🏆 Mastering Python Clean Code: 150 Key Principles

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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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Python Interview Codes Cheatsheet

https://t.iss.one/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

✨ Send Feedback ✨

📖 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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❔ Interview question

How do you create a new directory using the os module in Python, and what is the recommended way to handle cases where the directory might already exist?

Answer: The primary function to create a new directory (and any necessary parent directories) is os.makedirs(). To gracefully manage situations where the target directory might already exist without causing a FileExistsError, the recommended approach is to set the exist_ok parameter to True. This ensures that if the directory already exists, no exception is raised, allowing your program to continue execution smoothly. An example usage would be os.makedirs('path/to/my/new_directory', exist_ok=True).

tags: #interview #os #PythonBasics #FileSystem

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By: @DataScience4 ✨

🐍 Python: Unobvious and Probable

Python, for all its readability and clear syntax, holds a treasury of less-trodden paths and nuanced behaviors that can catch even seasoned developers off guard. Understanding these intricacies deepens one's mastery and illuminates the language's design philosophy.

The Enigma of the ~ Operator: Bitwise NOT

Often overlooked outside of bit manipulation contexts, the unary ~ operator performs a bitwise NOT operation. For integers, its behavior can seem counter-intuitive at first glance.

Mathematically, ~x is equivalent to -(x+1).

x = 5
result = ~x
print(f"~{x} is {result}") # Output: ~5 is -6

y = -10
result = ~y
print(f"~{y} is {result}") # Output: ~-10 is 9

This behavior stems from how negative numbers are represented in two's complement form within computers. While its primary role is in low-level bitwise operations, it finds practical use in libraries like NumPy for inverting boolean arrays or selections, where ~ acts as a logical NOT.

import numpy as np

arr = np.array([True, False, True])
inverted_arr = ~arr
print(f"Original: {arr}, Inverted: {inverted_arr}") # Output: Original: [ True False  True], Inverted: [False  True False]

Its unobvious integer arithmetic hides a powerful, foundational operation.

all() and any() with Empty Sequences

The built-in functions all() and any() are crucial for evaluating the truthiness of elements within an iterable. Their behavior when faced with an empty sequence, however, is a classic source of mild confusion.

• all(iterable) returns True if all elements of the iterable are truthy (or if the iterable is empty).
• any(iterable) returns True if any element of the iterable is truthy (and False if the iterable is empty).

empty_list = []
print(f"all({empty_list}) is {all(empty_list)}") # Output: all([]) is True
print(f"any({empty_list}) is {any(empty_list)}") # Output: any([]) is False

truthy_list = [1, True, 'hello']
print(f"all({truthy_list}) is {all(truthy_list)}") # Output: all([1, True, 'hello']) is True
print(f"any({truthy_list}) is {any(truthy_list)}") # Output: any([1, True, 'hello']) is True

mixed_list = [0, 1, '', True]
print(f"all({mixed_list}) is {all(mixed_list)}") # Output: all([0, 1, '', True]) is False
print(f"any({mixed_list}) is {any(mixed_list)}") # Output: any([0, 1, '', True]) is True

The result all([]) being True is an example of a "vacuously true" statement: there are no falsy elements in an empty list, so the condition "all elements are truthy" holds. This design prevents unexpected errors in loops or conditional checks where an empty sequence might otherwise break logic. any([]) being False is straightforward: there are no elements to be truthy.

The is vs. == for Small Integers and Strings

Python has two primary ways to check for equality: == (value equality) and is (identity equality, checking if two variables refer to the exact same object in memory). While is is generally reserved for None or specific memory optimizations, CPython exhibits an unobvious caching behavior for certain immutable objects.

a = 100
b = 100
print(f"a == b: {a == b}") # Output: a == b: True
print(f"a is b: {a is b}") # Output: a is b: True (for integers -5 to 256)

c = 300
d = 300
print(f"c == d: {c == d}") # Output: c == d: True
print(f"c is d: {c is d}") # Output: c is d: False (for integers outside -5 to 256)

s1 = "hello"
s2 = "hello"
print(f"s1 is s2: {s1 is s2}") # Output: s1 is s2: True (string interning for short, simple strings)

s3 = "hello world!"
s4 = "hello world!"
print(f"s3 is s4: {s3 is s4}") # Output: s3 is s4: False (interring not guaranteed for complex strings)

CPython pre-allocates and caches integer objects in the range of -5 to 256. Similarly, short, simple string literals are often "interned" for performance. This means that multiple references to these specific values will point to the same object in memory, making is return True. This is an implementation detail and should not be relied upon for general equality checks, where == is the correct semantic choice.

