Optimizing Python Code for Green Computing

{
  "title": "Optimizing Python Code for Green Computing",
  "description": "Techniques for writing energy-efficient Python code to support sustainable computing practices, focusing on algorithm optimization and resource management.",
  "category": "Python Cursor Rules",
  "rules": [
    {
      "id": "efficient_data_structures",
      "description": "Use appropriate data structures to minimize computational complexity and energy consumption.",
      "recommendation": "Choose data structures that offer optimal performance for your specific use case. For example, use sets for membership tests instead of lists to achieve O(1) time complexity.",
      "example": {
        "bad": "numbers = [1, 2, 3, 4, 5]\nif 3 in numbers:\n    print('Found')",
        "good": "numbers = {1, 2, 3, 4, 5}\nif 3 in numbers:\n    print('Found')"
      }
    },
    {
      "id": "algorithm_optimization",
      "description": "Implement efficient algorithms to reduce processing time and energy usage.",
      "recommendation": "Opt for algorithms with lower time complexity. For instance, use binary search instead of linear search for sorted data to achieve O(log n) time complexity.",
      "example": {
        "bad": "def linear_search(arr, x):\n    for i in range(len(arr)):\n        if arr[i] == x:\n            return i\n    return -1",
        "good": "def binary_search(arr, x):\n    low, high = 0, len(arr) - 1\n    while low <= high:\n        mid = (low + high) // 2\n        if arr[mid] < x:\n            low = mid + 1\n        elif arr[mid] > x:\n            high = mid - 1\n        else:\n            return mid\n    return -1"
      }
    },
    {
      "id": "lazy_evaluation",
      "description": "Utilize lazy evaluation to conserve memory and processing power.",
      "recommendation": "Use generators to handle large datasets efficiently, as they yield items one at a time and only when required.",
      "example": {
        "bad": "squares = [x * x for x in range(1000000)]",
        "good": "squares = (x * x for x in range(1000000))"
      }
    },
    {
      "id": "built_in_functions",
      "description": "Leverage Python's built-in functions and libraries for optimized performance.",
      "recommendation": "Utilize built-in functions like `sum()`, `map()`, and `filter()` which are implemented in optimized C code, making them faster than manual iterations.",
      "example": {
        "bad": "total = 0\nfor num in [1, 2, 3, 4]:\n    total += num",
        "good": "total = sum([1, 2, 3, 4])"
      }
    },
    {
      "id": "asynchronous_programming",
      "description": "Implement asynchronous programming to improve efficiency in I/O-bound tasks.",
      "recommendation": "Use the `asyncio` library to perform non-blocking I/O operations efficiently, reducing idle CPU cycles.",
      "example": {
        "bad": "import time\n\ndef fetch_data():\n    print('Fetching data...')\n    time.sleep(2)\n    print('Data fetched!')\n\nfetch_data()",
        "good": "import asyncio\n\nasync def fetch_data():\n    print('Fetching data...')\n    await asyncio.sleep(2)\n    print('Data fetched!')\n\nasyncio.run(fetch_data())"
      }
    },
    {
      "id": "profiling_code",
      "description": "Profile and analyze code performance to identify and optimize energy-intensive segments.",
      "recommendation": "Use profiling tools to measure execution time and memory usage, allowing you to pinpoint and optimize bottlenecks in your code.",
      "example": {
        "bad": "No profiling implemented.",
        "good": "import cProfile\n\ndef my_function():\n    # Your code here\n\ncProfile.run('my_function()')"
      }
    }
  ]
}