Andrew McCalip

Andrew McCalip

Andrew McCalip

@andrewmccalip

12 Apr 2025

Do with this what you will... import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from matplotlib.ticker import FuncFormatter, MultipleLocator # Data for cars and trucks (2024-2025 models) vehicles = { "Mitsubishi Mirage": (16695, 2084), "Nissan Versa": (16130, 2598), "Toyota Corolla": (22050, 2955), "Honda Civic": (23950, 2935), "Toyota Camry": (28915, 3310), "Honda Accord": (29045, 3239), "Toyota RAV4": (30025, 3370), "Jeep Wrangler": (31895, 4012), "Ford Maverick": (25515, 3563), "Ford F-150": (39060, 4391), "Ram 1500": (41415, 4765), "Chevrolet Silverado 1500": (37845, 4410), "Tesla Model 3": (40630, 3862), "Tesla Model S": (81630, 4560), "Rolls-Royce Phantom": (503000, 5644), "Mercedes-Benz S-Class": (118450, 4740), "BMW 330i": (45495, 3536), "Range Rover SE": (109025, 5240), "Cadillac Escalade": (86890, 5823), "Porsche 911 Carrera": (116050, 3354), "Lamborghini Revuelto": (608358, 4188), "Ferrari SF90 Stradale": (520000, 3461), "Chevrolet Corvette Stingray": (69995, 3366), "Ford Mustang GT": (42500, 3832), "Lucid Air Pure": (78900, 4564), "Nissan Leaf": (29255, 3509), "Toyota GR86": (30395, 2811), "Rolls-Royce Spectre": (397750, 6537), "Ford F-150 Lightning": (49995, 6361), } # Calculate price per pound for vehicles vehicle_prices_per_pound = {k: v[0]/v[1] for k, v in vehicles.items()} # Data for cheeses (price per pound in USD) cheeses = { "Cheddar": 5.62, "Mozzarella": 5.58, "Swiss": 7.00, "Brie": 14.99, "Gouda": 11.50, "Parmigiano Reggiano": 20.00, "Roquefort Blue": 26.00, "Goat Cheese": 12.00, "Époisses": 30.00, "Manchego": 18.50, "Gruyère": 22.00, "Camembert": 16.75, "Stilton": 28.50, "Feta": 9.99, "Pecorino Romano": 17.25, "Burrata": 24.00, "Comté": 25.50, "Halloumi": 15.75, "Taleggio": 19.50, "Gorgonzola": 21.00, "Emmental": 13.75, "Ricotta": 8.50, "Mascarpone": 10.25, "Provolone": 12.50, "Asiago": 16.00, "Fontina": 18.75, "Havarti": 14.25, "Morbier": 23.50, "Raclette": 19.75, "Mimolette": 27.50, "Reblochon": 24.75, "Munster": 17.50, "Pont-l'Évêque": 29.00, "Cabrales": 22.50, "Ossau-Iraty": 26.75, "Queso Fresco": 7.99, "Cotija": 11.25, "Paneer": 8.75, "Wensleydale": 20.50, "Roquefort": 31.00, } # Prepare data for plotting vehicle_df = pd.DataFrame({ 'Item': list(vehicle_prices_per_pound.keys()), 'Price': list(vehicle_prices_per_pound.values()), 'Category': ['Vehicle'] * len(vehicle_prices_per_pound), }) cheese_df = pd.DataFrame({ 'Item': list(cheeses.keys()), 'Price': list(cheeses.values()), 'Category': ['Cheese'] * len(cheeses), }) # Combine data combined_df = pd.concat([vehicle_df, cheese_df]) # Filter data to show only items under $400 per pound filtered_df = combined_df[combined_df['Price'] <= 400] # Create dollar formatter function def dollar_formatter(x, pos): return f'${int(x):,}' if x >= 1 else f'${x:.2f}' # Create the bar chart in its own window plt.figure(figsize=(16, 20)) # Made slightly wider to accommodate legend # Sort data by price descending to interleave vehicles and cheeses sorted_df = filtered_df.sort_values('Price', ascending=False) bar_plot = sns.barplot(x='Price', y='Item', hue='Category', data=sorted_df, palette=['blue', 'orange']) plt.title('Price per Pound: Vehicles vs Cheeses (Interleaved, Descending, Max $400)', fontsize=16) plt.xlabel('Price per Pound (USD)', fontsize=18, labelpad=25) plt.ylabel('Items', fontsize=12) plt.xscale('log') # Log scale for better visibility of wide range plt.grid(True, which='both', linestyle='--', linewidth=0.5, axis='x') plt.xlim(5, 400) # Set axis limits plt.legend(fontsize=20, title_fontsize=20, bbox_to_anchor=(1.05, 1), loc='upper left') # Set the dollar formatter plt.gca().xaxis.set_major_formatter(FuncFormatter(dollar_formatter)) # Set dollar points starting from $5 dollar_points = [5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300] plt.gca().xaxis.set_ticks(dollar_points, minor=True) plt.gca().xaxis.set_ticklabels(['$' str(x) for x in dollar_points], minor=True, fontsize=8) plt.tight_layout() plt.show()

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