Mutable Default Arguments

A common pitfall for new and experienced developers alike arises from mutable objects used as default arguments in function definitions. Default arguments are evaluated once when the function is defined, not on each call.

def add_item_to_list(item, data=[]):
    data.append(item)
    return data

list1 = add_item_to_list(1)
print(f"List 1: {list1}") # Output: List 1: [1]

list2 = add_item_to_list(2)
print(f"List 2: {list2}") # Output: List 2: [1, 2] - Unobvious! `data` is the same list object as before.

list3 = add_item_to_list(3, []) # Passed a new list
print(f"List 3: {list3}") # Output: List 3: [3]
print(f"List 2 after List 3: {list2}") # Output: List 2 after List 3: [1, 2] - Unchanged.

The "unobvious" part is that data in the list2 call is the same list object that was modified by list1. The standard workaround is to use None as a sentinel value:

def add_item_to_list_safe(item, data=None):
    if data is None:
        data = []
    data.append(item)
    return data

list4 = add_item_to_list_safe(1)
print(f"List 4 (safe): {list4}") # Output: List 4 (safe): [1]

list5 = add_item_to_list_safe(2)
print(f"List 5 (safe): {list5}") # Output: List 5 (safe): [2] - Now as expected.

Chained Comparisons

Python allows for elegant chained comparisons, which can sometimes surprise those accustomed to other languages that require explicit logical operators (and, &&).

x = 7

# Traditional (and verbose)
if 0 < x and x < 10:
    print("x is between 0 and 10 (exclusive) - traditional")

# Python's elegant chained comparison
if 0 < x < 10:
    print("x is between 0 and 10 (exclusive) - chained")

# More complex chaining
a, b, c = 1, 2, 3
if a < b == c:
    print("a is less than b, and b is equal to c") # False, since b != c

This unobvious syntactic sugar evaluates from left to right, short-circuiting if any comparison is false. It is equivalent to (0 < x) and (x < 10) but offers a cleaner, more mathematical notation.

The Walrus Operator (:=)

Introduced in Python 3.8, the assignment expression operator :=, informally known as the "walrus operator," allows you to assign a value to a variable as part of an expression. This can lead to more concise code in situations where you would otherwise repeat an expression or assign it on a separate line.

# Without walrus
data = [1, 2, 3]
n = len(data)
if n > 0:
    print(f"List has {n} items")

# With walrus
if (n := len(data)) > 0:
    print(f"List has {n} items")

# In a loop condition
records = [("Alice", 30), ("Bob", 25), ("Charlie", 35)]
processed_records = []
while (record := records.pop()) if records else None: # Unobvious but powerful loop condition
    processed_records.append(record)
    print(f"Processing: {record}")
print(f"Processed all: {processed_records}")

The := operator enables patterns that are less common in earlier Python versions, making code more dense and, at times, more efficient by avoiding redundant computations, but it requires a slightly different way of thinking about expressions.

Conclusion

Python's journey from a simple scripting language to a powerhouse for diverse applications has imbued it with a rich set of features. Exploring these unobvious behaviors, from the mathematical elegance of ~ and the logical quirks of all() with empty sequences to the subtle optimizations of object caching and the syntactic conciseness of chained comparisons and the walrus operator, strengthens a developer's grasp of the language's core. These nuances are not merely trivia; they are cornerstones for writing robust, efficient, and truly Pythonic code.

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tags: python, programming, unobvious, nuances, features, operators, all, any, bitwise, walrus, is, equals, mutable defaults

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By: @DataScience4 ✨

✨ The Python Standard REPL: Try Out Code and Ideas Quickly ✨

📖 The Python REPL gives you instant feedback as you code. Learn to use this powerful tool to type, run, debug, edit, and explore Python interactively.

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🏆 Connecting Python to MySQL: `mysql-connector`

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By: @DataScience4 ✨

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By: @DataScience4 ✨

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