivers(driver)

# pick a lap that has a LapTime and not NaN
lap = driver_laps.dropna(subset=["LapTime"]).iloc[0]
lap

Time                      0 days 00:58:21.863000
Driver                                       VER
DriverNumber                                   1
LapTime                   0 days 00:01:27.191000
LapNumber                                    1.0
Stint                                        1.0
PitOutTime                0 days 00:57:08.347000
PitInTime                                    NaT
Sector1Time                                  NaT
Sector2Time               0 days 00:00:39.288000
Sector3Time               0 days 00:00:17.131000
Sector1SessionTime                           NaT
Sector2SessionTime        0 days 00:58:04.976000
Sector3SessionTime        0 days 00:58:22.006000
SpeedI1                                    306.0
SpeedI2                                    244.0
SpeedFL                                    305.0
SpeedST                                      NaN
IsPersonalBest                             False
Compound                                    HARD
TyreLife                                     1.0
FreshTyre                                   True
Team                             Red Bull Racing
LapStartTime              0 days 00:56:54.480000
LapStartDate          2025-11-09 17:03:14.079000
TrackStatus                                   12
Position                                    18.0
Deleted                                    False
DeletedReason                                   
FastF1Generated                            False
IsAccurate                                 False
Name: 0, dtype: object

tel = lap.get_telemetry()
tel.head(), tel.columns.tolist()

(                     Date            SessionTime DriverAhead  \
 2 2025-11-09 17:03:14.079 0 days 00:56:54.480000               
 3 2025-11-09 17:03:14.142 0 days 00:56:54.543000               
 4 2025-11-09 17:03:14.324 0 days 00:56:54.725000               
 5 2025-11-09 17:03:14.342 0 days 00:56:54.743000               
 6 2025-11-09 17:03:14.564 0 days 00:56:54.965000          31   
 
    DistanceToDriverAhead                   Time          RPM      Speed  \
 2                  2.025        0 days 00:00:00  3529.597210  36.763888   
 3                  2.025 0 days 00:00:00.063000  3517.872198  35.538886   
 4                  2.025 0 days 00:00:00.245000  3484.000000  32.000000   
 5                  2.025 0 days 00:00:00.263000  3488.725034  31.924999   
 6                  2.025 0 days 00:00:00.485000  3547.000000  31.000000   
 
    nGear  Throttle  Brake  DRS         Source  Distance  RelativeDistance  \
 2      1       0.0   True    1  interpolation -0.224661         -0.000057   
 3      1       0.0   True    1            pos  0.335175          0.000086   
 4      1       0.0   True    1            car  1.954167          0.000500   
 5      1       0.0   True    1            pos  2.114423          0.000541   
 6      1       0.0   True    1            car  4.020833          0.001028   
 
     Status            X            Y       Z  
 2  OnTrack -2707.329260 -8073.792045  7766.0  
 3  OnTrack -2704.000000 -8081.000000  7766.0  
 4  OnTrack -2692.067640 -8105.921700  7766.0  
 5  OnTrack -2691.000000 -8108.000000  7766.0  
 6  OnTrack -2681.402321 -8123.656896  7766.0  ,
 ['Date',
  'SessionTime',
  'DriverAhead',
  'DistanceToDriverAhead',
  'Time',
  'RPM',
  'Speed',
  'nGear',
  'Throttle',
  'Brake',
  'DRS',
  'Source',
  'Distance',
  'RelativeDistance',
  'Status',
  'X',
  'Y',
  'Z'])

fig, ax = plt.subplots(figsize=(10,4))
ax.plot(tel["Distance"], tel["Speed"])
ax.set_title(f"Speed vs Distance — {lap['Driver']} Lap {lap['LapNumber']}")
ax.set_xlabel("Distance (m)")
ax.set_ylabel("Speed (km/h)")
plt.show()

