Get Diesel Prices in Python

from oilpriceapi import OilPriceAPI

client = OilPriceAPI(api_key="YOUR_API_KEY")

# Get California diesel price
ca_diesel = client.diesel_prices.get_regional(state="CA")

print(f"California diesel: $" + str(ca_diesel.price) + "/gal")
print(f"Last updated: " + str(ca_diesel.updated_at))

# Output:
# California diesel: $4.86/gal
# Last updated: 2025-12-15T00:00:00Z

from oilpriceapi import OilPriceAPI

client = OilPriceAPI(api_key="YOUR_API_KEY")

# Get all state prices
states = ["CA", "TX", "FL", "NY", "PA"]
prices = []

for state in states:
    data = client.diesel_prices.get_regional(state=state)
    prices.append({
        "state": state,
        "price": data.price
    })

# Sort by price
prices.sort(key=lambda x: x["price"])

# Show cheapest 5 states
for p in prices[:5]:
    print(f"{p['state']}: $" + str(p['price']) + "/gal")

from oilpriceapi import OilPriceAPI

client = OilPriceAPI(api_key="YOUR_API_KEY")

# Get cheapest diesel in Los Angeles
stations = client.diesel_prices.get_stations(
    lat=34.0522,
    lng=-118.2437,
    radius=8047  # 5 miles in meters
)

# Sort by price
stations.sort(key=lambda s: s.diesel_price)

# Show top 10
for i, station in enumerate(stations[:10], 1):
    print(f"#{i}: " + station.name)
    print(f"   $" + str(station.diesel_price) + "/gal")
    print(f"   " + station.address)
    print()

import pandas as pd
from oilpriceapi import OilPriceAPI

client = OilPriceAPI(api_key="YOUR_API_KEY")

# Get prices for multiple states
states = ["CA", "TX", "FL", "NY", "PA", "IL", "OH", "NC", "GA", "MI"]
data = []

for state in states:
    price_data = client.diesel_prices.get_regional(state=state)
    data.append({
        "state": state,
        "price": price_data.price,
        "updated": price_data.updated_at
    })

# Create DataFrame
df = pd.DataFrame(data)

# Calculate statistics
print(f"Average price: $" + str(df['price'].mean()))
print(f"Cheapest state: " + df.loc[df['price'].idxmin()]['state'])
print(f"Most expensive: " + df.loc[df['price'].idxmax()]['state'])

# Export to CSV
df.to_csv("diesel_prices.csv", index=False)