This is a backport of the subprocess standard library module from Python 3.2 and 3.3 for use on Python 2. It includes bugfixes and some new features. On POSIX systems it is guaranteed to be reliable when used in threaded applications. It includes timeout support from Python 3.3 but otherwise matches 3.2’s API.

NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, useful linear algebra, Fourier transform, and random number capabilities. Version 1.8 is the last one to contain the numpy.oldnumeric API that includes the compatibility layer numpy.oldnumeric with NumPy's predecessor Numeric.

Packaging is a Python module for dealing with Python packages. It offers an interface for working with package versions, names, and dependency information.

This is a backport of the standard library typing module to Python versions older than 3.5. Typing defines a standard notation for Python function and variable type annotations. The notation can be used for documenting code in a concise, standard format, and it has been designed to also be used by static and runtime type checkers, static analyzers, IDEs and other tools.

The goal of pathlib2 is to provide a backport of standard pathlib module which tracks the standard library module, so all the newest features of the standard pathlib can be used also on older Python versions.

Pathlib offers a set of classes to handle file system paths. It offers the following advantages over using string objects:

1. No more cumbersome use of os and os.path functions. Everything can be done easily through operators, attribute accesses, and method calls.

2. Embodies the semantics of different path types. For example, comparing Windows paths ignores casing.

3. Well-defined semantics, eliminating any inconsistencies or ambiguities (forward vs. backward slashes, etc.).

Pycairo is a set of Python bindings for the Cairo graphics library.

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

attrs is a Python package with class decorators that ease the chores of implementing the most common attribute-related object protocols.

python-configparser is a backport of configparser from Python 3.5 so that it can be used directly in other versions.

ScientificPython is a collection of Python modules that are useful for scientific computing. Most modules are rather general (Geometry, physical units, automatic derivatives, ...) whereas others are more domain-specific (e.g. netCDF and PDB support). The library is currently not actively maintained and works only with Python 2 and NumPy < 1.9.

Wcwidth measures the number of terminal column cells of wide-character codes. It is useful for those implementing a terminal emulator, or programs that carefully produce output to be interpreted by one. It is a Python implementation of the wcwidth and wcswidth C functions specified in POSIX.1-2001 and POSIX.1-2008.

Python Build Reasonableness (PBR) is a library that injects some useful and sensible default behaviors into your setuptools run. It will set versions, process requirements files and generate AUTHORS and ChangeLog file from git information.

Enum34 is the new Python stdlib enum module available in Python 3.4 backported for previous versions of Python from 2.4 to 3.3.

Setuptools_scm handles managing your Python package versions in software configuration management (SCM) metadata instead of declaring them as the version argument or in a SCM managed file.

This package provides an implementation of importlib.resources for older versions of Python.

Kiwi is an efficient C++ implementation of the Cassowary constraint solving algorithm. Kiwi has been designed from the ground up to be lightweight and fast. Kiwi ranges from 10x to 500x faster than the original Cassowary solver with typical use cases gaining a 40x improvement. Memory savings are consistently > 5x.

importlib_metadata is a library which provides an API for accessing an installed Python package's metadata, such as its entry points or its top-level name. This functionality intends to replace most uses of pkg_resources entry point API and metadata API. Along with importlib.resources in Python 3.7 and newer, this can eliminate the need to use the older and less efficient pkg_resources package.

Cython is an optimising static compiler for both the Python programming language and the extended Cython programming language. It makes writing C extensions for Python as easy as Python itself.

Library for atomic file writes using platform dependent tools for atomic file system operations.

Pytest-cov produces coverage reports. It supports centralised testing and distributed testing in both load and each modes. It also supports coverage of subprocesses.

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

This module is primarily a backport of the Python 3.2 contextlib to earlier Python versions. Like contextlib, it provides utilities for common tasks involving decorators and context managers. It also contains additional features that are not part of the standard library.

The dateutil module provides powerful extensions to the standard datetime module, available in Python 2.3+.

Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It’s based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow.