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