# Throttle & Brake often exist as 0-100 and True/False or 0/1
if "Throttle" in tel.columns:
    fig, ax = plt.subplots(figsize=(10,4))
    ax.plot(tel["Distance"], tel["Throttle"])
    ax.set_title("Throttle vs Distance")
    ax.set_xlabel("Distance (m)")
    ax.set_ylabel("Throttle (%)")
    plt.show()

if "Brake" in tel.columns:
    fig, ax = plt.subplots(figsize=(10,4))
    ax.plot(tel["Distance"], tel["Brake"])
    ax.set_title("Brake vs Distance")
    ax.set_xlabel("Distance (m)")
    ax.set_ylabel("Brake")
    plt.show()

plt.figure(figsize=(8,5))

# Scatter plot showing relationship between tyre life and lap time
sns.scatterplot(
    data=laps,
    x="TyreLife",  # Tyre life in laps
    y=laps["LapTime"].dt.total_seconds(),  # Convert lap time to seconds
    hue="Compound"  # Color by tyre compound
)

plt.title("Lap Time vs Tyre Life")
plt.xlabel("Tyre Life (laps)")
plt.ylabel("Lap Time (seconds)")
plt.show()

# Histogram with KDE overlay
sns.histplot(
    laps["LapTime"].dt.total_seconds(),    # Converting LapTime to seconds
    bins=40,
    kde=True  # Adds smooth density curve
)
plt.title("Distribution of Lap Times")
plt.xlabel("Lap Time (seconds)")
plt.show()

lap = session.laps.pick_fastest()
pos = lap.get_pos_data()

circuit_info = session.get_circuit_info()

print(lap)

Time                      0 days 02:15:00.987000
Driver                                       ALB
DriverNumber                                  23
LapTime                   0 days 00:01:12.400000
LapNumber                                   59.0
Stint                                        3.0
PitOutTime                                   NaT
PitInTime                                    NaT
Sector1Time               0 days 00:00:18.386000
Sector2Time               0 days 00:00:37.404000
Sector3Time               0 days 00:00:16.610000
Sector1SessionTime        0 days 02:14:07.025000
Sector2SessionTime        0 days 02:14:44.429000
Sector3SessionTime        0 days 02:15:01.039000
SpeedI1                                    330.0
SpeedI2                                    258.0
SpeedFL                                    327.0
SpeedST                                    327.0
IsPersonalBest                              True
Compound                                  MEDIUM
TyreLife                                     4.0
FreshTyre                                   True
Team                                    Williams
LapStartTime              0 days 02:13:48.587000
LapStartDate          2025-11-09 18:20:08.186000
TrackStatus                                    1
Position                                    14.0
Deleted                                    False
DeletedReason                                   
FastF1Generated                            False
IsAccurate                                  True
Name: 490, dtype: object

'''
https://docs.fastf1.dev/gen_modules/examples_gallery/general/plot_annotate_corners.html#sphx-glr-gen-modules-examples-gallery-general-plot-annotate-corners-py
'''

# The matric [[cos, sin], [-sin, cos]] if called a rotation matrix
# My matrix multiplication of the roation matrix with a vector [x, ]y, a new rotated vector [x_rot, y_rot] is obtained.
def rotate(xy, *, angle):
    rot_mat = np.array([[np.cos(angle), np.sin(angle)],
                        [-np.sin(angle), np.cos(angle)]])
    return np.matmul(xy, rot_mat)



# Get an array of shape [n, 2] where n is the number of points and the second
# axis is x and y.
track = pos.loc[:, ('X', 'Y')].to_numpy()

# Convert the rotation angle from degrees to radian.
track_angle = circuit_info.rotation / 180 * np.pi

# Rotate and plot the track map.
rotated_track = rotate(track, angle=track_angle)
plt.plot(rotated_track[:, 0], rotated_track[:, 1])

