5 Common Refactors in Python for Beginners

#python #beginners

Refactoring helps make your code cleaner and more efficient. Here are five common refactors for beginners in Python.

I. Simplifying Boolean Expressions

A common pattern is using an if-else block just to return True or False. For example:

if condition:
    return True
else:
    return False

Refactor it to:

return condition

The condition itself is already a Boolean expression, so the if-else block is unnecessary. By directly returning the condition, the code becomes shorter and more readable. This is a simple but effective way to improve clarity without changing the functionality.

II. List Comprehensions Instead of for / if

Beginners often use for loops with if statements to build lists. For example:

result = []
for item in items:
    if condition(item):
        result.append(item)

Refactor it to a list comprehension:

result = [item for item in items if condition(item)]

List comprehensions provide a more concise way to construct lists. They are also typically faster than equivalent for loops because they are optimized internally by Python. This approach is easier to read as well, especially for simple list creation tasks.

III. Avoid Repeating Calculations

If you call the same function multiple times in a loop, store the result in a variable. For example:

for item in items:
    if len(item) > 5:
        result.append(item)
...

Refactor it to:

for item in items:
    len = len(item)
    if len > 5:
        result.append(item)
...

Imagine if this condition held in multiple elif or nested if statements. Here, the len(item) is called twice for each iteration, which can be inefficient, especially for large lists. Storing the result of len(item) in a variable (len) eliminates the repeated calculation, improving performance and making the code cleaner. This is a basic example.

IV. Lambda and Built-in Methods

Instead of writing explicit loops, use Python’s built-in functions like map() and filter(), which can be more efficient and concise. For example, to double each item in a list:

result = []
for item in items:
    result.append(item * 2)

Refactor it to:

result = list(map(lambda x: x * 2, items))

Or to filter items greater than 5:

result = []
for item in items:
    if item > 5:
        result.append(item)

Refactor it to:

result = list(filter(lambda x: x > 5, items))

Both map() and filter() take functions as arguments, so we can use lambda to define small anonymous functions. The lambda function is a concise way to define simple operations. For example, lambda x: x * 2 creates a function that multiplies x by 2. The benefit of map() and filter() is that they are often more efficient than using a for loop and are typically more readable. One could also use list comprehensions (see above).

V. Combine Multiple if Statements

When checking multiple conditions, combining them with logical operators (and, or) can simplify your code. For example:

if a > 0:
    if b > 0:
        result = a + b

Refactor it to:

if a > 0 and b > 0:
    result = a + b

This reduces unnecessary nesting and makes the code easier to read and maintain. Combining conditions into one if statement makes the flow of logic clearer and eliminates redundancy.

Give It a Shot

How do you feel about refactoring control flow and utilizing built in methods? Take the following code and see if you can refactor:

count = {}
sum = {}
    for team, items in dict().items():
        for player in items["players"]:
            brand = player["shoe_brand"]

            if brand not in count.keys():
                count[brand] = 1
            else:
                count[brand] += 1

            if brand not in sum.keys():
                sum[brand] = player["rebounds_per_game"]
            else:
                sum[brand] += player["rebounds_per_game"]

Here we have two dictionaries where we need to set the initial values. Using the tactics in the article, you should see that both if statements can be combined using a logical operator. Go a step further and it might stand out that when one dictionary lacks an initial property, so does the other and neither is incremented (/aggregated) before the other is initialized. So we may refactor this code as follows since it would seem one condition is both sufficient and necessary for the other to obtain before any incrementing takes place, avoiding an error:

        if brand not in count:
            count[brand] = 1
            sum[brand] = player["rebounds_per_game"]
        else:
            count[brand] += 1
            sum[brand] += player["rebounds_per_game"]

Can we refactor further? Think about using a built-in method...

        count[brand] = count.get(brand, 0) + 1
        sum[brand] = sum.get(brand, 0) + player["rebounds_per_game"]

Conclusion

Refactoring is about making your code shorter, clearer, and more efficient without changing what it does. By simplifying Boolean expressions, using list comprehensions, avoiding repeated calculations, leveraging built-in functions like map() and filter(), and merging conditions, you can make your code DRY. Using lambda allows you to define small functions in a single line, keeping the code neat and fast. These practices not only improve performance but also enhance readability, which is crucial for maintaining code in the long run.