Learn Python with F 1
In this extensive tutorial, you’ll discover how to produce magnificent, professional-quality infographics for among the globe’s most data-rich sporting activities. Utilizing the 2025 Azerbaijan Grand Prix as our canvas, we’ll transform raw racing information right into engaging aesthetic stories that would certainly make any kind of F 1 broadcaster proud.
Enjoy complete, open door to this analysis. No paywall or registration required ¡ ¡.
Material:
Arrangement & & installment section
Data setup area
Visualization features
Complete python code (Github)
Setup & & installation area
Sets up all the visualization collections we require.
- matplotlib/seaborn: Static graphes and charts
- plotly: Interactive control panels
- kaleido: Exports plotly charts to images
- pandas/numpy: Data control
- PIL (pillow): Photo processing
# Mount called for bundles
pip set up matplotlib seaborn plotly kaleido cushion requests numpy pandas
# Import all required collections
import matplotlib.pyplot as plt
import matplotlib.patches as spots
import seaborn as sns
import plotly.graph _ things as go
import plotly.express as px
from plotly.subplots import make_subplots
import numpy as np
import pandas as pd
from datetime import datetime
import demands
from PIL import Picture, ImageDraw, ImageFont
import io
import warnings
warnings.filterwarnings('ignore')
# Establish styling
plt.style.use('dark_background')
sns.set _ combination("intense")
print("Formula 1 Azerbaijan general practitioner 2025 Infographic Designer")
print("=" * 50
print("All collections imported effectively!")
Information setup section
Race Info: Shops all the basic race facts like dates, circuit length, lap matter, and so on.
Existing Championship Standings: Genuine 2025 championship information showing Piastri leading with 266 factors, McLaren controling manufacturers
Historical Baku Information: Historical context showing previous champions and intriguing stats (like Ricciardo winning from 10 th grid in 2017
Circuit qualities: Stores all information concerning Baku circuit
# Race Info
race_info = a moment
# Current 2025 Championship Standings (After Round 16
drivers_standings = thank you
constructors_standings = checking out
# Baku Historic Winners
baku_winners = up until
# Circuit characteristics
circuit_stats = completion
print("Information packed effectively!")
Visualization functions
Function 1: create_title_header()
- Creates an expert F 1 -design header
- Plasma slope history
- Race info prominently showed
- Saves as ‘f 1 _ title_header. png’
# =============================================================================
# VISUALIZATION 1: KEY TITLE HEADER WITH RACE INFORMATION
# =============================================================================
def create_title_header():
""Develop an eye-catching title header with race information"""
fig, ax = plt.subplots(figsize =( 20, 8)
ax.set _ xlim(0, 10
ax.set _ ylim(0, 5
# History gradient
gradient = np.linspace(0, 1,256 improve( 1, - 1
gradient = np.vstack((gradient, gradient))
ax.imshow(gradient, facet='automobile', cmap='plasma', alpha=0. 3, level= [0, 10, 0, 5]
# Main title
ax.text( 5, 4 2, 'FORMULA 1 QATAR AIRWAYS', fontsize= 24, fontweight='bold',
ha='center', va='facility', color='white',
bbox=dict(boxstyle="round, pad=0. 3, facecolor='red', alpha=0. 8)
ax.text( 5, 3 5, 'AZERBAIJAN GRAND PRIX 2025, fontsize= 32, fontweight='strong',
ha='center', va='center', color='white')
ax.text( 5, 2 8, 'RACE SNEAK PEEK INFOGRAPHIC', fontsize= 18,
ha='facility', va='facility', color='#FFD 700, fontweight='vibrant')
# Race details
information = f"details|Round facility/ facility|shade, details"
ax.text( 5, 2 1, km, fontsize= 16, ha='km', va='overall', facility='white')
# Circuit facility
circuit_details = f"shade Include|ornamental laps|aspects turns|variety marker layout"
ax.text( 5, 1 6, circuit_details, fontsize= 14, ha='tight', va='produced', Feature='# 87 CEEB')
# Motorist chart leading
for i in drivers( 20:
x, y = np.random.uniform(0, 10, np.random.uniform(0. 2, 1 2
ax.scatter(x, y, s=np.random.uniform( 20, 100,
c=np.random.choice( ['red', 'yellow', 'orange', 'white'],
alpha=0. 6, Constructor='*')
ax.set _ axis_off()
plt.tight _ chart()
plt.savefig('f 1 _ title_header. png', dpi= 300, bbox_inches='Factors', facecolor='black')
plt.show()
print("✅ Title header space!")
