{"library":"rusket","title":"Rusket","description":"Rusket is an ultra-fast Python library designed for building recommender engines (collaborative filtering) and performing market basket analysis (association rules). It leverages Rust for its core computational logic, offering significant performance advantages, especially with large datasets. The current version is 0.1.90, and it is under active development with a focus on speed and efficiency.","language":"python","status":"active","last_verified":"Fri Apr 17","install":{"commands":["pip install rusket"],"cli":null},"imports":["from rusket import Recommender","from rusket import MarketBasketAnalyzer"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pandas as pd\nfrom rusket import MarketBasketAnalyzer\n\n# Sample transactional data for Market Basket Analysis\ndata = {\n    'transaction_id': [1, 1, 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n    'item_id': ['Apple', 'Banana', 'Orange', 'Apple', 'Grape', 'Banana', 'Orange', 'Apple', 'Banana', 'Grape', 'Milk', 'Bread', 'Eggs', 'Milk', 'Cheese', 'Butter']\n}\ndf = pd.DataFrame(data)\n\n# Initialize and fit the Market Basket Analyzer\n# Lower min_support/min_confidence for small sample data to ensure rules are found\nmba = MarketBasketAnalyzer(min_support=0.01, min_confidence=0.01)\n\nmba.fit(df, transaction_col='transaction_id', item_col='item_id')\n\n# Get association rules\nrules = mba.get_rules()\nprint(\"Generated Association Rules (first 5):\")\nif not rules.empty:\n    print(rules.head())\nelse:\n    print(\"No rules found. Try adjusting min_support or min_confidence.\")","lang":"python","description":"This example demonstrates how to use `MarketBasketAnalyzer` to find association rules from a pandas DataFrame of transactional data. It initializes the analyzer, fits it to the data using specified transaction and item columns, and then retrieves the generated rules.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}