Why i stopped using jupyter notebooks

jupyter notebooks have been a popular tool among data scientists, researchers, and developers for many years due to their interactive and easy-to-use interface. however, there are some reasons why some users may choose to stop using jupyter notebooks and opt for other tools or workflows. here are a few common reasons:

1. **version control**: jupyter notebooks are not ideal for version control as the notebook files can become complex and difficult to track changes over time. using traditional code files (such as python scripts) in conjunction with version control systems like git can provide a more robust solution for tracking changes and collaborating with others.

2. **reproducibility**: while jupyter notebooks allow for an interactive and exploratory workflow, they can sometimes make it challenging to ensure reproducibility of results. running cells out of order or modifying code in an ad-hoc manner can lead to discrepancies in results. writing scripts in a traditional ide or text editor can promote better coding practices and reproducibility.

3. **performance**: jupyter notebooks are not optimized for performance-critical tasks or large-scale data processing. running complex computations or handling big datasets in a notebook environment may lead to performance issues. utilizing dedicated tools or environments designed for performance, such as ides or specialized libraries, can be more efficient in such cases.

4. **code organization**: while jupyter notebooks allow for mixing code, text, and visualizations in a single document, this flexibility can sometimes lead to messy and unorganized notebooks. separating code into modular functions and classes in traditional python scripts can promote better code organization and reusability.

5. **dependency management**: managing dependencies and virtual environments within jupyter notebooks can be cumbersome. tools like `pip` and `conda` are more commonly used in a traditional python environment for managing dependenci ...

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