liuminyt / gist:c02286e1e2ae46e505e8

setuptools

APSW windows上python数据库操作 conn = aspw.Connection(‘conn.db’)

Backports 升级python新版本

Basemap 2D绘图工具包

bitarry bitarry objects like lists

blist better performance for large lists

blaze next generation of NumPy 分布式数据分析包

blosc 针对二进制数据进行优化的压缩器

bokeh 基于html5的可视化库针对大数据集和流

Boost.Python c++和python 交互包

Cartopy 地图科学包

ceODBC 操作数据库 api

certifi

cffi C foreign function interface for pyhon

Cheetah webdevelopment and code generation

Cld compact language detection topLanguageName, topLanguageCode, isReliable, textBytesFound, details = cld.detect(bytes)

console out of style

coverage 测试代码的执行范围

cx_Freeze 跨平台封装执行包

Cython

Delny

dipy

windows安装easy_install 错误：原因和解决方案: ‘ascii’ codec can’t decode byte.

解决：打开C:\Python27\Lib下的 mimetypes.py 文件，找到大概256行（你可以用Notepad++的搜索功能）的

‘default_encoding = sys.getdefaultencoding()’。

在这行前面添加三行：

view plaincopy在CODE上查看代码片派生到我的代码片

1.if sys.getdefaultencoding() != ‘gbk’:

2. reload(sys)

3. sys.setdefaultencoding(‘gbk’)

4.default_encoding = sys.getdefaultencoding()

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Christoph Gohlke Naming Convention for Unofficial Windows Binaries for Python Extension Packages

For example, for scipy here are two of names of wheels on the page:

What does ‘none’ indicate?

What’s the difference between win32 and win_amd64?

Does it matter if I’m using the x86 or x86-64 version of Python (ref Python 2.7.11)?

2 Answers 2

Actually that’s the wheel tool «naming convention». Sincerely I’m not sure what «none» indicates, but yes, your Python version matters. If you’re using the 32-bit interpreter, then go ahead with the win32 option (under Windows, of course). Otherwise download the win_amd64 version for 64-bit distributions.

tl;dr: it’s the wheel naming convention and none means it’s pure python.

I’ve taken the extra step to follow answer/comments.

The none in this case is probably the ABI tag. From PEP 425:

The ABI tag indicates which Python ABI is required by any included extension modules. For implementation-specific ABIs, the implementation is abbreviated in the same way as the Python Tag, e.g. cp33d would be the CPython 3.3 ABI with debugging.

So none in this case means the package is advertised as «pure-python» (none of it’s local dependencies require a specific application binary interface).

This is assuming the provided wheel files are names using the official wheel file name convention:

distribution

Distribution name, e.g. ‘django’, ‘pyramid’.

version

Distribution version, e.g. 1.0.

build tag

Optional build number. Must start with a digit. A tie breaker if two wheels have the same version. Sort as the empty string if unspecified, else sort the initial digits as a number, and the remainder lexicographically.

language implementation and version tag

Unofficial Windows Binaries for Python Extension Packages

by Christoph Gohlke, Laboratory for Fluorescence Dynamics, University of California, Irvine.

I just stumbled upon on wonderful resource online dedicated to python libraries. Thanks to Mr. Christoph Gohlke

This page is an unofficial repository of hundreds of binaries, wheels and libraries of popular applications including python, which was my interest of subject.

His page provides 32- and 64-bit Windows binaries of many scientific open-source extension packages and wheels for the official CPython distribution of the Python programming language. A few binaries are available for the PyPy distribution.

Even though the files are unofficial (informal, unrecognized, personal, unsupported, no warranty, no liability, provided “as is”), most of these available software published are stable release version where 90% bugs are fixed. It could work on 99% of your personal requirements and projects.

The binaries provided there are compatible with the most recent official CPython distributions on Windows. Chances are they do not work with custom Python distributions included with Blender, Maya, ArcGIS, OSGeo4W, ABAQUS, Cygwin, Pythonxy, Canopy, EPD, Anaconda, WinPython etc. Many binaries are not compatible with Windows XP or Wine.

Python Libraries index – My personal choice for cyber security related experiments.

Scientific Applications¶

Context¶

Python is frequently used for high-performance scientific applications. It is widely used in academia and scientific projects because it is easy to write and performs well.

Due to its high performance nature, scientific computing in Python often utilizes external libraries, typically written in faster languages (like C, or Fortran for matrix operations). The main libraries used are NumPy, SciPy and Matplotlib. Going into detail about these libraries is beyond the scope of the Python guide. However, a comprehensive introduction to the scientific Python ecosystem can be found in the Python Scientific Lecture Notes.

Tools¶

IPython¶

IPython is an enhanced version of Python interpreter, which provides features of great interest to scientists. The inline mode allows graphics and plots to be displayed in the terminal (Qt based version). Moreover, the notebook mode supports literate programming and reproducible science generating a web-based Python notebook. This notebook allows you to store chunks of Python code alongside the results and additional comments (HTML, LaTeX, Markdown). The notebook can then be shared and exported in various file formats.

Libraries¶

NumPy¶

NumPy is a low level library written in C (and Fortran) for high level mathematical functions. NumPy cleverly overcomes the problem of running slower algorithms on Python by using multidimensional arrays and functions that operate on arrays. Any algorithm can then be expressed as a function on arrays, allowing the algorithms to be run quickly.

NumPy is part of the SciPy project, and is released as a separate library so people who only need the basic requirements can use it without installing the rest of SciPy.

