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lijin-thu/notes-python

7,114Jupyter NotebookAudience · developerComplexity · 1/5LicenseSetup · easy

TLDR

A collection of Chinese-language Python study notes as interactive Jupyter Notebooks covering basics, NumPy, SciPy, Matplotlib, and advanced topics including Cython, designed for self-study.

Mindmap

mindmap
  root((notes-python))
    Topics
      Python basics
      NumPy arrays
      SciPy science
      Matplotlib charts
    Advanced Topics
      Decorators
      Generators
      Cython
    Audience
      Python learners
      Chinese readers
    Format
      Jupyter Notebooks
      Study reference
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Code map

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Things people build with this

USE CASE 1

Work through Python fundamentals, data types, loops, functions, and modules, using interactive notebook examples you can run and edit.

USE CASE 2

Study NumPy array operations and SciPy scientific computing methods with runnable code cells.

USE CASE 3

Learn how to write Python extension modules and call C or C++ code from Python using Cython.

Tech stack

PythonJupyter NotebookNumPySciPyMatplotlibCython

Getting it running

Difficulty · easy Time to first run · 5min

Originally written for Python 2.7, some code examples may need minor edits to run on Python 3.

Free for personal study and reference only, commercial use is not permitted.

In plain English

This repository is a collection of Chinese-language Python study notes presented as Jupyter Notebook files. The notes cover a broad curriculum starting from Python tools and the basics of the language, then moving into scientific computing libraries including NumPy, SciPy, and Matplotlib, and finally covering more advanced Python topics and how to extend Python with code written in other languages. The topics sections include Python data types, strings, lists, dictionaries, loops, functions, modules, and file input and output. The NumPy section covers arrays, array shapes, sorting, matrix operations, broadcasting, and reading and writing array data. The SciPy section covers interpolation, statistics, curve fitting, optimization, integration, and sparse matrices. The Matplotlib section covers plotting basics, style sheets, text handling, annotations, and a variety of chart types. Advanced Python topics include iterators, generators, decorators, context managers, regular expressions, working with CSV files and SQL databases, and dynamic code execution. A section on interfacing with other languages covers writing Python extension modules and using Cython to call C and C++ code from Python. The README notes the content is largely assembled from online sources and is provided for personal study and reference only. Commercial use is not permitted. A physical book based on this material has been published in Chinese. The notes were originally written for Python 2.7, which is now outdated, so readers following along may encounter differences if using a modern Python version.

Copy-paste prompts

Prompt 1
I'm using the notes-python NumPy notebooks, show me how to perform matrix multiplication and array broadcasting on two 2D arrays.
Prompt 2
Based on the notes-python Matplotlib section, help me create a line chart with annotations and a custom style sheet.
Prompt 3
Using the SciPy curve fitting examples from notes-python, write code to fit a polynomial to noisy measurement data.
Prompt 4
Show me how decorators work in Python, following the style of the notes-python advanced topics notebooks.
Prompt 5
How do I use Cython as shown in notes-python to wrap a simple C function and call it from Python?
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