💡 Top 10 Python Clean Code Practices

1. Use List Comprehensions for Simple Loops
(Replaces verbose for loops for creating lists.)

Cluttered Way:

squares = []
for i in range(10):
    squares.append(i * i)

Clean Way:

squares = [i * i for i in range(10)]

2. Use enumerate for Index and Value
(Avoids manual index tracking with range(len(...))).)

Cluttered Way:

items = ['a', 'b', 'c']
for i in range(len(items)):
    print(i, items[i])

Clean Way:

items = ['a', 'b', 'c']
for i, item in enumerate(items):
    print(i, item)

3. Use Context Managers for Resources
(Ensures resources like files are properly closed, even if errors occur.)

Cluttered Way:

f = open('my_file.txt', 'w')
try:
    f.write('hello world')
finally:
    f.close()

Clean Way:

with open('my_file.txt', 'w') as f:
    f.write('hello world')

4. Use Dictionary .get() for Safe Key Access
(Prevents KeyError and avoids verbose if key in dict checks.)

Cluttered Way:

my_dict = {'name': 'Alice'}
if 'age' in my_dict:
    age = my_dict['age']
else:
    age = 0

Clean Way:

my_dict = {'name': 'Alice'}
age = my_dict.get('age', 0)

5. Use F-Strings for Formatting
(More readable and often faster than other string formatting methods.)

Cluttered Way:

name = "Bob"
age = 30
message = "Hello, " + name + "! You are " + str(age) + " years old."
# Or: message = "Hello, {}! You are {} years old.".format(name, age)

Clean Way:

name = "Bob"
age = 30
message = f"Hello, {name}! You are {age} years old."

6. Use Unpacking to Swap Variables
(A concise, Pythonic way to swap values without a temporary variable.)

Cluttered Way:

a = 5
b = 10
temp = a
a = b
b = temp

Clean Way:

a = 5
b = 10
a, b = b, a

7. Check for Empty Sequences Correctly
(Leverages Python's "truthiness" for more readable code.)

Cluttered Way:

my_list = []
if len(my_list) == 0:
    print("List is empty!")

Clean Way:

my_list = []
if not my_list:
    print("List is empty!")

8. Use Underscores for Unused Variables
(Signals to other developers that a variable is intentionally ignored.)

Cluttered Way:

# 'i' is created but never used
for i in range(5):
    print("Hello")

Clean Way:

for _ in range(5):
    print("Hello")

9. Chain Comparison Operators
(Makes numerical range checks more intuitive and readable.)

Cluttered Way:

x = 10
if x > 5 and x < 15:
    print("x is between 5 and 15")

Clean Way:

x = 10
if 5 < x < 15:
    print("x is between 5 and 15")

10. Return from a Function Early
(Reduces nesting and improves readability by handling edge cases or invalid states first.)

Cluttered Way:

def process_data(data):
    if data is not None:
        if isinstance(data, list) and len(data) > 0:
            # ... deep nested logic here ...
            print("Processing data...")
            return "Done"
        else:
            return "Error: Invalid data format."
    else:
        return "Error: No data provided."

Clean Way:

def process_data(data):
    if data is None:
        return "Error: No data provided."
    if not isinstance(data, list) or not data:
        return "Error: Invalid data format."

    # ... logic is now at the top level ...
    print("Processing data...")
    return "Done"

#Python #CleanCode #Programming #BestPractices #CodingTips

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By: @DataScience4 ✨

💡 Top 10 More Python Clean Code Practices

1. Use Ternary Operators for Simple Conditionals
(Replaces a multi-line if/else block for simple assignments.)

Cluttered Way:

is_adult = False
age = 20
if age >= 18:
    is_adult = True

Clean Way:

age = 20
is_adult = True if age >= 18 else False

2. Use str.join() for Concatenating Strings in a List
(More efficient and readable than using + in a loop.)

Cluttered Way:

words = ["hello", "world", "this", "is", "python"]
sentence = ""
for word in words:
    sentence += word + " "

Clean Way:

words = ["hello", "world", "this", "is", "python"]
sentence = " ".join(words)

3. Use collections.defaultdict for Grouping or Counting
(Avoids manual key checking when appending to lists or incrementing counters.)

