The role of data scientists and analysts in data-driven decision-making cannot be overstated. Flexibility is a must when it comes to handling large datasets. This abstract introduces a Python library that optimizes critical stages of the data science workflow. It simplifies data loading and preprocessing, facilitates exploratory data analysis (EDA), and streamlines feature engineering, machine learning modeling, and output visualization.

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