NumPy is compatible with Python versions 2.4 through 2.7.2 and 3.1+.

Numba¶

Numba is a NumPy aware Python compiler (just-in-time (JIT) specializing compiler) which compiles annotated Python (and NumPy) code to LLVM (Low Level Virtual Machine) through special decorators. Briefly, Numba uses a system that compiles Python code with LLVM to code which can be natively executed at runtime.

SciPy¶

SciPy is a library that uses NumPy for more mathematical functions. SciPy uses NumPy arrays as the basic data structure, and comes with modules for various commonly used tasks in scientific programming, including linear algebra, integration (calculus), ordinary differential equation solving, and signal processing.

Matplotlib¶

Matplotlib is a flexible plotting library for creating interactive 2D and 3D plots that can also be saved as manuscript-quality figures. The API in many ways reflects that of MATLAB, easing transition of MATLAB users to Python. Many examples, along with the source code to recreate them, are available in the matplotlib gallery.

Pandas¶

Pandas is a data manipulation library based on NumPy which provides many useful functions for accessing, indexing, merging, and grouping data easily. The main data structure (DataFrame) is close to what could be found in the R statistical package; that is, heterogeneous data tables with name indexing, time series operations, and auto-alignment of data.

xarray¶

xarray is similar to Pandas, but it is intended for wrapping multidimensional scientific data. By labelling the data with dimensions, coordinates, and attributes, it makes complex multidimensional operations clearer and more intuitive. It also wraps matplotlib for quick plotting, and can apply most operations in parallel using dask.

Rpy2 is a Python binding for the R statistical package allowing the execution of R functions from Python and passing data back and forth between the two environments. Rpy2 is the object oriented implementation of the Rpy bindings.

PsychoPy¶

PsychoPy is a library for cognitive scientists allowing the creation of cognitive psychology and neuroscience experiments. The library handles presentation of stimuli, scripting of experimental design, and data collection.

Resources¶

Installation of scientific Python packages can be troublesome, as many of these packages are implemented as Python C extensions which need to be compiled. This section lists various so-called scientific Python distributions which provide precompiled and easy-to-install collections of scientific Python packages.

Unofficial Windows Binaries for Python Extension Packages¶

Many people who do scientific computing are on Windows, yet many of the scientific computing packages are notoriously difficult to build and install on this platform. Christoph Gohlke, however, has compiled a list of Windows binaries for many useful Python packages. The list of packages has grown from a mainly scientific Python resource to a more general list. If you’re on Windows, you may want to check it out.

Anaconda¶

The Anaconda Python Distribution includes all the common scientific Python packages as well as many packages related to data analytics and big data. Anaconda itself is free, and a number of proprietary add-ons are available for a fee. Free licenses for the add-ons are available for academics and researchers.

Canopy¶

Canopy is another scientific Python distribution, produced by Enthought. A limited ‘Canopy Express’ variant is available for free, but Enthought charges for the full distribution. Free licenses are available for academics.

This opinionated 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.

O’Reilly Book

This guide is now available in tangible book form!

All proceeds are being directly donated to the DjangoGirls organization.

Installing NumPy and SciPy on 64-bit Windows (with Pip)

I found out that it’s impossible to install NumPy/SciPy via installers on Windows 64-bit, that’s only possible on 32-bit. Because I need more memory than a 32-bit installation gives me, I need the 64-bit version of everything.

I tried to install everything via Pip and most things worked. But when I came to SciPy, it complained about missing a Fortran compiler. So I installed Fortran via MinGW/MSYS. But you can’t install SciPy right away after that, you need to reinstall NumPy. So I tried that, but now it doesn’t work anymore via Pip nor via easy_install . Both give these errors:

• There are a lot of errors about LNK2019 and LNK1120 ,.
• I get a lot of errors in the range of C : C2065, C2054 , C2085 , C2143`, etc. They belong together I believe.
• There is no Fortran linker found, but I have no idea how to install that, can’t find anything on it.
• And many more errors which are already out of the visible part of my cmd-windows.

The fatal error is about LNK1120 :

build\lib.win-amd64-2.7\numpy\linalg\lapack_lite.pyd : fatal error LNK1120: 7 unresolved externals error: Setup script exited with error: Command «C:\Users\me\AppData\Local\Programs\Common\Microsoft\Visual C++ for Python\9.0\VC\Bin\amd64\link.exe /DLL /nologo /INCREMENTAL:NO /LIBPATH:C:\BLAS /LIBPATH:C:\Python27\libs /LIBPATH:C:\Python27\PCbuild\amd64 /LIBPATH:build\temp.win-amd64-2.7 lapack.lib blas.lib /EXPORT:initlapack_lite build\temp.win-amd64-2.7\Release\numpy\linalg\lapack_litemodule.obj /OUT:build\lib.win-amd64-2.7\numpy\linalg\lapack_lite.pyd /IMPLIB:build\temp.win-amd64-2.7\Release\numpy\linalg\lapack_lite.lib /MANIFESTFILE:build\temp.win-amd64-2.7\Release\numpy\linalg\lapack_lite.pyd.manifest» failed with exit status 1120

What is the correct way to install the 64-bit versions NumPy and SciPy on a 64-bit Windows machine? Did I miss anything? Do I need to specify something somewhere? There is no information for Windows on these problems that I can find, only for Linux or Mac OS X, but they don’t help me as I can’t use their commands.