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This article mainly explains "what are the useful suggestions for Python programming". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "what useful suggestions are there in Python programming"?

1. The project documents are filed in advance.

Every time I start a new job, I always crave convenience. Code, Data and documents are all concentrated in a folder, which looks very messy, which once makes the backtracking process very painful, or when I change a computer, the files all fail, and I need to change the path by myself, which is very painful.

After your own exploration, you can roughly divide the project into several subfolders, and code is placed in the main folder:

two。 Never manually modify the source data and make a backup ✍️

We need to make a good backup of the source data to facilitate our next backtracking. We can do the next operation or modify the intermediate steps. Moreover, we also need to back up the code and other files to avoid accidental loss.

Here is an article from Liangxu Linux that recommends four tools:

Git version control system

Rsync file backup

Dropbox cloud storage

Time Machine time machine

More introduction and use of tools will not be carried out here, you can go to understand it by yourself.

3. Make the correct configuration of the path

Many students like to use the absolute path directly when writing the path, although there is no problem in general, but if the code is shared with others to learn or run, the problem will come. In many cases, you can't run through it directly.

Here is a suggestion:

Use relative paths: scripts are located in the home directory, and other resources (such as data, third-party packages, etc.) are in their sibling or lower-level directories, such as. / data/processed/test1.csv

Global path configuration variables:

# set the home directory HOME_PATH = ringing Elaise ML90615-PROJECT1'# read data data = open (HOME_PATH+'/data/processed/test1.csv') data = pd.read_csv (data) data.head ()

4. Make comments and instructions where necessary in the code

I'm sure most people feel the same way, don't you believe? Take back the code you wrote a month ago and see how much you can understand (if you don't make notes)

5. Speed up your Python loop code ⚡️

Here is an article by Brother Yun (Python and the beauty of algorithms): 24-style acceleration of your python

Put it away, read it a few more times, and form a good habit, so that you can write code faster and faster.

6. Visualize the progress of your loop code

Here is an introduction to a Python library, tqdm, first install: pip install tqdm

This is a library that can show the progress of the loop, with which you can be more strategic.

You can look at the following example:

7. Using an efficient exception capture tool

Abnormal bug positioning, I used to be a print () function to the end, although there is no problem, but the efficiency will be relatively slow, and then found a decorator called PySnooper, as if to discover a new world.

We generally debug, is in the place where we may think there will be a problem, to print out, to see what the actual output is, and then think about the problem, which requires us to change the code, very carefully change, compared to directly add a decorator, is very troublesome.

Take a look at Example:

Import pysnooper@pysnooper.snoop ('. / file.log') def number_to_bits (number): if number: bits = [] while number: number, remainder = divmod (number, 2) bits.insert (0, remainder) return bits else: return [0] number_to_bits (6)

We save the output of each step of the function as file.log, and we can go directly to see what went wrong.

Project address: https://github.com/cool-RR/pysnooper

8. More consideration should be given to code robustness

What is the robustness of the code, as the name implies, is to withstand the testing of various exception scenarios, exception handling work consists of "catch" and "throw" two parts. "capture" refers to the use of try. Except wraps specific statements and completes the error flow properly. Proper use of raise to actively "throw" an exception is an essential part of elegant code. Here are a few points for your reference:

1) know what the parameters are, type and number (exception handling, logical judgment)

Def add (a, b): if isinstance (a, int) and isinstance (b, int): return aquib else: return 'parameter type error' print (add (1, 2)) print (add (1,'a'))

2) only do the most accurate exception capture

Sometimes we think that the script work is the king, so regardless of whether we create a big try...except to wrap the whole piece of code, it is easy to swallow up the AttibuteError that should have been thrown. This adds unnecessary trouble to our debug process.

Therefore, we always capture only blocks of statements that might throw exceptions, and try to capture only the exact type of exception, not the obscure Exception.

From requests.exceptions import RequestExceptiondef save_website_title (url, filename): try: resp = requests.get (url) except RequestException as e: print (f'save failed: unable to get page content: {e}') return False# itself should not throw an exception, so we do not need to use try statement block # if group is mistakenly typed as grop, the program will tell us through AttributeError immediately. Obj = re.search (r'(. *)', resp.text) if not obj: print ('save failed: title tag not found in page content') return Falsetitle = obj.group (1) try: with open (filename,' w') as fp: fp.write (title) except IOError as e: print (f'save failed: unable to write to file {filename}: {e}') return Falseelse: return True

3) exception handling should not dominate the host.

As mentioned in the previous article, exception capture should be accurate, but if each one is accurate, there will be a lot of try...except statements in our code, which disturbs the core code and the readability of the code as a whole.

Here, we can use the context manager to improve our exception handling flow and simplify repeated exception handling logic.

Class raise_api_error: "captures specified exception and raise ApiErrorCode instead:raises: AttributeError if code_name is not valid"def _ init__ (self, captures, code_name): self.captures = captures self.code = getattr (error_codes, code_name) def _ enter__ (self): # this method will call return selfdef _ exit__ (self, exc_type, exc_val) when entering the context Exc_tb): # this method will call # exc_type, exc_val, exc_tb when exiting the context to represent the # exception type, exception value, error stack if exc_type is None: return False if exc_type = = self.captures: raise self.code from exc_val return False thrown in this context, respectively

In the above code, we define a context manager called raise_api_error that does nothing when entering the context. However, when exiting the context, it is determined whether an exception of type self.captures has been thrown in the current context, and if so, replace it with the APIErrorCode exception class.

After using the context manager, the concise code is as follows:

Def upload_avatar (request): "" user uploads new avatar "" with raise_api_error (KeyError, 'AVATAR_FILE_NOT_PROVIDED'): avatar_file = request.FILES [' avatar'] with raise_api_error (ResizeAvatarError, 'AVATAR_FILE_INVALID'), raise_api_error (FileTooLargeError,' AVATAR_FILE_TOO_LARGE'): resized_avatar_file = resize_avatar (avatar_file) with raise_api_error (Exception) 'INTERNAL_SERVER_ERROR'): request.user.avatar = resized_avatar_file request.user.save () return HttpResponse ({}) Thank you for your reading The above is the content of "what useful suggestions are there in Python programming". After the study of this article, I believe you have a deeper understanding of what useful suggestions there are in Python programming, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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