- Short
- Short guide for complete beginners (with programming background)
- Code like a Pythonist
- Summary: Python cheat sheet by Mark Graph
- Long
- Python for Everybody, online course (no previous programming experience required)
- Basic Tutorial Python 3
- Official Python 3 documentation
- Introduction to Python for Econometrics, Statistics and Numerical Analysis by Kevin Sheppard (covers some pretty advanced topics)
- Book: Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code
- Guide Numpy-Style Docstrings related to this topic, we can also mentioned (PEP 484) (Pandas uses the same style as Numpy for the docstrings, it is the most common one in scientific Python)
- Style guide: PEP8 (Pycharm follows this convention by default)
- Naming convention
- Projects and packages
- Structuring your project: 1 and 2
- 5 simple rules for building great Python packages
- Open sourcing a Python project the right way
- A tour on Python Packaging, self-contained guide on Python Packaging
- Summary: The Best of the Best Practices (BOBP) Guide for Python
- How to loop like a Pythonist
- How to think like a Pythonist
- Namespaces and variable scopes
- A collection of not-so-obvious Python stuff you should know!
- Magic methods, function names in between two double underscores, e.g.
__str__. - What are the “best practices” for using import in a module? And those links (A and B) for those who want further details
- setup.py vs. requirements.txt
- Modules and imports (pretty advanced content)
- Static, class, abstract methods
- Exceptions
- Decorators (closures, scoping, etc.) (According to a comment: Reading this article is like eating a mushroom by Mario.)
- All the above about decorators is wrong (from the author of the wrapt module)
- Descriptor HowTo Guide, reference guide on Python's Descriptor Protocol
- Performance tuning
- Time complexity of various operations in CPython
- The magic of timeit
- Timing & profiling
- Sphinx: a Python documentation generator
- MyST: allows you to write Sphinx documentation entirely in markdown
Some of them can be regarded as ways to accelerate Python computations:
- Python by the C side, a summary of alternatives (maybe missing Numba)
- Calling C Libraries from Numba Using CFFI
- Building Extensions for Python 3.5, why each Python version is linked to a particular version of a compiler and how Microsoft's redistributable libraries refactoring affected the development of CPython 3.5
- Py4J, dynamically access Java objects in a Java Virtual Machine (the library used by PySpark)
- IPython or Jupyter?
- What is IPython? (Python vs IPython vs Jupyter)
- Built-in magics reference
- Parallel computation with IPython
- Numeric matrix manipulation – The cheat sheet for MATLAB, Python NumPy, R, and Julia and performance comparisons
- Numpy for Matlab users
- Numpy array technicalities
- Understanding the internals of NumPy to avoid unnecessary array copying
- What is array_like?
- Losing your loops by Jake VanderPlas
- Numpy
- pure Python vs Numpy
- Things in Pandas I Wish I'd Known Earlier
- Summary: Cheat Sheet: The pandas DataFrame Object by Mark Graph
- Official documentation (Full but long documentation, including an introduction of 10 minutes for the most impatient ones)
- Indexing and selecting (loc, iloc, at, iat, ix)
- SettingWithCopyWarning
- Extending Pandas
- Pandas under the hood (Block Manager, indexes, etc.)