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Code With Python
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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🔰 Master File Paths with pathlib in Python

The pathlib module, introduced in Python 3.4, provides an object-oriented interface for working with filesystem paths. It makes your code cleaner, more readable, and platform-independent, saving you from the complexities of string manipulation that come with older modules like os.path.

---

#### Why Use pathlib?

Object-Oriented: Paths are objects with methods, not just strings.
Intuitive Operators: Use the / operator to join paths naturally.
Platform Agnostic: Automatically handles differences between Windows (\) and Unix-like (/) path separators.
Cleaner Code: Methods like .exists(), .is_file(), and .read_text() simplify common operations.

---

1. Creating a Path Object

The first step is to import the Path class and create an object representing a path on your filesystem.

from pathlib import Path

# Create a Path object
# This path might not exist yet, it's just an object representing it.
p = Path('/home/user/documents/report.txt')


2. Accessing Path Components

Once you have a Path object, you can easily inspect its various parts without any string splitting.

# Get the full file name including the extension
print(f"File Name: {p.name}")

# Get the parent directory
print(f"Parent Directory: {p.parent}")

# Get the file name without the extension
print(f"File Stem: {p.stem}")

# Get the file extension
print(f"File Suffix: {p.suffix}")


3. Checking Path Properties

pathlib makes it trivial to check the status of a path.

# Check if the path exists on the filesystem
print(f"Exists: {p.exists()}")

# Check if it's a file
print(f"Is File: {p.is_file()}")

# Check if it's a directory
print(f"Is Directory: {p.is_dir()}")


4. Manipulating Paths

Modifying paths is clean and intuitive. The / operator is used to join path components, and methods like .rename() handle file operations.

# Join paths using the '/' operator
new_dir = p.parent / 'archive'

# Create a new path by renaming the file
new_path = new_dir / 'old_report.txt'

print(f"New Path: {new_path}")

# To actually rename the file on the filesystem:
# p.rename(new_path)


5. Working with Directories

pathlib provides simple methods for creating and iterating over directories.

# Create a directory (and any necessary parent directories)
# exist_ok=True prevents an error if the directory already exists
archive_dir = Path('/home/user/documents/archive')
archive_dir.mkdir(parents=True, exist_ok=True)

# Find all .txt files in a directory
docs_dir = Path('/home/user/documents')
for file in docs_dir.glob('*.txt'):
print(f"Found File: {file.name}")


---

Putting It All Together: A Complete Example

This noscript will perform all the operations discussed and generate the exact output shown in the prompt. For this to be a runnable example, it first creates a temporary directory structure and files.
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import pathlib
import shutil

# --- Setup: Create a temporary directory and files for the demo ---
# We use a relative path './temp_docs' so it works anywhere
docs_dir = pathlib.Path("temp_docs")
if docs_dir.exists():
shutil.rmtree(docs_dir) # Clean up from previous runs
docs_dir.mkdir()

# Create dummy files
(docs_dir / "report.txt").write_text("This is a test report.")
(docs_dir / "notes.txt").write_text("Some important notes.")
# ----------------------------------------------------------------

# 1. Create a Path object for our report file
file_path = docs_dir / "report.txt"

# 2. Inspect Path Components
print(f"File Name: {file_path.name}")
print(f"Parent Directory: {file_path.parent}")
print(f"File Stem: {file_path.stem}")
print(f"File Suffix: {file_path.suffix}")

# 3. Check Path Properties
print(f"Exists: {file_path.exists()}")
print(f"Is File: {file_path.is_file()}")
print(f"Is Directory: {file_path.is_dir()}")

# 4. Manipulate Paths and prepare for renaming/moving
archive_dir = docs_dir / "archive"
archive_dir.mkdir() # Create the 'archive' subdirectory
new_file_path = archive_dir / "old_report.txt"
print(f"New Path: {new_file_path}")

# To demonstrate renaming, let's rename the original file
file_path.rename(new_file_path)

# 5. Iterate over the original directory to find files
for found_file in sorted(docs_dir.glob("*.txt")):
print(f"Found File: {found_file.name}")

# 6. Demonstrate a copy operation
# `pathlib` itself doesn't have a copy method, but works perfectly with `shutil`
source_file = docs_dir / "notes.txt"
destination_file = archive_dir / "notes_backup.txt"
shutil.copy(source_file, destination_file)
print("File copied successfully!")


# --- Cleanup: Remove the temporary directory ---
shutil.rmtree(docs_dir)
# -----------------------------------------------


This self-contained noscript first sets up a realistic file structure, then demonstrates the power and simplicity of pathlib to inspect, manipulate, and manage files and directories, cleaning up after itself when it's done.

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By: @DataScience4
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Python Tip.

The @dataclass decorator automatically generates standard methods like init, repr, and eq based on the class attributes.

Example 👇

# with dataclass
from dataclasses import dataclass

@dataclass
class Point:
    x: int
    y: int


Here's how it would look without @dataclass:

class Point:
    def __init__(self, x: int, y: int):
        self.x = x
        self.y = y

    def __repr__(self):
        return f"Point(x={self.x}, y={self.y})"

    def __eq__(self, other):
        return (self.x, self.y) == (other.x, other.y)


dataclass saves time and eliminates boilerplate code when a class simply stores data.

👉  @DataScience4
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🏆 Mastering Python Generators

📢 Unlock the power of Python generators! Learn how these memory-efficient iterators yield items on demand with 20 practical examples.

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By: @DataScience4 💛
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✖️ MODIFYING A LIST WHILE LOOPING OVER IT SKIPS ITEMS.

Because of this, Python's iterator gets confused. When you remove an element, the next element shifts into its place, but the loop moves on to the
next index, causing the shifted element to be skipped entirely.

