Use Lists Instead of Tuples for Mutability: While tuples are great for immutable data structures, lists allow you to modify them on the fly. This can save you time and resources in the long run by avoiding unnecessary copying or reallocation of memory. Plus, it’s just more fun to write `my_list[3] = “hello”` instead of `new_tuple = (old_tuple[:3], “hello”, old_tuple[4:])`.

2. Avoid Nested Loops Like the Plague: If you find yourself writing nested loops, take a step back and think about how to refactor your code. Not only do they make it harder for others to understand what’s going on (and for you to remember later), but they can also be incredibly slow when dealing with large datasets or complex algorithms.

3. Use Generators Instead of Lists: When working with iterative processes, generators are your new best friend. They allow you to generate data as needed instead of loading everything into memory at once. This not only saves resources but can also make your code run faster and more efficiently. Plus, it’s just cooler to write `(x for x in range(10) if x % 2 == 0)` than `my_list = [x for x in range(10) if x % 2 == 0]`.

4. Use Functions Instead of Repeating Yourself: If you find yourself writing the same code over and over again, it’s time to create a function! Not only does this make your code more concise and easier to read, but it can also save you time in the long run by avoiding errors or inconsistencies. Plus, it’s just plain fun to write `def add_two(x): return x + 2` instead of copying and pasting that line over and over again.

5. Use Comments Sparingly: While comments can be helpful for explaining complex code or providing context, they should not be used as a crutch for poor coding practices. Instead, focus on writing clear and concise code that speaks for itself. If you find yourself using comments to explain what’s going on in your code, it may be time to rethink your approach.

6. Use Whitespace Liberally: While Python is known for its terse syntax, whitespace can actually make your code more readable and easier to understand. Don’t be afraid to use blank lines or extra spaces between statements to break up complex logic or provide context. Plus, it just looks cooler to write `if x > 10:` than `ifx>10:`.

7. Use Lambda Functions Sparingly: While lambda functions can be useful for simple one-liners, they should not be used as a crutch for poor coding practices. Instead, focus on writing clear and concise code that speaks for itself. If you find yourself using lambdas to avoid writing longer or more complex functions, it may be time to rethink your approach.

8. Use List Comprehensions Sparingly: While list comprehensions can be useful for simple one-liners, they should not be used as a crutch for poor coding practices. Instead, focus on writing clear and concise code that speaks for itself. If you find yourself using list comprehensions to avoid writing longer or more complex loops, it may be time to rethink your approach.

9. Use Dictionaries Sparingly: While dictionaries can be useful for storing key-value pairs, they should not be used as a crutch for poor coding practices. Instead, focus on using other data structures (such as lists or tuples) when appropriate. If you find yourself using dictionaries to avoid writing longer or more complex code, it may be time to rethink your approach.

10. Use Numpy Sparingly: While numpy can be useful for working with large datasets and performing advanced mathematical operations, it should not be used as a crutch for poor coding practices. Instead, focus on using other libraries (such as pandas or scikit-learn) when appropriate. If you find yourself using numpy to avoid writing longer or more complex code, it may be time to rethink your approach.