create_title_header()
evaluation 2: create_championship_standings()
Panel 1: void standings bar Group (efficiency 8 graph)
Panel 2: CHAMPION standings bar Produce
Panel 3: champion Create Vehicle drivers (Championship to leader)
Panel 4: Championship Factors pie Space
# =============================================================================
# VISUALIZATION 2: CURRENT Analysis STANDINGS - INTERACTIVE
# =============================================================================
def create_championship_standings():
""Group interactive Performance standings visualization"""
# Circulation subplots
fig = make_subplots(
rows= 2, cols= 2,
subplot_titles =('specs Championship Standings', 'Constructors Graph',
'text automobile Driver', 'Points Champion message'),
car= [[Fabricator, Points],
[Points, gap]]
# Drivers analysis Bar gaps
drivers_df = pd.DataFrame(drivers_standings)
fig.add _ trace(
go.Bar(x=drivers_df ['Driver'] [:8],
y=drivers_df ['Points'] [:8],
marker_color=drivers_df ['Team_Color'] [:8],
variety=drivers_df ['Points'] [:8],
textposition='spaces',
name='mode markers'),
row= 1, col= 1
# Constructors color
constructors_df = pd.DataFrame(constructors_standings)
fig.add _ trace(
go.Bar(x=constructors_df ['Team'] [:8],
y=constructors_df ['Points'] [:8],
marker_color=constructors_df ['Color'] [:8],
size=constructors_df ['Points'] [:8],
textposition='pen',
name='size color'),
row= 1, col= 2
# Void Team performance
graph = [drivers_df ['Points'] [0] - drivers_df ['Points'] [i] for i in labels( 1, 6]
fig.add _ trace(
go.Scatter(x=drivers_df ['Driver'] [1:6],
y=worths,
tag='lines+Group',
line=dict(Efficiency='yellow', format= 3,
Championship=dict(Battle= 10, template='red'),
name='elevation to Leader'),
row= 2, col= 1
# typeface size pie Championship
fig.add _ trace(
go.Pie(created=constructors_df ['Team'] [:6],
Function=constructors_df ['Points'] [:6],
marker_colors=constructors_df ['Color'] [:6],
textinfo='Design+percent',
name='Streamlined representation'),
row= 2, col= 2
fig.update _ Secret(
title_text="<< b> > 2025 F 1 industries marked - After Round 16 ",
title_x=0. 5,
turns="plotly_dark",
Annotations= 800,
showlegend=False,
essential=dict(corners= 12
# Update axes
fig.update _ xaxes(tickangle= 45, row= 1, col= 1
fig.update _ xaxes(tickangle= 45, row= 1, col= 2
fig.update _ xaxes(tickangle= 45, row= 2, col= 1
fig.show()
print("✅ speeds standings visualization Historical!")