The code looks logical, but the result is buggy — a classic iteration trap.
Correct — iterate over a
copy* of the list, or build a new list.

Follow for more Python tips daily!

# hidden error — removing items while iterating skips elements
numbers = [1, 2, 3, 2, 4, 2, 5]

for num in numbers:
if num == 2:
numbers.remove(num) # seems like it should remove all 2s

# a '2' was skipped and remains in the list!
print(numbers) # [1, 3, 4, 2, 5]


# 🖕 correct version — iterate over a copy
numbers_fixed = [1, 2, 3, 2, 4, 2, 5]

# The [:] makes a crucial copy!
for num in numbers_fixed[:]:
if num == 2:
numbers_fixed.remove(num)

print(numbers_fixed) # [1, 3, 4, 5]

# A more Pythonic way is to use a list comprehension:
# [n for n in numbers if n != 2]


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By: @DataScience4
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Lazy Imports Land in Python and Other Python News for December 2025

📖 PEP 810 brings lazy imports to Python 3.15, PyPI tightens 2FA security, and Django 6.0 reaches release candidate. Catch up on all the important Python news!

🏷️ #community #news
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🔰 For Loop In Python (10 Best Tips & Tricks)

Here are 10 tips to help you write cleaner, more efficient, and more "Pythonic" for loops.

---

1️⃣. Use enumerate() for Index and Value

Instead of using range(len(sequence)) to get an index, enumerate gives you both the index and the item elegantly.

# Less Pythonic 👎
items = ["a", "b", "c"]
for i in range(len(items)):
print(i, items[i])

# More Pythonic 👍
for i, item in enumerate(items):
print(i, item)


---

2️⃣. Use zip() to Iterate Over Multiple Lists

To loop through two or more lists at the same time, zip() is the perfect tool. It stops when the shortest list runs out.

names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]

for name, age in zip(names, ages):
print(f"{name} is {age} years old.")


---

3️⃣. Iterate Directly Over Dictionaries with .items()

To get both the key and value from a dictionary, use the .items() method. It's much cleaner than accessing the key and then looking up the value.

# Less Pythonic 👎
config = {"host": "localhost", "port": 8080}
for key in config:
print(key, "->", config[key])

# More Pythonic 👍
for key, value in config.items():
print(key, "->", value)


---

4️⃣. Use List Comprehensions for Simple Loops

If your for loop just creates a new list, a list comprehension is almost always a better choice. It's more concise and often faster.

# Standard for loop
squares = []
for i in range(5):
squares.append(i * i)
# squares -> [0, 1, 4, 9, 16]

# List comprehension 👍
squares_comp = [i * i for i in range(5)]
# squares_comp -> [0, 1, 4, 9, 16]


---

5️⃣. Use the _ Underscore for Unused Variables

If you need to loop a certain number of times but don't care about the loop variable, use _ as a placeholder by convention.

# I don't need 'i', I just want to repeat 3 times
for _ in range(3):
print("Hello!")


---

6️⃣. Unpack Tuples Directly in the Loop

If you're iterating over a list of tuples or lists, you can unpack the values directly into named variables for better readability.

points = [(1, 2), (3, 4), (5, 6)]

# Unpacking directly into x and y
for x, y in points:
print(f"x: {x}, y: {y}")


---

7️⃣. Use break and a for-else Block

A for loop can have an else block that runs only if the loop completes without hitting a break. This is perfect for search operations.

numbers = [1, 3, 5, 7, 9]

for num in numbers:
if num % 2 == 0:
print("Even number found!")
break
else: # This runs only if the 'break' was never hit
print("No even numbers in the list.")


---

8️⃣. Iterate Over a Copy to Safely Modify

Never modify a list while you are iterating over it directly. This can lead to skipped items. Instead, iterate over a copy.

# This will not work correctly! 👎
numbers = [1, 2, 3, 2, 4]
for num in numbers:
if num == 2:
numbers.remove(num) # Skips the second '2'

# Correct way: iterate over a slice copy [:] 👍
numbers = [1, 2, 3, 2, 4]
for num in numbers[:]:
if num == 2:
numbers.remove(num)
print(numbers) # [1, 3, 4]


---

9️⃣. Use reversed() for Reverse Iteration

To loop over a sequence in reverse, use the built-in reversed() function. It's more readable and efficient than creating a reversed slice.
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# Less readable
items = ["a", "b", "c"]
for item in items[::-1]:
print(item)

# More readable 👍
for item in reversed(items):
print(item)


---

🔟. Use continue to Skip the Rest of an Iteration

The continue keyword ends the current iteration and moves to the next one. It's great for skipping items that don't meet a condition, reducing nested if statements.

# Using 'if'
for i in range(10):
if i % 2 == 0:
print(i, "is even")

# Using 'continue' can be cleaner
for i in range(10):
if i % 2 != 0:
continue # Skip odd numbers
print(i, "is even")


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By: @DataScience4
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0️⃣ Python
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2️⃣ Machine Learning
3️⃣ Data Visualization
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6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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ptpython | Python Tools

📖 An enhanced interactive REPL for Python.

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Pipenv | Python Tools

📖 A dependency and virtual environment manager for Python.

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YOU CAN'T USE LAMBDA LIKE THIS IN PYTHON

The main mistake is turning lambda into a logic dump: adding side effects, print calls, long conditions, and calculations to it.

Such lambdas are hard to read, impossible to debug properly, and they violate the very idea of being a short and clean function. Everything complex should be moved into a regular function. Subscribe for more tips every day !

# you can't do this - lambda with state changes
data = [1, 2, 3]
logs = []

# dangerous antipattern
process = lambda x: logs.append(f"processed {x}") or (x * 10)

result = [process(n) for n in data]

print("RESULT:", result)
print("LOGS:", logs)

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