The development environment for Python machine learning

Python Data Analysis Library Python programming language

Pythong tutorial:https://docs.python.org/3/tutorial/

NumPy

Provides a number of commonly used arrays, matrices and other functions to provide Python with a fast multi-dimensional array processing capabilities.

Official website: http://www.numpy.org

Document Quickstart:https://docs.scipy.org/doc/numpy/user/quickstart.html

SciPy

It is an expansion pack that uses NumPy to do advanced mathematics, signal processing, optimization, and statistics.
A number of scientific computing toolkits have been added on the basis of numpy.

Official website: https://www.scipy.org

Numpy and Scipy Documentation

SCIPY Documentation

Pandas

Python Data Analysis Library

is a high-level data structure and ingenious tool built on the numpy, which can quickly and easily process the information.

It provides more data reading and writing tools based on NumPy.

Official website: http://pandas.pydata.org/

Document: http://pandas.pydata.org/pandas-docs/stable/

Matplotlib

Python Drawing Library

Official website: matplotlib.org

Documentation: MATPLOTLIB Documentation

Nltk

Natural Language Processing Toolkit (Natural Language Toolkit)

Igraph

Graph Computing and social network analysis library

http://igraph.org/python/

Scikit-learn

is a python module built on top of scipy for machine learning.

Http://scikit-learn.org/stable/index.html

Python Development Environment PIP

Pip is a Python package management tool that is primarily used to install packages on PyPI and can replace the Easy_install tool.

Recommended tool for installing Python packages: Https://pypi.python.org/pypi/pip

Replacement of domestic sources:pipinstall-ihttps://pypi.tuna.tsinghua.edu.cn/simplenumpy

IPython

Ipython is an interactive Python environment, an enhanced version of Python's native interactive shell, that can accomplish many unusual tasks, such as helping to parallelize computations, mainly using the interactive help it provides, such as code coloring, improved command-line callbacks, tab completion, Macro features and improved interactive help.

Official website: http://ipython.org

Jupyter Notebook

Jupyter Notebook, formerly known as Ipython Notebook, is an interactive programming environment that now supports running 40+ programming languages and can be used to write beautiful interactive documents. Using Jupyter notebook to write Python code, you can display the results of the operation in a good interactive.

Official website: https://jupyter.org

Anaconda

Anaconda Python is a collection of Python science and technology packages, similar in functionality to Python (x, y). It's a new show that has been updated many times. Package management using Conda,gui based on pyside, all packages are basically the latest version, no PYQT and Wxpython, etc., the capacity is moderate, but the scientific calculation package has: Numpy,sicpy,matplotlib,spyder ...

Anaconda Python is a completely free enterprise-class Python release for large-scale data processing, predictive analytics, and scientific computing tools.

Linux system, Anaconda installation, update and delete are very convenient, and all things are installed in only one directory/home/user/anaconda/. The development and maintenance of Anaconda is a core member of the Python founders and community. Anaconda currently offers Python 2.6.x,python 2.7.x,python 3.3.X and Python 3.4.X four series packages, which is a legacy of other distributions. Therefore, in various operating systems, whether it is Linux, or Windows, MAC, it is recommended anaconda!

Since Anacoda is a collection of Python science and technology packages, different packages follow the same protocol, and you can see http://docs.continuum.io/anaconda/licenses.html

Anacoda common documents are as follows:

Anaconda Official documents

Conda Official documents

My Anaconda Landscape

Anaconda Usage Summary

Anaconda integrates Ipthon, Jupyter Notebook to automatically solve Python dependencies. It is convenient to use Anaconda to install, manage, and use Python and Python packages, and we recommend the use of Anaconda.

View Python version

ImportSysPrint(' Python: {} '.format(sys.version))ImportSciPyPrint(' scipy: {} '.format(scipy.__version__))# NumPyImportNumPyPrint(' numpy: {} '.format(numpy.__version__))# matplotlibImportMatplotlibPrint(' matplotlib: {} '.format(matplotlib.__version__))# PandasImportPandasPrint(' pandas: {} '.format(pandas.__version__))# Scikit-learnImportSklearnPrint(' Sklearn: {} '.format(sklearn.__version__))

My development environment output is as follows:

Python: 2.7.13 |Anaconda 4.4.0 (x86_64)| (default, Dec 20 2016, 23:05:08) [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]scipy: 0.19.0numpy: 1.12.1matplotlib: 2.0.2pandas: 0.20.1sklearn: 0.18.1

The development environment for Python machine learning