MARCO MAFFEZZOLI

Free online books:

• Think Python - How to Think Like a Computer Scientist, 2nd Edition. "an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression."
• Scipy Lecture Notes. Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques.
• 20 Python Libraries You Aren’t Using (But Should). "This report helps you explore some of the lesser known Python libraries and tools, including third-party modules and several extremely useful tools in the standard library that deserve more attention."
• How to Make Mistakes in Python.
• Functional Programming in Python. "This paper examines the functional aspects of the language and points out which options work well and which ones you should generally decline."
• IPython Cookbook, Second Edition (2018). Contains over 100 hands-on recipes on high-performance numerical computing and data science in the Jupyter Notebook. Consider in particular the following recipes:
  • Understanding the internals of NumPy to avoid unnecessary array copying.
  • Using stride tricks with NumPy.
  • Accelerating pure Python code with Numba and just-in-time compilation.
  • Accelerating array computations with Numexpr.
  • Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA.
  • Distributing Python code across multiple cores with IPython.

On-line courses:

• Computational Statistics and Statistical Computing (2018) in Python.

Useful web pages:

• Python speed.Coding guidelines for applications that demand peak performance (in terms of memory utilization, speed, or scalability).
• The Hitchhiker’s Guide to Python. "This handcrafted guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a daily basis."
• Time Series Analysis in Python: An Introduction.
• Einstein Summation in Numpy.