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.
Unleash the power of Python for your data analysis projects with For Dummies!Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization.
The fast and easy way to learn Python programming and statistics. Python is a general-purpose programming language created in the late 1980s - and named after Monty Python - that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Scienc ...
The best applications use data and present it in a meaningful, easy-to-understand way. Packed with sample code and tutorials, this book will walk you through installing common charts, graphics, and utility libraries for the Python programming language.
Harness the power of Python objects and data structures to implement algorithms for analyzing your data and efficiently extracting information. This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You'll quickly start writing programs, building websites, and working w ...
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