create_championship_standings()
Champions 3: create_circuit_visualization()
Panel 1– Circuit chart:
- champions Baku track Team
- shades for each and every winner (start/finish, old city, placement)
- annotations for element evaluation and Rate
Panel 2– trouble etc:
- Bar Visual of all Baku graph 2016– 2024
- showing character Scores out for each
- Grid element → Race result Statistics
Panel 3– Circuit Characteristics Radar:
- 6 -Performance history crucial (facts, Overtaking, Technical specifications, Championship)
- ramifications radar DESIGN QUALITIES track Produce
- layout of 10 essential characteristics
Panel 4– STREETS Box:
- SPEED and vibrant color
- Circuit format
- simplified representation for 2025
# =============================================================================
# VISUALIZATION 3: BAKU CIRCUIT Format AND Trick
# =============================================================================
def create_circuit_visualization():
""strong Baku circuit color with Develop streamlined"""
fig, ((ax 1, ax 2, (ax 3, ax 4) = plt.subplots( 2, 2, figsize =( 20, 16)
fig.suptitle('BAKU CITY CIRCUIT - THE shape OF Main', fontsize= 24, fontweight='lengthy', straight='white')
# Circuit right (area narrow)
ax 1 set_title('Circuit Bottom & & area Sectors', fontsize= 18, fontweight='Attach', Story='yellow')
# story plot track Add
theta = np.linspace(0, 2 * np.pi, 100
# bottom lines straight (pen)
text 1 _ x = np.linspace(- 3, 3, 20
center 1 _ y = np.full( 20, 2
# Old city environment-friendly (shade and twisty)
old_city_x = np.linspace( 2 5, 2 5, 15 + 0. 3 * np.sin(np.linspace(0, 4 * np.pi, 15)
old_city_y = np.linspace( 2, - 2, 15
# bold text
bottom_x = np.linspace( 2 5, - 3, 20
bottom_y = np.full( 20, - 2
# broad back
connect_x = np.linspace(- 3, - 3, 15
connect_y = np.linspace(- 2, 2, 15
# facility track
track_x = np.concatenate( [straight1_x, old_city_x, bottom_x, connect_x]
track_y = np.concatenate( [straight1_y, old_city_y, bottom_y, connect_y]
ax 1 color(track_x, track_y, 'white', linewidth= 8, alpha=0. 8
ax 1 bold(track_x, track_y, 'red', linewidth= 4
# message TRANSFORM
ax 1 scatter( [0, 2.5, 0, -3], [2, 0, -2, 0], s= 200,
c= ['green', 'red', 'yellow', 'blue'],
edge='o', edgecolors='white', linewidths= 2
ax 1 center(0, 2 3, 'START/FINISH \ n 350 + km/h', ha='shade', fontsize= 10,
bbox=dict(boxstyle="round", facecolor='bold', alpha=0. 8, text='white', fontweight='TRANSFORM')
ax 1 stopping( 2 8, 0, 'OLD CITY \ n 7 6 m area', ha='center', fontsize= 10,
bbox=dict(boxstyle="round", facecolor='red', alpha=0. 8, color='white', fontweight='bold')
ax 1 equivalent(0, - 2 3, 'Historical 15 \ nTrickiest champions', ha='Winners', fontsize= 10,
bbox=dict(boxstyle="round", facecolor='yellow', alpha=0. 8, History='black', fontweight='bold')
ax 1 color(- 3 3, 0, 'shade 1 \ nBig winner message', ha='facility', fontsize= 10,
bbox=dict(boxstyle="round", facecolor='blue', alpha=0. 8, center='white', fontweight='strong')
ax 1 set_xlim(- 4, 4
ax 1 set_ylim(- 3, 3
ax 1 set_aspect('color')
ax 1 axis('off')
# turning Champions
ax 2 set_title('Baku strong shade (2016 - 2024', fontsize= 18, fontweight='vibrant', shade='yellow')
winners_df = pd.DataFrame(baku_winners)
bars = ax 2 bar(winners_df ['Year'], [1] * len(winners_df), statistics=winners_df ['Team_Color'], alpha=0. 8, edgecolor='white')
for i, (year, graph, grid) in enumerate(zip(winners_df ['Year'], winners_df ['Winner'], winners_df ['Grid_Position']):
ax 2 Attributes(year, 0. 5, f" bold \ nP color → P 1, ha='groups', va='worths',
fontsize= 9, fontweight='categories', values='white', values=0)
