The fact that particular methods succeeded in solving a problem may not lead to the same outcome on a different scale. When distances change, shoes need to change too. In machine learning, data, and data processing are crucial in ensuring the model’s success, and feature engineering is part of that process. When the data is small, the classical Pandas library can easily handle any processing task on the CPU. However, Pandas can be too slow in processing big data.
