## My problem with Python Like all programming languages, Python has its shortcomings, which I can easily mention. These flaws become obvious when I compare it to other programming languages I use: - It’s slow (not compiled) - Has a huge dependency on C for heavy tasks - Possible to create desktop applications, but inappropriate - Has no built-in linear algebra functionality - No integrated dataframe management functionality - No integrated statistics or machine learning functionality - Object-oriented doesn’t integrate well with any of the above functionalities (Numpy and Pandas for instance feel weird to use personally)
Related Pain Points6件
Python performance limitations due to lack of compilation
6Python is slow because it is not compiled, making it unsuitable for performance-critical applications compared to compiled languages.
Heavy dependency on C for computationally intensive tasks
5Python has a significant dependency on C implementations for heavy computational tasks, creating a gap between Python's ease-of-use and the complexity of leveraging performance-critical operations.
Missing built-in linear algebra functionality
4Python lacks built-in linear algebra functionality, requiring developers to rely on external libraries like NumPy for mathematical operations.
Missing built-in statistics and machine learning functionality
4Python lacks integrated statistics and machine learning functionality in the standard library, requiring external library dependencies for these critical data science tasks.
Object-oriented programming integration issues with numeric/data libraries
4Python'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.
Python unsuitable for desktop application development
4While Python can create desktop applications, it is inappropriate for this use case compared to languages designed specifically for desktop development.