30 Ways to Write More Pythonic Code and Improve Your Job Readiness (part 2 of 6)

Python is widely celebrated for its readability, simplicity, and elegance. However, writing functional code is just the beginning. If you're looking to elevate your coding skills and stand out in technical interviews, mastering Pythonic patterns will provide you with a competitive edge.

In this second installment of our six-part series, we’ll continue exploring practical ways to write cleaner, more efficient, and more readable Python code. By adopting these best practices, you’ll not only enhance the maintainability of your projects but also improve your problem-solving capabilities in real-world applications and coding interviews.

Missed the first part? Check it out! - https://hashnode.com/post/cm738aso3000509l71xtg4sc9

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6. Dictionary Comprehension

Non-Pythonic

squared_numbers = {}
for x in range(10):
    squared_numbers[x] = x**2

Pythonic

squared_numbers = {x: x**2 for x in range(10)}

Why?

• More concise and easier to read.

How it works?

In the Pythonic approach, dictionary comprehension is used. This concise syntax combines the loop and dictionary creation into a single readable expression. It improves code clarity and efficiency because dictionary comprehensions are implemented in C within Python’s internals, making them faster than manually appending values in a loop.

Time Complexity

Both approaches have an O(n) time complexity, as they iterate over range(n), performing O(1) insertions per element.

Benchmarking

• Pythonic approach (Dictionary Comprehension): 12.349122 seconds

• Non-Pythonic approach (For Loop with Manual Assignment): 12.004668 seconds

The manual approach was slightly faster because dictionary comprehension introduces a small overhead by constructing a temporary object before inserting items, whereas the manual loop inserts values directly into the dictionary. Additionally, CPython optimizations for for loops may improve performance for direct key-value assignments. However, the performance difference (~3%) is negligible, and dictionary comprehension remains the preferred choice due to its cleaner syntax and better maintainability.

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7. Using get() for Dictionary Lookup

Non-Pythonic

if "key" in my_dict:
    value = my_dict["key"]
else:
    value = "default"

Pythonic

value = my_dict.get("key", "default")

Why?

• Avoids explicit if condition.

• get() is optimized and more readable.

How it works?

In the Pythonic approach, the built-in .get() method is used. This method directly retrieves the value of "key" from my_dict. If the key exists, its value is returned; if it does not exist, the second argument ("default") is returned instead. This approach eliminates the need for an explicit conditional check, making the code cleaner, more readable, and optimized.

Time Complexity

The non-Pythonic approach requires two O(1) lookups—one to check if the key exists and another to retrieve its value—leading to unnecessary redundancy. In contrast, the Pythonic approach using .get() performs a single O(1) lookup, making it slightly more efficient if the key exists.

Benchmarking

• Pythonic approach (get) - Existing Key: 0.501676 seconds

• Pythonic approach (get) - Non-Existing Key: 0.378200 seconds

• Non-Pythonic approach (if in) - Existing Key: 0.731654 seconds

• Non-Pythonic approach (if in) - Non-Existing Key: 0.331082 seconds

The Pythonic approach using get() is slightly slower for missing keys because it performs additional internal operations to handle the default value, while if key in my_dict short-circuits as soon as it confirms non-existence. Additionally, get() introduces a function call overhead, whereas if key in my_dict directly accesses the dictionary’s hash table and exits faster when the key is missing.

The get() method is faster when the key exists because it performs only one lookup (O(1)) in the dictionary’s hash table, whereas if key in my_dict requires two lookups (O(1) + O(1))—one to check for the key’s existence and another to retrieve the value.

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8. Using set() to Remove Duplicates from a List

Non-Pythonic

unique_items = []
for item in my_list:
    if item not in unique_items:
        unique_items.append(item)

Pythonic

unique_items = list(set(my_list))

Why?

• set() automatically removes duplicates.

• More efficient, as in checks are O(1) for sets (instead of O(n) for lists).

How it works?

In the Pythonic approach, the set() function is used to automatically remove duplicates, as sets in Python store only unique values. Converting my_list into a set eliminates duplicates efficiently in O(n) time. The final list(set(my_list)) conversion restores the original list format but with only unique elements.

Time Complexity

The non-Pythonic approach has a worst-case time complexity of O(n²) because each element undergoes an O(n) membership check before being appended, making it inefficient for large lists. In contrast, the Pythonic approach using set() reduces the complexity to O(n) by leveraging hash tables, where membership checks run in O(1). Converting the set back to a list is also O(n), making the overall process significantly faster while keeping the code cleaner and more readable.

Benchmarking

• Pythonic approach (set()): 0.031786 seconds

• Non-Pythonic approach (for loop with if not in): 2.069776 seconds

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9. Using join() to Concatenate Strings

Non-Pythonic

result = ""
for word in words:
    result += word + " "

Pythonic

result = " ".join(words)

Why?

• Using + for string concatenation inside a loop is inefficient (O(n²) complexity).

• join() is optimized and much faster (O(n) complexity).

How it works?

In the Pythonic approach, the built-in " ".join(words) method is used. Instead of creating multiple new string objects, join() efficiently concatenates all words in a single pass, reducing memory overhead and improving performance.

Time Complexity

The non-Pythonic approach has an O(n²) time complexity because Python strings are immutable, meaning each += operation creates a new string and copies all previous characters, leading to excessive memory usage and slow performance. In contrast, the Pythonic approach using .join() has O(n) complexity as it pre-allocates memory and concatenates all strings in a single pass, making it significantly faster and more efficient

Benchmarking

• Pythonic approach (join()): 0.108995 seconds

• Non-Pythonic approach (+= in loop): 1.898809 seconds

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10. Using with for File Handling

Non-Pythonic

file = open("file.txt", "r")
content = file.read()
file.close()

Pythonic

with open("file.txt", "r") as file:
    content = file.read()

Why to use?

• with automatically closes the file, even if an error occurs.

• It reduces the risk of forgetting to close a file.

• It makes the code cleaner, more readable, and more maintainable.

• In real-world applications, the slight overhead is negligible compared to safety benefits.

How it works?

In the Pythonic approach, the with statement is used to handle the file. When the with block is entered, the file is opened, and when the block exits—regardless of whether an error occurs—Python automatically closes the file. This makes the code cleaner, more readable, and safer by ensuring proper resource management.

Time Complexity

Both approaches have an O(1) time complexity for opening and closing files.

Benchmarking

• Pythonic approach (with 140K characters - run 10K times): 6.070539 seconds

• Non-Pythonic approach (Manual open/close - 140K characters - run 10K times): 5.775945 seconds

The with open() approach was slightly slower due to the context manager overhead, which adds a small setup and teardown cost when handling the file. In contrast, manually calling file.close() may benefit from internal CPython optimizations, making it slightly faster. However, the performance difference (~5%) is minimal and only noticeable in high-frequency benchmarks.

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Time Complexity & Benchmarking Analysis

Conclusion

In Part 2 of 6 of this blog series, we explored Pythonic ways to handle common programming tasks, covering techniques 6 to 10:

• Dictionary comprehensions improve readability but may introduce minor overhead.

• set() dramatically improves duplicate removal performance, reducing complexity from O(n²) to O(n).

• .join() is significantly faster for string concatenation, avoiding O(n²) inefficiencies from repeated string copying.

• with open() ensures safe file handling, though it has a minor (~5%) overhead due to context management.

• .get() is faster for existing keys but slightly slower for missing keys due to handling default values.

These Pythonic approaches enhance readability, improve performance, and promote best practices, making code more concise, efficient, and maintainable.

See you in the 3nd article of a series of 6.

Thanks!