Life is short, I use Python

                            Life is short, I use Python (turn)

Features of Python

  1. Simple  2. Easy  to learn 3. Free, open source  4. High-level language: package memory management, etc.  5. Portability: If a program avoids using system-dependent features, it can run on any platform without modification  6. Explanatory: Running the program directly from the source code eliminates the need to worry about compiling the program, making the program easier to port.  7. Object-oriented: support for process-oriented programming also supports object-oriented programming.  8. Extensibility: Require confidential or efficient code that can be written in C or C + + and then used in Python programs.  9. embeddable: Python can be embedded in a C + + program to provide scripting capabilities to your program users.  10. Rich libraries: including regular expressions, document generation, unit tests, threads, databases, Web browsers, CGI, FTP,     e-mail, XML, XML-RPC, HTML, WAV files, cryptography systems, GUI (graphical user interface), TK and other system-related operations.     In addition to the standard library, there are many other high-quality libraries, such as Wxpython, twisted, and Python image libraries, and so on.  11. Summary: Python is indeed a very exciting and powerful language. It combines the high performance with the simple and interesting features that make the program easy to write.  12. Canonical code: Python uses forced indentation to make the code extremely readable.

Python in the big Data age

Let's take a look at Python's ecosystem in the field of data analysis!

Base Library

• Numpy: Matrix computing and the data processing basis of most other frameworks;

• SCIPY: Scientific Computing Library, providing a lot of scientific calculation toolkit and algorithm;

• Matplotlab: Professional Drawing tools, say this word or really in matlab between the insertion of the word plot this formation;

• Pandas: Provides a dataframe operation similar to the R language, very convenient;

machine learning and deep learning

• OpenCV: Provides a lot of convenient operation for image recognition;

• Orange: A machine learning program based on a graphical interface, or a Python script that can be used to manipulate calls;

• Scikit-learn: This is the masterpiece of Python in the field of machine learning, as stated earlier. In particular, its documentation, can be used as a reference to machine learning to read, once I recommend to a friend said, said, the Scikit-learn document as a Buddhist scriptures to read, false in time, will greatly increase the skill.

• Theano: A very well-known framework in deep learning and very representative. is the foundation of many other frameworks.

• Keras: Based on the abstraction of Theano, it is recommended that you use this as a building block to get a neural network out.

• NLTK: Natural language Processing, providing a powerful feature.

Mxnet Python interface for domestic productions

Distributed machine learning and deep learning

• Spark's Mllib Python interface Pyspark

• H2O's Python interface

• Charged graph create for Python interface

• Google's recently-TensorFlow python interface

• Samsung recently out of Veles, currently only available in Python interface

• New machine learning or deep learning frameworks, if not provided Python interface, I am afraid it will be difficult to promote it.

  The above list is only part of it, there are many more. Of course, many of them are not implemented in Python, but they all share the same Python interface, and even several have made Python a prime citizen (first-class).

  This is not to say that the Python language is powerful or complex, and on the contrary, thanks to Python's simplicity and inclusiveness. So that it has such a place in the field of data mining.

  This is the power of the biosphere, not the personal will of the transfer.

  For friends who want to get started with data analysis, data mining, and machine learning, Python is a great time-to-spend choice. Because, in addition to the chain of tools above the biosphere, there are books and knowledge of the spread of the ecological circle.

Life is short, I use Python