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This article mainly introduces "what are the tools that can make Python faster". In daily operation, I believe many people have doubts about the tools that can make Python faster. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the questions of "what tools can make Python faster?" Next, please follow the editor to study!

1.NumPy, SciPy, Sage and Pandas

First, NumPy. Its core is the implementation of a multi-dimensional digital array. In addition to this data structure, several functions and operators are implemented to perform array operations efficiently. And the number of calls is simplified. It can be used to perform extremely efficient mathematical operations.

Both SciPy and Sage have built-in NumPy as part of themselves, along with other different tools that can be used for specific scientific, mathematical, and high-performance computing modules.

Pandas is a tool that focuses on data analysis. If you are dealing with large amounts of semi-structured data, you may also use Pandas-related tools, such as Blaze.

2.PyPy, Pyston, Parakeet, Psyco and Unladen Swallow

The least intrusive way to make your code run faster is to use a real-time compiler (JIT compilation). In the past, we could install Psyco directly. Import psyco after installation, and then call psyco.full (). The running speed of the code can be significantly improved. When running Python code, it can monitor the program in real time and compile part of the code into machine code.

Now many accelerator projects such as Psyco have been discontinued, but similar features have been inherited in PyPy.

To facilitate analysis, optimization, and translation of PyPy, Python is reimplemented in Python so that it can be compiled by JIT. And PyPy can directly translate the code into a higher-performance language like C.

Unladen Swallow is a JIT compiler for Python. Is a version of the Python interpreter and is called the underlying virtual machine (LLVM). But this development has stopped.

Pyston is a Python JIT compiler that is close to the LLVM platform. In many cases, it is better than the implementation of Python, but there are still many imperfections.

3.GPULib, PyStream, PyCUDA and PyOpenCL

These four are used in the image processing unit to achieve code acceleration. All of the above is accelerated by code optimization. These are accelerated from the hardware level, if there is a powerful GPU, we can use GPU to calculate, thus reducing the valuable resources of CPU.

PyStream is a little older. GPULib provides various forms of data calculation based on GPU.

If you use GPU to speed up your code, you can use PyCUDA and PyOpenCL.

4.Pyrex, Cython, Numba and Shedskin

All four projects are dedicated to translating Python code into C, C++ and LLVM code. Shedskin compiles the code into the C++ language. The main goal of Pyrex and Cython compilation is the C language. Cython is also a branch of Pyrex.

Also, Cython has additional support for NumPy arrays.

If facing arrays and mathematical calculations, Numba is a better choice. Import will automatically generate the corresponding LLVM code. The upgraded version is NumbaPro with support for GPU.

5.SWIG, F2PY and Boost.Python

These tools can encapsulate other languages as modules of Python. The first can encapsulate the Cmax Cobb + language. F2PY can encapsulate Fortran. Boost.Python can encapsulate C++ language.

SUIG only needs to start a command line tool and enter the header file of C or C++ into it, and the wrapper code will be generated automatically. In addition to Python, it can also be a wrapper for other languages, such as Java and PHP.

6.ctypes, llvm-py and CorePy2

These modules can help us to implement the operation of the underlying objects in Python. The ctypes module can be used to build C-compiled objects in memory. And call the function of C in the shared library. However, ctypes is already included in Python's standard library.

Llvm-py mainly provides the Python interface of LLVM. So that you can build the code and then compile them. You can also build its compiler in Python. Of course, it is possible to come up with your own programming language.

CorePy2 can also be accelerated, but this acceleration runs at the assembly layer.

7.Weave, Cinpy and PyInline

These three packages allow us to use C or other high-level languages directly in our Python code. Mixed code can still be kept clean and tidy. You can use the multi-line nature of the string of Python code to make other code typeset in its own style.

8. Other tools

If we want to save memory, we can't use JIT. Generally speaking, JIT consumes too much memory. There is a saying that it is true that we can't have both time and memory, and we always have to find a balance between them in engineering development.

As for other things, such as the Micro Python project, this is for embedded devices or microcontrollers.

If you just want to work in a Python environment and then want to use another language, you can take a look at the project Julia.

At this point, the study on "what are the tools that can make Python faster" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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