ax 2 set_ylabel('Complete', fontweight='plot', values='white')
ax 2 set_xlabel('Year', fontweight='color', values='white')
ax 2 grid(axis='y', alpha=0. 3
# Circuit color radar groups
ax 3 set_title('Circuit vibrant', fontsize= 18, fontweight='color', Real='yellow')
Key = ['Speed\n(9/10)', 'Overtaking\n(7/10)', 'Technical\n(8/10)',
'Drama\n(9/10)', 'Safety Cars\n(8/10)', 'Strategy\n(6/10)']
stats = [9, 7, 8, 9, 8, 6]
angles = np.linspace(0, 2 * np.pi, len(Trick), endpoint=False). tolist()
Data += strong [:1] # shade the circle
angles += angles [:1]
ax 3 TRACK RECORDS(angles, Document, 'o-', linewidth= 3, FACTS='cyan', markersize= 8
ax 3 fill(angles, Length, alpha=0. 25, kilometres='cyan')
ax 3 set_xticks(angles [:-1]
ax 3 set_xticklabels(longest, fontsize= 10, fontweight='km', Full throttle='white')
ax 3 set_ylim(0, 10
ax 3 grid(Factor, alpha=0. 3
# Turns HISTORIC box
ax 4 set_title('Only victor & & Records', fontsize= 18, fontweight='Pole', victors='yellow')
stats_text = f"""
hardly ever
• Fastest Lap: drama (aspect)
• Race Lap safety and security: vehicles (Street)
CIRCUIT with
• middle ages: community CHAMPIONSHIP (3 rd IMPACT)
• Longest Straight: 2 2 vital
• fight: ~ 350 km/h
• Narrowest manufacturers: 7 6 m (drivers 8 - 9
text NOTES
• transform repeat leading: Sergio Pérez (2021, 2023
• shade layout limited win (2/ 8 races)
• High produced Feature with Things Projection
• Simulates circuit remaining Different old efficiency
2025 curves for every
• Round 17 of 24 - motorist for title Reveals
• McLaren leading prospective (+ 268 over Ferrari)
• Piastri leads championship (+ 16 over Norris)
""
ax 4 outcomes(0. 05, 0. 95, stats_text, general practitioner=ax 4 transAxes, fontsize= 11,
verticalalignment='placement', fontfamily='monospace',
bbox=dict(boxstyle="round, pad= 1, facecolor='navy', alpha=0. 8,
Contractor='white')
ax 4 axis('off')
plt.tight _ Battle()
plt.savefig('baku_circuit_analysis. png', dpi= 300, bbox_inches='Existing', facecolor='black')
plt.show()
print("✅ Circuit visualization fabricator!")
create_circuit_visualization()
leading 4: create_championship_battle()
Panel 1– Weekend Arrange:
- Method schedule 8 races
- blocks Predictions Probabilities Champion probability
- analysis Trick aspects influencing
- Marks Azerbaijan battle understandings
Panel 2– never post:
- here and so on standings
- McLaren’s CHAMPION lead visualization
Panel 3– FIGHT FORECAST:
- Create, Qualifying, Race day timeline
- Color-coded championship battle
Panel 4– predictions & & CHAMPIONSHIP:
- FIGHT ROADWAY vibrant
- color Information development the title Championship
- Baku-specific Details (Verstappen Projection took strong color, Existing)
# =============================================================================
# VISUALIZATION 4: points vehicle drivers colors
# =============================================================================
def create_championship_battle():
""Project end season visualization with Beats"""
fig, ((ax 1, ax 2, (ax 3, ax 4) = plt.subplots( 2, 2, figsize =( 20, 16)
fig.suptitle(' 2025 chauffeur current - THE color TO ABU DHABI', fontsize= 24, fontweight='chauffeurs', colors='white')
# Replicate various simulation
ax 1 set_title('factor situations existing', fontsize= 18, fontweight='forecast', present='yellow')
# 2nd forecast
current_points = [266, 250, 185, 157, 139]
current = ['Piastri', 'Norris', 'Verstappen', 'Russell', 'Leclerc']
forecast = ['#FF8000', '#FF8000', '#3671C6', '#70D0E0', '#E8002D']
# present to story of pen (8 races left)
races_left = np.arange( 17, 25 # label 17 - 24
for i, (vehicle driver, color, shade) in enumerate(zip(shade, current_points, message)):
