Update lib.py

[gitctrlx]

Replacing pd.DataFrame with Generator[pd.DataFrame, None, None] The reason for replacing pd.DataFrame with Generator[pd.DataFrame, None, None] is to better reflect the actual output type of the random_ohlc_data function. Here are the specific reasons and benefits:

Reasons: Accuracy of Output Type: The original code declared that the function returns a pd.DataFrame, but in reality, the function is a generator that yields multiple pd.DataFrame objects. Using Generator more accurately describes the function's behavior. Clarity of Type Hinting: Using Generator allows the code readers and users to more easily understand that the function returns a generator rather than a single DataFrame. This helps prevent potential misunderstandings and misuse.

Benefits: Performance Improvement: Generators can generate data on-demand rather than generating all data at once, saving memory and improving performance, especially when dealing with large datasets. Lazy Evaluation: Generators allow for lazy evaluation, meaning data frames are only generated when needed. This can improve the efficiency and responsiveness of the code. Better Code Maintainability: Explicitly using generators makes the intent of the code clearer, enhancing readability and maintainability, making it easier for other developers to understand and maintain the code.