Название: Fast Python for Data Science (MEAP) V3
Автор: Tiago Rodriques Antao
Издательство: Manning Publications
Год: 2021
Страниц: 127
Язык: английский
Формат: pdf (true)
Размер: 11.8 MB

Master these effective techniques to reduce costs and run times, handle huge datasets, and implement complex machine learning applications efficiently in Python. Fast Python for Data Science is your guide to optimizing every part of your Python-based data analysis process, from the pure Python code you write to managing the resources of modern hardware and GPUs. You'll learn to rewrite inefficient data structures, improve underperforming code with multithreading, and simplify your datasets without sacrificing accuracy.

Fast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science. Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.

Python is widely used in modern data process applications. As with any language, it has its advantages and its drawbacks. There are great reasons to use Python but here we are more concerned with dealing with Python’s limitations for high performance data processing.

Give Python’s limitations with regards to performance, optimizing our Python code sometimes not be enough. In those cases we will end up rewriting that part in a lower-level language — or at the very least annotate our code so that it gets rewritten in a lower-level language by some code conversion tool. The part of the code that we will need to rewrite is normally very small, so its not the case that we are ditching Python. When we do this last stage optimization probably more that 90% of the code will still be Python. This is what many core scientific libraries like NumPy, scikit-learn or SciPy actually do: their most computationally demanding parts are usually implemented in C or Fortran.