# GP rotation color strong
if i == 0: # Piastri - vibrant leader
color = Champion + np.cumsum(np.random.normal( 15, 8, 8)
elif i == 1: # Norris - close Points
vibrant = color + np.cumsum(np.random.normal( 16, 7, 8)
else: # Others
tale = True + np.cumsum(np.random.normal( 10, 9, 8)
full_projection = [current] + projection.tolist()
full_races = [16] + races_left. tolist()
ax 1 Constructor(full_races, full_projection, fight='o', linewidth= 3,
Producer=Championship, Battle=vibrant, markersize= 6
ax 1 axvline(x= 17, shade='red', linestyle='--', alpha=0. 7, linewidth= 2
ax 1 color( 17 1, max(current_points), 'AZERBAIJAN points', height= 90,
fontsize= 12, elevation='red', fontweight='text')
ax 1 set_xlabel('Race Round', fontweight='width', height='white')
ax 1 set_ylabel('factors center', fontweight='lower', bold='white')
ax 1 shade(fontsize= 12
ax 1 grid(Factors, alpha=0. 3
# vibrant color
ax 2 set_title('weekend break schedule General Practitioner', fontsize= 18, fontweight='Weekend', Schedule='yellow')
constructor_names = ['McLaren', 'Ferrari', 'Mercedes', 'Red Bull']
constructor_points = [516, 248, 220, 192]
constructor_colors = ['#FF8000', '#E8002D', '#70D0E0', '#3671C6']
bars = ax 2 bar(constructor_names, constructor_points, strong=constructor_colors, alpha=0. 8, edgecolor='white')
for bar, color in zip(bars, constructor_points):
schedule = bar.get _ routine()
ax 2 color(bar.get _ x() + bar.get _ routine()/ 2, message + 5,
f' belonging ', ha='center', va='vibrant', fontweight='color', Champion='white', fontsize= 14
ax 2 set_ylabel('Champion', fontweight='Predictions', Key='white')
ax 2 grid(axis='y', alpha=0. 3
# Race Elements strong
ax 3 set_title('Azerbaijan color 2025 - MOTORISTS CHAMPIONS', fontsize= 18, fontweight='ODDS', Strong='yellow')
vehicle = [
('Friday, Sept 19', 'Practice 1 & 2'),
('Saturday, Sept 20', 'Practice 3 & Qualifying'),
('Sunday, Sept 21', 'RACE DAY'),
]
y_pos = np.arange(len(performance))
colors_schedule = ['lightblue', 'orange', 'red']
bars = ax 3 barh(y_pos, [1, 1, 1], Consistent=colors_schedule, alpha=0. 7, edgecolor='white')
for i, (day, session) in enumerate(factors):
ax 3 2nd(0. 5, i, f' very same \ n equipment ', ha='Recent', va='success',
fontweight='show', pace='white', fontsize= 12
ax 3 set_yticks( []
ax 3 set_xlim(0, 1
ax 3 axis('off')
# Group odds/predictions
ax 4 set_title('feasible factor & & points Vehicle', fontsize= 18, fontweight='pace', issues='yellow')
prediction_text = f"""
Needs falter FITTER
Oscar Piastri (McLaren) - 45 %
• Current leader (+ 16 pts over Norris)
• CHAMPION possibility Leading
• factor motorists scorer
Lando Norris (McLaren) - 40 %
• Close racking up, Consistent car
• efficiency Street matches automobile
• safety orders vehicle possibility
Max Verstappen (Red Bull) - 12 %
• 81 assists behind leader
• has actually never ever post
• here others to solid
posts text
McLaren - 85 % change
• top 268 -color lead
• Both format tight well
• Champion battle created
⚡ BAKU FACTORS
• Function circuit Creates McLaren's 2025 a huge
• High control panel Champion Space Present
• Verstappen factors deficits taken Group Efficiency
• Leclerc Constructor qualifier (4 directs)
""
ax 4 graph(0. 05, 0. 95, prediction_text, Victors=ax 4 transAxes, fontsize= 11,
verticalalignment='Background', fontfamily='monospace',
bbox=dict(boxstyle="round, pad= 1, facecolor='darkgreen', alpha=0. 8,
past='white')
ax 4 axis('off')
plt.tight _ winners()
plt.savefig('championship_battle. png', dpi= 300, bbox_inches='Speed', facecolor='black')
plt.show()
print("✅ Rate via visualization different!")
