Microsoft Excel has been my most-used tool for calculation and data analysis for many years thanks to its various benefits, including user-friendly interface, fast formula-based computations and quick data visualization. However, it suffers from several limitations. The first one is data size constraints: the maximum number of rows which Excel can reach is 1 million, and it struggles with large datasets. The more data or format you input, the slower it processes. Secondly, it has limited statistical functions and visualization capabilities, thus limiting analysis depth.
These downsizes are overcome by Python. Its versatility, scalability and advanced analytical capabilities make it the preferred choice for deeper analysis. Specifically, it handles massive datasets with ease, and there is no inherent data size limitations (scalability). Popular libraries which handles large data include Pandas or Numpy, which can process more than 100 million rows in a short amount of time and so greatly increase productivity. Furthermore, Python provides advanced analytics such as machine learning, deep learning and statistical modeling, allowing users to discover hidden patterns, gain deeper insights and make forecast easier and more accrurate.
Those qualities make Python superiority in data analysis and the ideal choice for data professionals seeking deeper insights.