Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.

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9. Use isinstance() for Type Checking
(It's safer and more robust than type() because it correctly handles inheritance.)

Cluttered Way (brittle, fails on subclasses):
class MyList(list): pass
my_list_instance = MyList()
if type(my_list_instance) == list:
print("It's a list!") # This will not print

Clean Way (correctly handles subclasses):
class MyList(list): pass
my_list_instance = MyList()
if isinstance(my_list_instance, list):
print("It's an instance of list or its subclass!") # This prints


10. Use the else Block in try/except
(Clearly separates the code that runs on success from the try block being monitored.)

Cluttered Way:
try:
data = my_ risky_operation()
# It's not clear if this next part can also raise an error
process_data(data)
except ValueError:
handle_error()

Clean Way:
try:
data = my_risky_operation()
except ValueError:
handle_error()
else:
# This code only runs if the 'try' block succeeds with NO exception
process_data(data)


#Python #CleanCode #Programming #BestPractices #CodeReadability

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By: @DataScience4
10👍3
Learning Common Algorithms with Python

• This lesson covers fundamental algorithms implemented in Python. Understanding these concepts is crucial for building efficient software. We will explore searching, sorting, and recursion.

Linear Search: This is the simplest search algorithm. It sequentially checks each element of the list until a match is found or the whole list has been searched. Its time complexity is O(n).

def linear_search(data, target):
for i in range(len(data)):
if data[i] == target:
return i # Return the index of the found element
return -1 # Return -1 if the element is not found

# Example
my_list = [4, 2, 7, 1, 9, 5]
print(f"Linear Search: Element 7 found at index {linear_search(my_list, 7)}")


Binary Search: A much more efficient search algorithm, but it requires the list to be sorted first. It works by repeatedly dividing the search interval in half. Its time complexity is O(log n).

def binary_search(sorted_data, target):
low = 0
high = len(sorted_data) - 1

while low <= high:
mid = (low + high) // 2
if sorted_data[mid] < target:
low = mid + 1
elif sorted_data[mid] > target:
high = mid - 1
else:
return mid # Element found
return -1 # Element not found

# Example
my_sorted_list = [1, 2, 4, 5, 7, 9]
print(f"Binary Search: Element 7 found at index {binary_search(my_sorted_list, 7)}")


Bubble Sort: A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order. The process is repeated until the list is sorted. Its time complexity is O(n^2).

def bubble_sort(data):
n = len(data)
for i in range(n):
# Last i elements are already in place
for j in range(0, n-i-1):
if data[j] > data[j+1]:
# Swap the elements
data[j], data[j+1] = data[j+1], data[j]
return data

# Example
my_list_to_sort = [4, 2, 7, 1, 9, 5]
print(f"Bubble Sort: Sorted list is {bubble_sort(my_list_to_sort)}")


Recursion (Factorial): Recursion is a method where a function calls itself to solve a problem. A classic example is calculating the factorial of a number (n!). It must have a base case to stop the recursion.

def factorial(n):
# Base case: if n is 1 or 0, factorial is 1
if n == 0 or n == 1:
return 1
# Recursive step: n * factorial of (n-1)
else:
return n * factorial(n - 1)

# Example
num = 5
print(f"Recursion: Factorial of {num} is {factorial(num)}")


#Python #Algorithms #DataStructures #Coding #Programming #LearnToCode

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By: @DataScience4
1
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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.iss.one/pythonRe 🌟
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If you work with Python, remember a simple rule: do not modify a list while iterating over it. 🐍🛑 This can lead to unexpected results because the iterator does not track structural changes.

Here is an example that looks logical but works incorrectly: 🤔

items = [1, 2, 2, 3, 4]
for item in items:
    if item == 2:
        items.remove(item)
print(items)
# Output: [1, 2, 3, 4]


It seems that all 2s should disappear, but one remains. Why?

After removing an element, the list shifts, but the loop moves on — as a result, some values are simply skipped. 🔄🚫

How to do it correctly — iterate over a copy:

for item in items[:]:
    if item == 2:
          items.remove(item)
print(items)
# Output: [1, 3, 4]


Even better — use list comprehension: 🚀

items = [x for x in items if x != 2]

Conclusion: 🏁 do not modify a collection during iteration. This can lead to skipped elements, duplication, or even errors during execution. 🛠️🚧

#Python #Coding #Programming #Debugging #TechTips #PythonTips
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The Python library itertools contains many useful functions. 🐍

One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻

Here's an example: 📝👇

#Python #Programming #Itertools #Coding #Tech #DataScience
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Cheat sheet on the basics of Python: 🐍📚

basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢

datetime — working with date and time 📅

data structures — Python data structures (list, tuple, dict, set) 🗄

list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘

slicing — obtaining parts of sequences through indices and step ✂️

module/library — connecting modules and libraries 🔌

help functions — using help() and dir() to explore the Python API 🛠

#Python #Coding #DataScience #Programming #Tech #DevCommunity
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Do you know that Python can shift sequences without slicing and creating new lists? 🤔

When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡

#Python #Programming #Deque #CodingTips #Tech #DevCommunity
7
How to check for the presence of subclasses in Python? 🐍🧐

Here's how you can do it:

import inspect

def has_subclasses(cls):
return any(issubclass(sub, cls) for sub in inspect.getmembers(sys.modules[cls.__module__], inspect.isclass))

This function uses the inspect module to find all subclasses of the given class. 🛠️

#Python #Programming #Subclasses #Coding #Dev #Tech
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📂 Reminder about Python map()!

map() — a built-in function that applies the specified function to each element of an iterable object (list, tuple, set, etc.).

The picture shows the basic syntax, an example of use with lambda, and a typical case — data transformation without a manual for loop.

Save it to quickly remember the syntax!

🐍💻🗺️ #Python #Coding #Programming #LearnToCode #DevTips #Tech
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"Introduction to Algorithms" 📘 - an outstanding university resource for everyone studying algorithms and computer science. 🎓💻

The book covers computational complexity, data structures, algorithms on graphs, dynamic programming, divide-and-conquer methods, greedy algorithms, randomized algorithms, and many mathematical foundations of modern computer science. 🧮📊🔍

What's particularly valuable here is the combination of mathematical rigor and practical algorithmic thinking. 🧠 This is one of those books that greatly change the approach to problem analysis, efficiency, and computing itself. 🚀🛠

An essential tool in the library of any developer and engineer working in the field of computer science. 🏗💾

https://www.cs.mcgill.ca/~akroit/math/compsci/Cormen%20Introduction%20to%20Algorithms.pdf 🔗

#Algorithms #ComputerScience #Programming #CSStudent #TechEducation #DevTools
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