[<matplotlib.lines.Line2D at 0xfbcde30cdf90>]

import fastf1.plotting

# Enable Matplotlib patches for plotting timedelta values and load
# FastF1's dark color scheme
fastf1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# Loading session again (just in case)
session = fastf1.get_session(2025, "Monza", "R")
session.load()

core           INFO 	Loading data for Italian Grand Prix - Race [v3.7.0]
req            INFO 	Using cached data for session_info
req            INFO 	Using cached data for driver_info
req            INFO 	Using cached data for session_status_data
req            INFO 	Using cached data for lap_count
req            INFO 	Using cached data for track_status_data
req            INFO 	Using cached data for _extended_timing_data
req            INFO 	Using cached data for timing_app_data
core           INFO 	Processing timing data...
req            INFO 	Using cached data for car_data
req            INFO 	Using cached data for position_data
req            INFO 	Using cached data for weather_data
req            INFO 	Using cached data for race_control_messages
core           INFO 	Finished loading data for 20 drivers: ['1', '4', '81', '16', '63', '44', '23', '5', '12', '6', '55', '87', '22', '30', '31', '10', '43', '18', '14', '27']

driver_laps = session.laps.pick_drivers("VER").pick_quicklaps().reset_index()

fig, ax = plt.subplots(figsize=(8, 8))

sns.scatterplot(data=driver_laps,
                x="LapNumber",
                y="LapTime",
                ax=ax,
                hue="Compound",
                palette=fastf1.plotting.get_compound_mapping(session=session),
                s=80,
                linewidth=0,
                legend='auto')

<Axes: xlabel='LapNumber', ylabel='LapTime'>

# import numpy as np
# import matplotlib.pyplot as plt

# # Convert LapTime to seconds - already converted

# # Encode tyre compound
# compound_map = {
#     "SOFT": 0, 
#     "MEDIUM": 1, 
#     "HARD": 2
# }

# # loc - explicitly sets the coloumn on the full DataFrame
# laps.loc[:, "CompoundEncoded"] = laps["Compound"].map(compound_map)

# # Drop missing values
# data = laps[["LapTime", "TyreLife", "CompoundEncoded"]].dropna()

# # Using code snippt from Professor Neil's Linear and Quadratic Fitting
# # X = TyreLife, Y = LapTime
# x = data["TyreLife"].values
# y = data["LapTime"].dt.total_seconds().values

# # Fit linear and quadratic
# coeff1 = np.polyfit(x, y, 1)  # linear
# coeff2 = np.polyfit(x, y, 2)  # quadratic

# # Prepare fitted curves
# xfit = np.linspace(x.min(), x.max(), 100)
# yfit1 = np.poly1d(coeff1)(xfit)
# yfit2 = np.poly1d(coeff2)(xfit)

# # Plot
# plt.figure(figsize=(8,5))
# plt.scatter(x, y, alpha=0.5, label="laps")
# plt.plot(xfit, yfit1, 'g-', label="linear fit")
# plt.plot(xfit, yfit2, 'r-', label="quadratic fit")
# plt.xlabel("Tyre Life (laps)")
# plt.ylabel("Lap Time (s)")
# plt.title("Lap Time vs Tyre Life")
# plt.legend()
# plt.show()

# from sklearn.linear_model import LinearRegression

# # X = TyreLife + CompoundEncoded
# X = data[["TyreLife", "CompoundEncoded"]].values
# y = data["LapTime"].values

# # Linear regression
# model = LinearRegression()
# model.fit(X, y)

# # Print coefficients
# print("Intercept:", model.intercept_)
# print("Coefficients:", model.coef_)

# import pandas as pd

# X_quad = pd.DataFrame({
#     "TyreLife": data["TyreLife"],
#     "TyreLife2": data["TyreLife"]**2,
#     "CompoundEncoded": data["CompoundEncoded"]
# })

# model = LinearRegression()
# model.fit(X_quad, y)
# print("Intercept:", model.intercept_)
# print("Coefficients:", model.coef_)