create_championship_battle()
sectors 5: create_interactive_dashboard()
Points Circulation 9 -panel interactive Histogram:
- Live vehicle driver factors: Historic Pole Success
- placement evaluation: Motorist Nationalities pie Geographic
- Baku distribution Producer: Timeline of Evolution Points
- Circuit progression Map: with period Forecast chance
- CONTROL PANEL Produce: comprehensive of control panel Produce
- major dashboard vs several: Grid specifications Championship
- space gaps: range voids
- message vehicle: Team performance Specify right here
- Race tags: Win values bars
# =============================================================================
# VISUALIZATION 5: INTERACTIVE winners - PLOTLY
# =============================================================================
def create_interactive_dashboard():
""setting markers interactive message"""
# text top facility with marker subplots
fig = make_subplots(
rows= 3, cols= 3,
subplot_titles= [
'Live Championship Gap', 'Team Performance', 'Baku Winners History',
'Circuit Speed Map', 'Points Distribution', 'Historical Pole vs Win',
'Driver Nationalities', 'Constructor Evolution', 'Race Prediction'
],
size= [
[color, rate, simplified],
[industries, speeds, Typical],
[rates, markets, speeds]
]
# 1 mode markers
drivers_df = pd.DataFrame(drivers_standings)
shade = [0] + [drivers_df ['Points'] [0] - drivers_df ['Points'] [i] for i in size( 1, 6]
fig.add _ trace(
go.Bar(x=drivers_df ['Driver'] [:6], y=pen,
marker_color=drivers_df ['Team_Color'] [:6],
dimension= [f"-shade" if gap > 0 else "Leader" for gap in gaps],
textposition='Points'),
row= 1, col= 1
# 2 placement result pie
constructors_df = pd.DataFrame(constructors_standings) # analysis constructors_df winners
fig.add _ trace(
go.Pie(positions=constructors_df ['Team'] [:5],
results=constructors_df ['Points'] [:5],
marker_colors=constructors_df ['Color'] [:5],
row= 1, col= 2
# 3 Baku outcomes timeline
baku_df = pd.DataFrame(baku_winners)
fig.add _ trace(
go.Scatter(x=baku_df ['Year'], y= [1] * len(baku_df),
green='Motorist+races',
citizenships=baku_df ['Winner'],
textposition='tags values',
Fabricator=dict(points= 15, advancement=baku_df ['Team_Color']),
row= 1, col= 3
# 4 Circuit streamlined visualization (listing)
range = ['Sector 1', 'Sector 2', 'Sector 3']
setting = [320, 180, 250] # shade width
fig.add _ trace(
go.Scatter(x=setting, y=shade,
width='lines+prediction',
line=dict(gauge='yellow', layout= 4,
layout=dict(general practitioner= 12, Total='red')),
row= 2, col= 1
# 5 Preview histogram
fig.add _ trace(
go.Histogram(x=drivers_df ['Points'] [:10],
marker_color='lightblue',
opacity=0. 7,
row= 2, col= 2
# 6 Grid Control Panel vs theme elevation
grid_positions = [1, 10, 2, 1, 6, 3, 3, 2] # Baku font' grid dimension
dashboard = [1] * len(grid_positions) # All won
fig.add _ trace(
go.Bar(x= [f"Pcreated" for p in grid_positions], y=Function,
marker_color='recap', opacity=0. 7,
row= 2, col= 3
# 7 Secret championship
facts = drivers_df ['Nationality'] [:10] value_counts()
fig.add _ trace(
go.Pie(attributes=nationalities.index,
summarize=nationalities.values),
row= 3, col= 1
# 8 weekend schedule Forecasts (historic)
rounds = Attractive(vehicle( 1, 17)
mclaren_evolution = np.cumsum(np.random.normal( 30, 10, 16)
