codes = ["A", "B", "C"]
found = False
for code in codes:
    if code == "B":
        found = True
        break
if found:
    print("Incorrect: Code B found (less efficient).")

Brief Explanation: The in operator is optimized for membership checks, offering better performance and cleaner code than manual loops, especially for larger lists.

---

5. Avoiding Unnecessary List Conversions

Description: Many functions and methods return iterators or generator objects for efficiency. Converting these directly to a list without need can waste memory and computation if you only need to process elements one by one.

Correct Usage: Process iterators directly when possible, convert to list only if multiple passes or random access is needed.

squares_gen = (x*x for x in range(5)) # Generator expression
for s in squares_gen: # Process elements one by one
    print(f"Correct: {s}", end=" ") # Output: 0 1 4 9 16
print()

# If you need the full list:
squares_list = list(x*x for x in range(5))
print(f"Correct (list conversion): {squares_list}") # Output: [0, 1, 4, 9, 16]

Incorrect Usage: Unnecessarily converting iterators to lists when single-pass processing suffices.

data_stream = map(str.upper, ['apple', 'banana', 'cherry'])
# If you only need to print them once:
full_list = list(data_stream) # Unnecessary list creation
for item in full_list:
    print(f"Incorrect: {item}", end=" ") # Output: APPLE BANANA CHERRY
print()

Brief Explanation: Iterators/generators are memory-efficient for single-pass operations. Convert to list() only when random access, repeated iteration, or a material collection is strictly required.

https://t.me/pythonRe 🌟

📂 Reminder on Python data structures!

For example, a list supports indexing, is mutable, and stores duplicates, while a set stores only unique elements and has no order.

The picture shows a brief summary of the main data types and their properties: order, mutability, duplicates, and indexing.

Save it to remember!

👉 @pythonRe

There's a floating-point number in Python and you need to output it as a percentage - use the % format in the f-string

x = .023
print(f'{x:.2%}')  # 2.30%

x = .02375
print(f'{x:.2%}')  # 2.38% -- rounded off!

x = 1.02375
print(f'{x:.2%}')  # 102.38%

👉 @PythonRe

Python Basics Arrays & Loops 🐍

Essential you need to start strong 💪

https://t.me/pythonRe 🔗

Python: simple things that improve code

If you write like this:

if type(x) == str:
    print("This is a string")

it might work, but it breaks on subclasses of str.

It's better to use isinstance(). It takes into account inheritance and is more consistent with polymorphism.

if isinstance(x, str):
    print("This is a string")

This variant will work for str and its subclasses.

Conclusion: type(x) == str is only suitable for simple cases, but it's fragile. isinstance(x, str) is a more stable and correct option almost always.

https://t.me/pythonRe 🤩

Why can't you use mutable default values in constructors?

If you set a list or dictionary as the default value, the object is created once and then reused by all instances.

class User:
    def __init__(self, tags=[]):
        self.tags = tags

This results in a change in one instance affecting the others:

u1 = User(); u2 = User()
u1.tags.append("x"); print(u2.tags)

default_factory creates a new object each time the constructor is called, eliminating shared state:
field(default_factory=list)

Thus, each instance receives an independent data structure:
User().tags is User().tags

🔥 Using default_factory is an important practice when working with mutable types and prevents hard-to-detect state errors.

https://t.me/pythonRe 👍

😎 The Algorithms Python — a huge collection of algorithms in Python! 🐍

The repository contains a large number of algorithms and data structures: sorting, graphs, trees, search, dynamic programming, cryptography, and much more. Everything is implemented in pure Python with clear code and a convenient structure. It's perfect for studying algorithms through real examples.

I'll leave a link: GitHub
https://github.com/TheAlgorithms/Python

👉 https://t.me/pythonRe

Exploring pathlib for Working with Paths!
Many projects still use os.path for path operations: join, dirname, exists, and more. It works, but the code quickly becomes cluttered with string manipulations and harder to read — especially when there are many paths being actively combined.

Since Python 3.4, there's pathlib — an object-oriented API for working with files and directories.

Importing the module is simple:

from pathlib import Path

You can create a path like any regular object:

path = Path("data/users.json")

When working with Path and the / operator, the correct separators for the current OS are used automatically. This keeps the code portable between Linux, macOS, and Windows without extra checks.

If you need an absolute path, use resolve():

print(path.resolve())

Very often when working with files, you need to check if a path exists:

if path.exists():
    print("File found")

Pathlib also lets you quickly determine the type of file system object:

path.is_file()
path.is_dir()

The Path object has convenient properties for getting path parts. This eliminates manual string parsing and working with split().

print(path.name)    # users.json
print(path.stem)    # users
print(path.suffix)  # .json
print(path.parent)  # data

For joining paths, the / operator is used, which looks noticeably cleaner and is easier to read compared to os.path.join:

base = Path("logs")
file_path = base / "2026" / "app.log"

Creating directories is also compact and convenient:

Path("backup/archive").mkdir(parents=True, exist_ok=True)

Here: parents=True creates nested directories; exist_ok=True doesn't raise an error if the folder already exists.