	Time	AirTemp	Humidity	Pressure	Rainfall	TrackTemp	WindDirection	WindSpeed
0	0 days 00:01:01.035000	17.9	70.0	929.9	False	32.7	124	1.0
1	0 days 00:02:01.033000	17.9	70.0	929.9	False	32.7	160	1.3
2	0 days 00:03:01.033000	17.9	70.0	930.0	False	32.8	178	1.5
3	0 days 00:04:01.035000	18.0	70.0	929.9	False	32.8	158	2.0
4	0 days 00:05:01.034000	17.9	70.0	930.0	False	32.3	177	1.9

	Time	Status	Message
0	0 days 00:00:00	1	AllClear
1	0 days 00:46:32.143000	2	Yellow
2	0 days 00:46:35.717000	1	AllClear
3	0 days 00:57:56.247000	2	Yellow
4	0 days 00:58:38.234000	4	SCDeployed
5	0 days 01:05:22.922000	1	AllClear
6	0 days 01:06:19.217000	2	Yellow
7	0 days 01:07:12.049000	6	VSCDeployed
8	0 days 01:10:06.866000	7	VSCEnding
9	0 days 01:10:19.151000	1	AllClear

Week 2: F1 Data Exploration

What is FastF1?¶

Install FastF1 in Jupyter Environment¶

Libraries¶

Importing and Load Session¶

2025 São Paulo Grand Prix¶

Loading Session¶

Metadata¶

Weather data¶

Lap Table¶

Stints (Tyre runs)¶

Driver Inspection¶

Extras: Telemetry¶

Plot telemetry (Speed + Throttle + Brake)¶

Visualization¶

Scatter Plot - Tyre Life vs Lap Time¶

Histogram - Lap Time Distribution¶

Sankey¶

Drawing a track map using FastF1 Tutorials¶

Linear and Quadratic Function¶

Fitting a Linear and Qudratic to LapTime and TyreLife¶

Multiple Features with Linear Regression¶

	Time	Driver	DriverNumber	LapTime	LapNumber	Stint	PitOutTime	PitInTime	Sector1Time	Sector2Time	...	FreshTyre	Team	LapStartTime	LapStartDate	TrackStatus	Position	Deleted	FastF1Generated	IsAccurate
0	0 days 00:58:21.863000	VER	1	0 days 00:01:27.191000	1.0	1.0	0 days 00:57:08.347000	NaT	NaT	0 days 00:00:39.288000	...	True	Red Bull Racing	0 days 00:56:54.480000	2025-11-09 17:03:14.079	12	18.0	False	False	False
1	0 days 01:00:22.276000	VER	1	0 days 00:02:00.413000	2.0	1.0	NaT	NaT	0 days 00:00:21.320000	0 days 00:01:15.859000	...	True	Red Bull Racing	0 days 00:58:21.863000	2025-11-09 17:04:41.462	24	17.0	False	False	False
2	0 days 01:02:11.283000	VER	1	0 days 00:01:49.007000	3.0	1.0	NaT	NaT	0 days 00:00:21.675000	0 days 00:00:58.682000	...	True	Red Bull Racing	0 days 01:00:22.276000	2025-11-09 17:06:41.875	4	16.0	False	False	False
3	0 days 01:03:57.903000	VER	1	0 days 00:01:46.620000	4.0	1.0	NaT	NaT	0 days 00:00:25.318000	0 days 00:00:59.748000	...	True	Red Bull Racing	0 days 01:02:11.283000	2025-11-09 17:08:30.882	4	16.0	False	False	False
4	0 days 01:05:45.792000	VER	1	0 days 00:01:47.889000	5.0	1.0	NaT	NaT	0 days 00:00:30.078000	0 days 00:00:50.463000	...	True	Red Bull Racing	0 days 01:03:57.903000	2025-11-09 17:10:17.502	41	16.0	False	False	False