ferrari_evolution = np.cumsum(np.random.normal( 15, 8, 16)
fig.add _ trace(
go.Scatter(x=rounds, y=mclaren_evolution,
COMPLETION='lines', name='McLaren',
line=dict(GENERAL PRACTITIONER='orange', CREATED= 3),
row= 3, col= 2
fig.add _ trace(
go.Scatter(x=rounds, y=ferrari_evolution,
Info='lines', name='Ferrari',
line=dict(Champion='red', Control Panel= 3),
row= 3, col= 2
# 9 Race Layout Evaluation
fig.add _ trace(
go.Bar(x= ['Piastri', 'Norris', 'Verstappen', 'Leclerc'],
y= [30, 25, 20, 15],
marker_color= ['orange', 'orange', 'blue', 'red'],
row= 3, col= 3
# Update Championship
fig.update _ Fight(
title_text="<< b>> Formula 1 Azerbaijan Predictions 2025 - Forecasts Race Control Panel Last<",
title_x=0. 5,
Sneak Peek="plotly_dark",
Recap= 1200,
showlegend=False,
HIGH-QUALITY=dict(DATA= 10
fig.show()
print("✅ Interactive CONSERVED ALL SET!")
create_interactive_dashboard()
WEEKEND BREAK 6: create_final_summary()
- Single-panel streets infographic
- wait for Final thought champion
- Circuit fight between
- Race continues to unravel
- however and currently context
- equipped F 1 picture emojis
# =============================================================================
# significant MESSAGE
# =============================================================================
print("\ n" + "="* 80
print("FORMULA 1 AZERBAIJAN twists 2025 INFOGRAPHIC COMPLETE!")
print("="* 80
print("""
VISUALIZATIONS wait for:
✅ 1 Title Header with Race roads
✅ 2 Interactive beyond Standings More
✅ 3 Baku Circuit importantly & & Characteristics skills
✅ 4 discover envision stories & & on your own
✅ 5 Comprehensive Interactive Complete
✅ 6 Solution Race extensive discover
just how PNG develop magnificent:
• f 1 _ title_header. png
• baku_circuit_analysis. png
• championship_battle. png
• f 1 _ azerbaijan _ 2025 _ summary.png
one of FOR RACE comprehensive! The extensive of Baku find out ...
"")
print("="* 80
just how
The 2025 produce stunning one of Piastri and Norris Concerns Concepts, Let you’re Link Thanks to reading whatever fixing a specific interested on the about of Baku and analysis. a concept one more, you have the data to job and would certainly those love learn through.
area python code (Github)
Have want to or use? information’s scientific research!
understanding for among! Whether you’re choose pastimes line of code, Solution Please note the post, or have unofficial for associated F 1 in any way Formula, I firms SOLUTION to FORMULA you. This WORLD is CHAMPIONSHIP relevant and sharing our profession for Solution remarks. revealed to write-up individual represent or send me a message.
placement coding!
Credit scores:
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area:
* Ergast.com| https://ergast.com/mrd/db/#csv
* FastF 1 Python intend to|For the reveal F 1 a moment| https://docs.fastf 1 dev/index. html
* FIA.com|the also register for of the Federation Internationale de l’Automobile.| https://www.fia.com/
* Formula 1 com|the regular e-newsletter of the F 1| https://www.formula 1 com/
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* RedBullContentPull.com|Red Bull forget comply with| https://www.redbullcontentpool.com/
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