Python is elegant, expressive, and powerful. Whether you're new to programming or an experienced engineer, Python never stops offering ways to make your code more concise, readable, and performant.

Here’s a definitive list of 100 practical tips and tricks—from foundational syntax to powerful patterns—that will elevate your Python development game from 0 to 100.

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🧩 0–20: Basics Every Developer Should Know

1.Multiple Variable Assignment

a, b, c = 1, 2, 3

2.Swapping Variables

a, b = b, a

3.List Comprehension

squares = [x**2 for x in range(10)]

4.Inline If Statement

status = "Success" if score > 60 else "Fail"

5.Using enumerate() in loops

for idx, val in enumerate(my_list):
    print(idx, val)

6.The zip() Function

for a, b in zip(list1, list2):
    print(a, b)

7.Use join() to Concatenate Strings

", ".join(["Python", "Java", "C++"])

8.Readable Large Numbers

population = 1_000_000

9.Slicing Lists

my_list[::-1]  # Reverses the list

10.The Walrus Operator (:=)

if (n := len(data)) > 10:
    print(f"{n} items found.")

11.Truth Value Testing

if not my_list:  # Checks for empty list
    print("List is empty")

12.Using type() for Debugging

print(type(variable))

13.Set for Unique Values

unique = set(my_list)

14.F-Strings for Clean Formatting

name = "Alice"
print(f"Hello, {name}")
  1. List Unpacking 
head, *body, tail = [1, 2, 3, 4, 5]

16.Copying Lists Safely

new_list = old_list[:]

17.Using any() and all()

any([False, True, False])  # True
all([True, True, True])    # True

18.Default Dictionary Values

from collections import defaultdict
d = defaultdict(int)
d["missing_key"] += 1  # No KeyError

19.List Flattening

nested = [[1, 2], [3, 4]]
flat = [item for sublist in nested for item in sublist]

20.Using Counter

from collections import Counter
count = Counter("hello world")
print(count)

🔧 21–50: Intermediate Power Moves

21.Use get() for Safer Dict Access

22.Unpack Dictionary with ` in Functions**
23.**Use pathlib Over os.path**
24.**Named Tuples for Clean Code**
25.**Set Operations: Union, Intersection**
26.**Dict Comprehension**
27.**Generator Expressions for Memory Efficiency**
28.**Using with open() for Safe File Handling**
29.**Using timeit for Benchmarking Code**
30.**Lambda Functions with map() and filter()`**

31.Simple HTTP Server

```bash
python -m http.server
```

32.Creating Your Own Iterator

33.Comprehensions with Conditions

34.Using is vs ==

35.Understanding Python's None

36.Using globals() and locals()

37.Using _ in the Interpreter

38.Exception Handling Best Practices

39.try...else Usage

40.Chaining Comparisons

41.Function Annotations

42.Duck Typing in Practice

43.Using vars() for Objects

44.Using assert for Debugging

45.Inspect Module for Runtime Introspection

46.Creating Decorators

47.Memoization with lru_cache

48.Using itertools for Advanced Iteration

49.Using dataclass to Simplify Classes

50.Context Managers with contextlib

🧠 51–80: Advanced and Pythonic Patterns

51.Custom Generators with yield

52.Custom Context Managers

53.Chained Function Calls

54.Using __slots__ to Save Memory

55.Using Abstract Base Classes (abc)

56.Type Hinting for Better Maintenance

57.Function Factories

58.Dynamic Attribute Setting with setattr()

59.Property Decorators for Controlled Access

60.MetaProgramming with type()

61.Using Descriptors

62.Using __repr__ vs __str__

63.Monkey Patching (Use with Care)

64.Handling Circular Imports

65.Contextlib’s suppress()

66.Using __call__() in Classes

67.Using Generators for Pipelines

68.Understanding the GIL (Global Interpreter Lock)

69.Use __future__ for Forward Compatibility

70.Creating Plugins with importlib

71.Dynamic Imports

72.Using sys.setrecursionlimit() Safely

73.Debugging with pdb

74.Multithreading vs Multiprocessing

75.Using subprocess for Shell Commands

76.Understanding Closures and Scope

77.Partial Functions with functools.partial

78.Event-Driven Programming with asyncio

79.Writing DSLs with Python

80.Python’s Eval/Exec (Use with Caution)

🛡️ 81–100: Performance, Security, and Best Practices

81.Memory Profiling with memory_profiler

82.Code Profiling with cProfile

83.Use black for Code Formatting

84.Use mypy for Type Checking

85.Avoid Mutable Defaults in Functions

86.Secure Coding with secrets over random

87.Environment Variables with os.getenv()

88.Configuration with .env and python-dotenv

89.Use Virtual Environments Always

90.Use Dependency Managers like poetry or pipenv

91.Logging Instead of Printing

92.Use try/except/finally Blocks

93.Watch for Race Conditions in Threads

94.Secure File Handling

95.Limit Resource Usage in Scripts

96.Create Command Line Interfaces with argparse / click

97.Test Your Code with unittest or pytest

98.Use Linters like flake8 or pylint

99.Avoid Overusing Global Variables

100.Document Everything with Docstrings and Comments

🎯 Final Thoughts

Python is a journey of continuous refinement. These 100 tips are more than shortcuts—they represent years of community wisdom. Embrace Pythonic thinking. Write clear, clean, and impactful code.

If you found this article valuable, connect with me on GitHub and Dev.to.

Let’s keep building meaningful things, one line of Python at a time.