Cluttered Way:

data = [('fruit', 'apple'), ('veg', 'carrot'), ('fruit', 'banana')]
grouped = {}
for category, item in data:
    if category not in grouped:
        grouped[category] = []
    grouped[category].append(item)

Clean Way:

from collections import defaultdict

data = [('fruit', 'apple'), ('veg', 'carrot'), ('fruit', 'banana')]
grouped = defaultdict(list)
for category, item in data:
    grouped[category].append(item)

4. Use collections.namedtuple for Simple Data Objects
(Provides readable attribute access instead of relying on numeric indices.)

Cluttered Way:

point = (10, 20)
# Unclear what point[0] or point[1] represents
if point[0] > 5:
    print("X is greater than 5")

Clean Way:

from collections import namedtuple

Point = namedtuple('Point', ['x', 'y'])
point = Point(10, 20)
if point.x > 5:
    print("X is greater than 5")

5. Use Argument Unpacking (* and **)
(Passes all items from a list or dictionary as arguments to a function.)

Cluttered Way:

def print_coords(x, y, z):
    print(f"X: {x}, Y: {y}, Z: {z}")

coords = [1, 2, 3]
print_coords(coords[0], coords[1], coords[2])

Clean Way:

def print_coords(x, y, z):
    print(f"X: {x}, Y: {y}, Z: {z}")

coords_list = [1, 2, 3]
print_coords(*coords_list)

coords_dict = {'x': 4, 'y': 5, 'z': 6}
print_coords(**coords_dict)

6. Use any() and all() for Boolean Checks on Iterables
(More declarative and concise than manual loops with a flag variable.)

Cluttered Way:

numbers = [-1, -2, 5, -4]
has_positive = False
for num in numbers:
    if num > 0:
        has_positive = True
        break

Clean Way:

numbers = [-1, -2, 5, -4]
has_positive = any(num > 0 for num in numbers)

7. Prefer Generator Expressions for Large Datasets
(They don't store the entire sequence in memory, making them highly efficient.)

Cluttered Way (can cause high memory usage):

total = sum([i * i for i in range(1000000)])

Clean Way (memory efficient):

total = sum(i * i for i in range(1000000))

8. Use Destructuring for More Powerful Unpacking
(A clean way to assign elements from a sequence to multiple variables.)

Cluttered Way:

numbers = [1, 2, 3, 4, 5]
first = numbers[0]
last = numbers[-1]

Clean Way:

numbers = [1, 2, 3, 4, 5]
first, *middle, last = numbers
# first = 1, middle = [2, 3, 4], last = 5

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 ✨

✨ few-shot learning | AI Coding Glossary ✨

📖 A setting where a model adapts to a new task using only a small number of labeled examples.

🏷️ #Python

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 ✨

✨ Quiz: Python MarkItDown: Convert Documents Into LLM-Ready Markdown ✨

📖 Practice MarkItDown basics. Convert PDFs, Word documents, Excel documents, and HTML documents to Markdown. Try the quiz.

🏷️ #intermediate #ai #tools

✨ Python 3.14 Released and Other Python News for November 2025 ✨

📖 Python 3.14 is officially out, Python 3.15 begins, and Python 3.9 reaches end of life. Plus, Django 6.0 first beta released, new PEPs, and more Python news.

🏷️ #community #news

✨ reasoning model | AI Coding Glossary ✨

📖 A generative model tuned to solve multi-step problems.

🏷️ #Python

✨ chain of thought (CoT) | AI Coding Glossary ✨

📖 A prompting technique that asks models to show intermediate steps, often improving multi-step reasoning but not guaranteeing accurate explanations.

🏷️ #Python

🏆 Mastering Python Clean Code: 150 Key Principles

📢 Elevate your Python skills! Dive into 150 Clean Code principles to write truly readable and maintainable code for any project.

⚡ Tap to unlock the complete answer and gain instant insight.

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By: @DataScience4 ✨

❔ Interview Question

What is the potential pitfall of using a mutable object (like a list or dictionary) as a default argument in a Python function?

Answer: A common pitfall is that the default argument is evaluated only once, when the function is defined, not each time it is called. If that default object is mutable, any modifications made to it in one call will persist and be visible in subsequent calls.

