Pandas

PyPI security breaches forced strict corporate policies, fragmented package management (pip/conda), and critical libraries like NumPy and Pandas struggle with GPU demands, creating incompatible forks and version conflicts.

Python loops and standard lists cannot compete with NumPy/Polars in data-heavy applications. Developers must manually optimize or migrate to specialized libraries for acceptable performance on large datasets.

Python's object-oriented paradigm doesn't integrate well with numeric and data manipulation libraries like NumPy and Pandas, creating an awkward development experience when combining OOP with these tools.