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coffeetrove

Python SDK for CoffeeTrove.com -- explore 440,000+ cafes, coffee origins, brewing methods, beans, and specialty coffee data worldwide.

Install

pip install coffeetrove

For pandas DataFrame support:

pip install coffeetrove[pandas]

Quick Start

import coffeetrove

# Find cafes in any city
paris_cafes = coffeetrove.cafes("Paris", limit=5)
print(paris_cafes[0]["name"])

# Search cafes by name
results = coffeetrove.search_cafes("Blue Bottle", city="Tokyo")

# Coffee origins with flavor profiles
origins = coffeetrove.origins()
ethiopia = [o for o in origins if o["slug"] == "ethiopia"][0]
print(ethiopia["flavor_profile"])

# Brewing methods with step-by-step guides
methods = coffeetrove.methods()

# Coffee drinks with nutritional info
drinks = coffeetrove.drinks()

# Bean varieties
beans = coffeetrove.beans()

# Equipment and gear
gear = coffeetrove.equipment()

Using the Client

For more control, use CoffeeTroveClient directly:

from coffeetrove import CoffeeTroveClient

with CoffeeTroveClient() as client:
    # Search cafes with multiple filters
    cafes = client.cafes(city="Melbourne", limit=20)

    # Get cafes by country
    japan_cafes = client.cafes(country="japan", limit=50)

    # Count cafes
    total = client.count_cafes()
    print(f"Total cafes: {total:,}")  # Total cafes: 440,000+

    # Count by country
    france = client.count_cafes(country="france")
    print(f"Cafes in France: {france:,}")

    # Get all coffee comparisons
    comparisons = client.comparisons()

Pandas DataFrames

import coffeetrove

# Cafes as DataFrame
df = coffeetrove.cafes("London", limit=100, as_dataframe=True)
print(df[["name", "city", "score"]].describe())

# Origins as DataFrame
origins_df = coffeetrove.origins(as_dataframe=True)

Data Available

Data Count Description
Cafes 440,000+ Name, address, coordinates, score, hours, chain type
Origins 15+ Growing regions with altitude, flavor, harvest data
Brewing Methods 15+ Step-by-step guides with grind, temp, ratio
Drinks 17+ Ingredients, caffeine, calories
Beans 23+ Species, origin, flavor notes, processing
Equipment 15+ Category, price range, difficulty
Comparisons 17+ Side-by-side drink and method analysis

Golden Drop Score

Every cafe has a score (0-100) based on data completeness, reviews, and chain type. Independent cafes receive a +10 bonus. Learn more.

Links

License

MIT

Metadata

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