Learn Python Coding

📌 Marking Deprecated Code in Python: deprecated() In large projects, old functions are not always immediately removed. They are often left for compatibility, but it's important to warn developers that they should no longer be used. Previously, warnings.warn() was often used for this purpose: import warnings def old_api(): warnings.warn( "Use new_api()", DeprecationWarning ) In Python 3.13, a deprecated() decorator has been introduced: from warnings import deprecated @deprecated("Use new_api()") def old_api(): return "old" Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools. This also works for classes: @deprecated("Use NewClient") class OldClient: pass This is convenient for libraries, SDKs, and large projects where the API is gradually changing. #Python #Python313 #Deprecated #Coding #Programming #SoftwareDevelopment ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

👩‍💻 heapq.merge(): Combining sorted data! If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again. heapq.merge() combines such sources into a single, ordered iterator. In this guide: • We will combine multiple sorted sequences; • We will explore lazy processing of large data sources; • We will configure comparison using the key argument; • We will combine data sorted in reverse order. This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order. #Python #heapq #DataProcessing #CodingTips #Programming #Algorithms ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Why regular expressions can hang on a simple string Even a short string can cause a regular expression to take seconds or minutes to execute. The reason is catastrophic backtracking, where the engine tries a huge number of matching possibilities. Let's look at an example: import re pattern = re.compile(r"(a+)+$") text = "a" * 18 + "!" print(bool(pattern.fullmatch(text))) Output: False The expression (a+)+$ looks simple, but the problem lies in the nested quantifiers (a+)+. When the engine encounters the !, the match fails. However, before giving up, it tries various ways to distribute the a characters among the repetitions. The number of possibilities grows rapidly with the length of the string. Let's check the execution time: import re from time import perf_counter pattern = re.compile(r"(a+)+$") for size in (10, 12, 14, 16, 18): text = "a" * size + "!" start = perf_counter() pattern.fullmatch(text) elapsed = perf_counter() - start print(size, f"{elapsed:.6f} сек.") The time depends on the processor and the version of Python, but for this pattern, you'll see an exponential increase in execution time as the number of a characters increases. This is dangerous when processing user input or external data. If you need to check if a string consists only of a characters, nested repetitions are not necessary: pattern = re.compile(r"a+") print(bool(pattern.fullmatch("aaaa"))) print(bool(pattern.fullmatch("aaaa!"))) Output: True False But not every problematic pattern can be simplified so easily. Starting with Python 3.11, the re module supports atomic groups (?x). They prevent the engine from backtracking inside a successfully matched group to try other possibilities: import re pattern = re.compile(r"(?x)a+!$") print(bool(pattern.fullmatch("aaaa"))) print(bool(pattern.fullmatch("aaaa!"))) Output: True False The group (?x)a+ fixes the captured sequence of characters. The engine can no longer redistribute them within the group, which eliminates c… Read in full →