python_reference - Python資源匯總

有關python的腳本、技巧、教程、算法和一堆有意思的東東，大部分是IPython Notebook形式提供。

• // Python tips and tutorials
• // Python and the web
• // Algorithms
• // Plotting and Visualization
• // Benchmarks
• // Python and "Data Science"
• // Other
• // Useful scripts and snippets
• // Links
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// Python tips and tutorials

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• A collection of not so obvious Python stuff you should know! [IPython nb]

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• Python's scope resolution for variable names and the LEGB rule [IPython nb]

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• Key differences between Python 2.x and Python 3.x [IPython nb]

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• A thorough guide to SQLite database operations in Python [Markdown]

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• Unit testing in Python - Why we want to make it a habit [Markdown]

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• Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [Markdown]

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• Sorting CSV files using the Python csv module [IPython nb]

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• Using Cython with and without IPython magic [IPython nb]

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• Parallel processing via the multiprocessing module [IPython nb]

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• Entry point: Data - using sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]

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• Awesome things that you can do in IPython Notebooks (in progress) [IPython nb]

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• A collection of useful regular expressions [IPython nb]

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• Quick guide for dealing with missing numbers in NumPy [IPython nb]

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• A random collection of useful Python snippets [IPython nb]

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• Things in pandas I wish I'd had known earlier [IPython nb]

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  // Python and the web

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  • Creating internal links in IPython Notebooks and Markdown docs [IPython nb]

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  • Converting Markdown to HTML and adding Python syntax highlighting [Markdown]

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    // Algorithms

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    The algorithms category has been moved to a separate GitHub repository rasbt/algorithms_in_ipython_notebooks

    • Sorting Algorithms [IPython nb]

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    • Linear regression via the least squares fit method [IPython nb]

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    • Dixon's Q test to identify outliers for small sample sizes [IPython nb]

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    • Sequential Selection Algorithms [IPython nb]

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    • Counting points inside a hypercube [IPython nb]

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      // Plotting and Visualization

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      The matplotlib-gallery in IPython notebooks has been moved to a separate GitHub repository matplotlib-gallery

      Featured articles:

      • Preparing Plots for Publication [IPython nb]
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      // Benchmarks

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      • Simple tricks to speed up the sum calculation in pandas [IPython nb]
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      More benchmarks can be found in the separate GitHub repository One-Python-benchmark-per-day

      Featured articles:

      • (C)Python compilers - Cython vs. Numba vs. Parakeet [IPython nb]

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      • Just-in-time compilers for NumPy array expressions [IPython nb]

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      • Cython - Bridging the gap between Python and Fortran [IPython nb]

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      • Parallel processing via the multiprocessing module [IPython nb]

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      • Vectorizing a classic for-loop in NumPy [IPython nb]

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        // Python and "Data Science"

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        The "data science"-related posts have been moved to a separate GitHub repository pattern_classification

        Featured articles:

        • Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]

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        • About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [IPython nb]

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        • Principal Component Analysis (PCA) [IPython nb]

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        • Linear Discriminant Analysis (LDA) [IPython nb]

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        • Kernel density estimation via the Parzen-window technique [IPython nb]

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          // Useful scripts and snippets

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          • watermark - An IPython magic extension for printing date and time stamps, version numbers, and hardware information.

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          • Shell script For prepending Python-shebangs to .py files.

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          • Convert 'tab-delimited' to 'comma-separated' CSV files [IPython nb]

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          • A random string generator function

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            // Links

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            • PyPI - the Python Package Index - The official repository for all open source Python modules and packages.

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            • PEP 8 - The official style guide for Python code.

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              // News

              • Python subreddit - My favorite resource to catch up with Python news and great Python-related articles.

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              • Python community on Google+ - A nice and friendly community to share and discuss everything about Python.

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              • Python Weekly - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python.

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                // Resources for learning Python

                • Learn Python The Hard Way - The popular and probably most recommended resource for learning Python.

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                • Dive Into Python / Dive Into Python 3 - A free Python book for experienced programmers.

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                • The Hitchhiker’s Guide to Python - A free best-practice handbook for both novices and experts.

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                • Think Python - How to Think Like a Computer Scientist - An introduction for beginners starting with basic concepts of programming.

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                • Python Patterns - A directory of proven, reusable solutions to common programming problems.

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                  // My favorite Python projects and packages

                  • The IPython Notebook - An interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.

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                  • matplotlib - Python's favorite plotting library.

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                  • NumPy - A library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.

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                  • SciPy - A library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ...

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                  • pandas - High-performance, easy-to-use data structures and data analysis tools build on top of NumPy.

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                  • Cython - C-extensions for Python, an optimizing static compiler to combine Python and C code.

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                  • Numba - A just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)

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                  • scikit-learn - A powerful machine learning library for Python and tools for efficient data mining and analysis.
                    
                    

                    </li> </ul> https://github.com/rasbt/python_reference