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This article mainly introduces "how to realize the anti-pattern in Python". In the daily operation, I believe that many people have doubts about how to realize the anti-pattern in Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubt of "how to realize the anti-pattern in Python". Next, please follow the editor to study!

1. No with is used to open files

When you open a file without the with statement, you need to remember that close () closes the file with an explicit call after processing. Even if the resource is explicitly closed, an exception can occur before the resource is actually released. This can lead to inconsistencies or file corruption. The open file implements the context manager protocol through with, freeing resources when execution is outside the with block.

Bad practices:

Python:

New_file = open ('some-file.txt', 'r') # do something excitingnew_file.close ()

Good practices:

Python:

With open ('some-file.txt', 'r') as fd: data = fd.read () # do something exciting2. It is not necessary to use list/ dict/set to understand

Built-in similar functions all,any,enumerate,iter,itertools.cycle and itertools.accumulate can work directly with generator expressions. They don't need to understand.

In addition to them, all () and any () also support short circuits in Python, but this behavior will be lost if used to understand it. This affects performance.

Bad practices:

Python:

... comma_seperated_names =', '.join ([name for name in my_fav_superheroes])

Good practices:

Python:

... comma_seperated_numbers =', '.join (name for name in my_fav_superheroes) 3. Unnecessary use of generators

There is no need to use generator expressions list,dict or set in calls to because each of these types is understood. Instead of using list/ dict/set surrounding generator expressions, they can be written as their respective understandings.

Bad practices:

Python:

Squares = dict ((1) for i in range (1))

Good practices:

Python:

Squares = {I: iPrefecture 2 for i in range (1m 10)} 4. Returns multiple object types in a function call

Having inconsistent return types in a function can make the code confusing and difficult to understand, and can lead to insurmountable errors. If a function should return a given type (such as integer constants, lists, tuples), but can also return other types, the caller of the function will always need to check the type of the return value. It is recommended that only one type of object is returned from the function.

If you need to return something empty in some failed cases, it is recommended that you throw an exception that can be caught cleanly.

Bad practices:

Python:

Def get_person_age (name): person = db.get_person (name) if person: return person.age # returns an int # returns None if person not found

Good practices:

Python:

Def get_person_age (name): person = db.get_person (name) if not person: raise Exception (f'No person found with name {name}') return person.age # guaranteed to return int every time5. Return the default value from the dictionary without using get ()

This anti-pattern affects the readability of the code. We often see code create a variable, assign it a default value, and then look for a key in the dictionary. If the key exists, the value of the key is assigned to the value of the variable. There is nothing wrong with doing this, but because it queries the dictionary twice, it is lengthy and inefficient, and the method of using the get () dictionary can be done easily.

Bad practices:

Python:

Currency_map = {'usd':' US Dollar'} if 'inr' in currency_map: indian_currency_name = currency_map [' inr'] else: indian_currency_name = 'undefined'

Good practices:

Python:

Currency_map = {'usd':' US Dollar'} indian_currency_name = currency_map.get ('inr',' undefined') 6. Do not use items () iterative dictionary

The method on the items dictionary returns an iterable object with a key-value tuple that can be unpackaged in a for loop. This method is commonly used, so it is recommended.

Bad practices:

Python:

For code in country_map: name = country_ map [code] # do something with name

Good practices:

Python:

For code, name in country_map.items (): # do something with name pass7. Initialize empty list/ dict/tuple without literal syntax

Initializing the empty dictionary dict () by calling is slower than using an empty literal value, because the name dict must be looked up globally in case it is rebound. The same goes for the other two types-list () and tuple ().

Bad practices:

Python:

My_evans = list () # Add something to this empty list

Good practices:

Python:

My_evans = [] # Add something to this empty list8. Push debugger in production code

Most of us have done this at least once-when debugging code, it may happen when you push the code after you find an error but forget to delete the debugger. This is critical and may affect the behavior of the code. It is strongly recommended that you review the code to delete the debugger call before checking in.

Using the Python parser, you can detect all these anti-patterns in the code base before they are put into production.

At this point, the study of "how to implement anti-patterns in Python" 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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