Template is undoubtedly a good thing, it can fix the format of string and reuse it. At the same time, Template also allows developers to consider the format and content of the string separately, which virtually reduces the pressure on developers.
Template belongs to a class in string, so if you want to use it, you can call it in the following way
from string import Template
Template has a special identifier $, which has the following rules:
Its main implementation method is $xxx, where xxx is a string that meets python naming rules, that is, it cannot start with a number, cannot be a keyword, etc.
If $xxx needs to be in contact with other strings, you can use { } Wrap xxx (it seems to use '()' before, one of my reference books says this, but the current version should only use '{}'). For example, aaa${xxx}aaa
There are two important methods in Template: substitute and safe_substitute.
Both methods can return strings by getting parameters
>>s=Template(There $a and $b) >>print s.subtitute(a='apple',b='banana') There apple and banana >>print s.safe_substitute(a='apple',b='banbana') There apple and banbana
You can also pass data directly by getting the dictionary, like this
>>s=Template(There $a and $b) >>d={'a':'apple','b':'banbana'} >>print s.substitute(d) There apple and banbana
The difference between them lies in the way they handle missing parameters.
The implementation of Template is to first initialize a string through Template. These strings contain keys one by one. By calling substitute or safe_subsititute, the key value is matched with the parameters passed in the method, thereby importing the string at the specified location. One advantage of this method is that there is no need to print '%s' or the like. The order of each parameter must be fixed. As long as the key is correct, the value can be inserted correctly. In this way, you can breathe a sigh of relief when inserting a lot of data. But even if there is such a lazy method, there is still no guarantee that there will be no errors. What if one less key is entered?
Substitute is a serious method. If there is a key that is not entered, an error will definitely be reported. Although it will be ugly, the problem can be found.
safe_substitute will not report an error, but input $xxx directly into the result string, such as
there apple and $b
. The advantage is that the program is always correct, and you don’t have to be confused by errors one by one. Worried.
Template can be inherited, and its subclasses can perform some 'personalization' operations...
The $ character can be changed to other characters, such as "#", by modifying the delimiter field. However, new identifiers need to comply with regular expression specifications.
You can modify the key naming rules by modifying the idpattern. For example, it is stipulated that the first character must start with a, which is very good for standardizing naming. Of course, this is also achieved through regular representation.
from string import Template class MyTemplate(Template): delimiter = "#" idpattern = "[a][_a-z0-9]*" def test(): s='#aa is not #ab' t=MyTemplate(s) d={'aa':'apple','ab':'banbana'} print t.substitute(d) if name=='main': test()
The above is the detailed content of Introduction to the use of Template in python. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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