Python has a wide range of uses, and the author will often come into contact with various Python-related projects, and can be regarded as a loyal developer. Will be happy to answer questions about Python usage. The concept of Python is very large. Its positioning is "computer programming language". From its characteristics, it is an "object-oriented" language and an "interpreted" language. There are many computer programming languages, including the most classic C, the same object-oriented C++, Java, and C#, as well as R for data computing and go. Python's ability to stand out from many programming languages is also determined by its own inherent characteristics.
First of all, Python is an interpreted language which means Python is relatively easy to get started with. The syntax of Python is closer to the syntax of English, and executing the basic program through the interpreter allows the computer to read the instructions. Of course, for developers, the program code of Python is easier to understand, but the execution code needs to be translated sentence by sentence, so it also occupies more hardware resources than the C language. Like MATLAB, since it is an interpreted language, Python is a powerful tool for data analysis for people like us who are not very proficient in computer languages but need to process data at work.
Powerful and rich libraries are a major feature of Python, followed by strong adhesion to C-like languages, which makes Python handy in data processing. Therefore, the author will first introduce the data analysis and processing functions of Python.
1. Data analysis and processing
Typically, Python is used for data analysis. Use C to design some low-level algorithms to encapsulate, and then use Python to call them. Because the algorithm module is relatively fixed, it is convenient and flexible to use Python to directly call it, and it can be used flexibly according to the needs of data analysis and statistics. Python is also a relatively complete data analysis ecosystem, in which matplotlib is often used to draw data charts. It is a 2D drawing tool with good cross-platform interaction characteristics. Histograms, scatter plots, bar charts, etc. used in daily descriptive statistics will be used, and a few lines of code can be used to plot. The K-line charts and monthly charts we see every day can also be drawn with matplotlib. If you do data analysis in the securities industry, Python is essential.
Another example is Pandas, which is also a data analysis package commonly used by Python for data analysis, and it is also a very useful open source tool. Pandas can perform calculations on more complex two-dimensional or three-dimensional arrays, and can also process data in relational databases. Compared with the R language, the range of data.frame calculations is much smaller than the range of DataFrame in Pandas, which also From another side, it shows that the data analysis function of Python is stronger than that of R.
In addition to the above two points, SciPy can also solve many scientific computing problems, such as differential equations, matrix analysis, probability distribution and other mathematical problems.
2. Web development and application
Python is the mainstream language for web development, but it cannot be said to be the best language. JavaScript, which is also an interpreted language, has been widely used in web development because it has a mature framework. But Python also has unique advantages. For example, Python is more complete at the language level than JS and PHP, and can provide multiple solutions for the same development needs. The library is rich in content and easy to use. Python also has its own frameworks on the web side, such as django and flask. It can be said that the Web projects developed in Python are small and sophisticated, support the latest XML technology, and have relatively powerful data processing functions.
3. Artificial intelligence application
In the application of artificial intelligence, the author believes that it benefits from Python's powerful and rich libraries and data analysis capabilities. For example, in terms of neural networks and deep learning, Python can find relatively mature packages to call. Moreover, Python is an object-oriented dynamic language and suitable for scientific computing, which makes Python popular in artificial intelligence. Although artificial intelligence programs are not limited to Python, they still provide a large number of APIs for Python, which is precisely because Python contains many modules suitable for artificial intelligence, such as sklearn modules and so on. Convenient invocation and powerful scientific computing functions are still Python's strongest competitiveness in the field of AI.