For reading and writing text files, there are built-in methods that cover most everyday tasks:

config = Path("config.txt")

config.write_text("debug=true", encoding="utf-8")

content = config.read_text(encoding="utf-8")
print(content)

For binary data, read_bytes() and write_bytes() methods are available.

You can iterate through directory contents using iterdir():

for file in Path("logs").iterdir():
    print(file)

If you need to search for files by pattern, use glob():

for py_file in Path(".").glob("*.py"):
    print(py_file)

And for recursive directory traversal, there's rglob():

for file in Path(".").rglob("*.json"):
    print(file)

Practical example — finding logs older than a certain date. This is a more real-world task:

from pathlib import Path
from datetime import datetime

logs = Path("logs")
limit_date = datetime(2026, 1, 1)

for file in logs.glob("*.log"):
    modified = datetime.fromtimestamp(file.stat().st_mtime)

    if modified < limit_date:
        print(file.name, modified)

The stat() method lets you get file metadata: size, modification time, permissions, and other system data.

Deleting files and directories is also built directly into the Path API:

path.unlink()  # file
path.rmdir()   # empty directory

It's important to note that pathlib doesn't fully replace shutil or os. For example, for copying files, recursive directory deletion, or complex permission operations, additional modules are usually used.



🔥 pathlib makes working with the file system noticeably cleaner: less string operations, better readability, and more predictable code when working with paths and files.



#Python #Pathlib #Programming #Coding #Developer #SoftwareEngineering #TechTips #LearnPython #PythonTips #FileSystem

https://t.me/pythonRe 🌟

❔ Interview question

What tools are used for error monitoring in Python services?

Answer: Most often, Sentry, centralized logging, and metrics are used. Sentry collects stack traces, context, and shows the frequency of errors.

It's also important to set up alerts - a sharp increase in exceptions usually signals problems after a release or a service degradation.

tags: #interview

https://t.me/pythonRe ✅

𝗣𝗿𝗲𝗶𝗺𝗶𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗚𝘂𝗶𝗱𝗲! 🚀🐍✨

𝗜𝗻𝗽𝘂𝘁/𝗢𝘂𝘁𝗽𝘂𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📥📤
- print()
- input()
- format()

𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔄
- int()
- float()
- str()
- bool()
- complex()
- list()
- tuple()
- set()
- dict()
- frozenset()
- bytes()
- bytearray()
- memoryview()

𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🧮📐
- abs()
- pow()
- round()
- divmod()
- sum()
- min()
- max()

𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 & 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📊📑
- len()
- sorted()
- range()
- zip()
- enumerate()
- reversed()
- all()
- any()

𝗧𝘆𝗽𝗲 & 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔍🆔
- type()
- id()
- isinstance()
- issubclass()

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 📂📝
- open()
- close()
- read()
- write()
- seek()
- tell()

𝗦𝘁𝗿𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🔤🔠
- ord()
- chr()
- ascii()
- repr()

𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🛠⚙️
- help()
- dir()
- eval()
- exec()
- hash()

𝗟𝗼𝗴𝗶𝗰𝗮𝗹 & 𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 🧠🔢
- bin()
- oct()
- hex()
- bool()

𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗢𝗯𝗷𝗲𝗰𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 💾📦
- memoryview()
- object()
- callable()

#PythonGuide #PythonFunctions #CodingLife #LearnPython #DevCommunity #PyTips

https://t.me/pythonRe ✅

Many applications require mapping strings to integers. In Python, this usually looks like:

d = {"apple": 100, "banana": 200, "cherry": 300}

If there are 1 million keys, this can consume a lot of memory — more than 100 bytes per key.
Our elephant has published a new library that uses about 9 bytes per key. Yes, only 9 bytes. Usage looks like this:

from fastconstmap import ConstMap

d = {"apple": 100, "banana": 200, "cherry": 300}
m = ConstMap(d)

m["apple"]                  # -> 100
m.get_many(["banana", "cherry"])  # -> [200, 300]

It can be significantly faster (for example, up to 2 times in some cases) than the standard dictionary. It can also be serialized and deserialized to disk or network for convenient reuse.

https://pypi.org/project/fastconstmap/

github: https://github.com/lemire/fastconstmap

👉 @PythonRe

"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨

The book discusses the implementation and analysis of basic structures: array-based lists, linked lists, hash tables, binary trees, red-black trees, heaps, sorting algorithms, graphs, and data structures for working with integers. 🔍🧮

This is a full-fledged open textbook for studying one of the fundamental topics of computer science and a good reference that's worth keeping on hand. 💻🌟

https://opendatastructures.org/ods-python.pdf 📄

👉 @PythonRe

#DataStructures #Algorithms #Python #ComputerScience #OpenSource #Learning