python scientific computing and data analysis commonly used library

1. NumPy
   NumPy is the most powerful n-dimensional array, the library consists of the basic linear algebra functions, Fourier transform, random function, and other low-level language (such as Fortran, C, and C ++) integrated tools.
2. SciPy
   SciPy built NumPy basis, it is one of the library discrete Fourier transform, linear algebra, optimization and sparse matrix and other high-level science and engineering modules most useful.
3. Matplotlib
   matplotlib mainly used to draw various graphics, from the histogram to the diagram, FIG heat may also be used to add commands Latex mathematical symbols in the image.
4. PANDAS
   PANDAS is mainly used for arithmetic operations and structured data, widely used for data sorting and pre-processing, which helps to improve the use of scientific data in the Python community.
5. Scikit
   Scikit mainly used for machine learning, the library is built on NumPy, SciPy and matplotlib basis, it contains many efficient machine learning and statistical modeling tools, such as classification, regression, clustering and dimension reduction.
6. Statsmodels
   Statsmodels for statistical modeling. Statsmodels provide users explore data in a Python estimate statistical models and perform statistical testing module. Can be used for descriptive statistics, statistical tests, and results of statistical mapping function of different types of data.
7. Seaborn
   Seaborn for data visualization. Seaborn is in Python for making attractive and informative statistical graphics library. It is based on matplotlib. Seaborn aims to become the core of visual data exploration and understanding of the composition.
8. Bokeh
   Bokeh is used to create interactive charts, dashboards, and data applications on a modern web browser. It gives users D3.js elegant simplicity of style generated graphics. In addition, it has the ability to interact with large or high-performance streaming data sets.
9. Blaze
   Blaze and the ability Pandas Numpy extended to distributed the streaming data and the sets. It can be used to access from a number of sources (including Bcolz, MongoDB, SQLAlchemy, Apache Spark , PyTables etc.) data. Together with Bokeh, Blaze as a powerful tool to create effective visualization and dashboards in huge data blocks.
10. Scrapy
    scrapy for web crawler. It is to obtain specific pattern data is very useful framework. It starts from the Home url, then tap the web content within the site to gather information.
11. SymPy
    SymPy for symbolic computation. It has a wide range of capabilities from basic arithmetic to symbolic calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the calculation result formatted as LaTeX code.
12. Requests
    Requests for web access. It is similar to the standard python library urllib2, but the code easier. You will find urllib2 with subtle differences, but for beginners, Requests may be more convenient.