This can lead to unexpected and buggy behavior.

Incorrect Example (The Pitfall):

def add_to_list(item, my_list=[]):
    my_list.append(item)
    return my_list

# First call seems to work fine
print(add_to_list(1))  # Output: [1]

# Second call has unexpected behavior
print(add_to_list(2))  # Output: [1, 2] -- The list from the first call was reused!

# Third call continues the trend
print(add_to_list(3))  # Output: [1, 2, 3]

The Correct, Idiomatic Solution:

The standard practice is to use None as the default and create a new mutable object inside the function if one isn't provided.

def add_to_list_safe(item, my_list=None):
    if my_list is None:
        my_list = []  # Create a new list for each call
    my_list.append(item)
    return my_list

# Each call now works independently
print(add_to_list_safe(1))  # Output: [1]
print(add_to_list_safe(2))  # Output: [2]
print(add_to_list_safe(3))  # Output: [3]

tags: #Python #Interview #CodingInterview #PythonTips #Developer #SoftwareEngineering #TechInterview

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By: @DataScience4 ✨

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🏷️ #basics #best-practices #python

Python Interview Codes Cheatsheet

https://t.me/CodeProgrammer

Python tip:
Use f-strings for easy and readable string formatting.

name = "Alice"
age = 30
message = f"Hello, my name is {name} and I am {age} years old."
print(message)

Python tip:
Utilize list comprehensions for concise and efficient list creation.

numbers = [1, 2, 3, 4, 5]
squares = [x * x for x in numbers if x % 2 == 0]
print(squares)

Python tip:
Use enumerate() to iterate over a sequence while also getting the index of each item.

fruits = ["apple", "banana", "cherry"]
for index, fruit in enumerate(fruits):
    print(f"{index}: {fruit}")

Python tip:
Use zip() to iterate over multiple iterables in parallel.

names = ["Alice", "Bob"]
ages = [25, 30]
for name, age in zip(names, ages):
    print(f"{name} is {age} years old.")

Python tip:
Always use the with statement when working with files to ensure they are properly closed, even if errors occur.

with open("example.txt", "w") as f:
    f.write("Hello, world!\n")
    f.write("This is a test.")
# File is automatically closed here

Python tip:
Use *args to allow a function to accept a variable number of positional arguments.

def sum_all(*args):
    total = 0
    for num in args:
        total += num
    return total

print(sum_all(1, 2, 3))
print(sum_all(10, 20, 30, 40))

Python tip:
Use **kwargs to allow a function to accept a variable number of keyword arguments (as a dictionary).

def display_info(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

display_info(name="Bob", age=40, city="New York")

Python tip:
Employ defaultdict from the collections module to simplify handling missing keys in dictionaries by providing a default factory.

from collections import defaultdict

data = [("fruit", "apple"), ("vegetable", "carrot"), ("fruit", "banana")]
categorized = defaultdict(list)
for category, item in data:
    categorized[category].append(item)
print(categorized)

Python tip:
Use if __name__ == "__main__": to define code that only runs when the script is executed directly, not when imported as a module.

def greet(name):
    return f"Hello, {name}!"

if __name__ == "__main__":
    print("Running directly as a script.")
    print(greet("World"))
else:
    print("This module was imported.")

Python tip:
Apply type hints to your code for improved readability, maintainability, and to enable static analysis tools.

def add(a: int, b: int) -> int:
    return a + b

result: int = add(5, 3)
print(result)

#PythonTips #PythonProgramming #PythonForBeginners #PythonTricks #CodeQuality #Pythonic #BestPractices #LearnPython

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By: @DataScience4 ✨

✨ Editorial Guidelines ✨

📖 See how Real Python's editorial guidelines shape comprehensive, up-to-date resources, with Python experts, educators, and editors refining all learning content.

🏷️ #Python

✨ Topic: Python Basics ✨

📖 Begin your Python journey with these beginner-friendly tutorials. Learn fundamental Python concepts to kickstart your career. This foundation will equip you with the necessary skills to further advance your budding Python programming skills.

🏷️ #357_resources

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📖 We welcome ideas, suggestions, feedback, and the occasional rant. Did you find a topic confusing? Or did you find an error in the text or code? Send us your feedback via this page.

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