In PyCharm, I find several components and libraries to be the most valuable. The support that Jupyter Notebook offers is essential, as we work through Jupyter regularly. Scientific libraries such as NumPy, Pandas, Matplotlib, and Plotly are integral to our work. Machine learning libraries including scikit-learn, PyTorch, and TensorFlow are used extensively. Hugging Face integration is particularly valuable because it is easily findable, the documentation is comprehensive, and it can be directly integrated with the IDEs we work with. The intelligent code editor in PyCharm definitely helps me manage code quality and efficiency in my projects. When using these libraries, it makes parallelization of data very efficient, allowing me to use multi-thread programming architecture. The code can work for multiple datasets rather than one at a time. With native Python code, a machine learning deployment taking 45 to 50 minutes to calculate can be efficiently reduced to a minute or half a second using these libraries.
