Python Loops: Four Bad Loop Practices You Must Avoid

Introduction

Regardless of your goal, when you have started learning Python, one of the fundamentals that you must learn well is loops. Both for loops and while loops can be useful and powerful in many scenarios in Python. However, most of us do not always know when and how to use them efficiently. In this article, we are going to look at some bad habits we should all strive to kick when it comes to using loops in Python code.

1. Using a While Loop When a For Loop is More Suitable

While and for loops serve different purposes in Python. While loops are used to create a block of code that repeats as long as the condition is true. For Loops, on the other hand, are used to iterate over a sequence or collection of items. This makes for loops a better choice for iterating over a sequence of items. Let's say you have a list of numbers and you want to extract odd numbers from the list. The worst way to go about it would be to use a while loop. Here is an example of using a while loop:

Even though this code works just fine, we are not using the right loop. This makes our code hard to read. It introduces unnecessary complexity (the index variable that we are incrementing), which increases the likelihood of errors. A for loop is more appropriate since it was specifically designed to iterate over a sequence of items. Here is how the code would look if you used the right loop (for loop):

Look at that! Isn't that beautiful Python code? A for loop is more concise and idiomatic for this task. So, when presented with a task, before writing your code, always take time to think about which loop is more appropriate.

2. Nesting Loops Unnecessarily

Nesting loops can be a powerful feature if you want to iterate over layers of data. But you must have heard the saying that too much of everything is bad. Well, that includes too many nested loops. Excessive nesting can lead to code that is difficult to understand and debug. It's essential to keep the level of nesting to a minimum. If the nesting becomes excessive, refactor code to reduce complexity where possible. Here is an example of an excessive nested loop:

In this code, we are using nested loops to flatten a nested list. This looks overly nested. While this method is effective, it can be streamlined using alternative approaches, like the single itertools.chain function. See the code below:

This is much better than using excess nested loops. So, if you think your for loops are getting over nested, consider other alternatives that Python provides.

50 Days of Data Analysis with Python

According to Forbes, data analysis is one of the high-income skills to learn in 2024. There is no better way to become proficient at data analysis than by getting your hands dirty and tackling some challenges. Start your 50-day journey today.

3. Calculating Array Statistics with For Loops

Let's say you want to calculate the mean and standard deviation of the data. You may be tempted to use a for loop and write much more complicated code. We are going to generate some data below using the random() module and calculate the mean and standard deviation using for loops. We will also use the time module to time the execution time of the code.

In this code, the for loop iterates over each element in the list, calculating the sum and squared_sum while iterating. For large datasets, this may make the code slow. The execution time of this code is 0.038 seconds. We are going to compare this execution time with alternative methods. Another thing to note is that even though this code works, it is hard to read.

Instead of using for loops, you can use NumPy. The np.mean and np.std functions directly calculate the mean and standard deviation.

Instead of iterating over each element in the list, NumPy, on the other hand, utilizes vectorized operations, performing calculations on entire arrays at once. This makes vectorized operations faster than for loops. This operation has taken 0.0070 seconds, which is faster than using for loops. The use of np.mean and np.std functions makes the code easy to read. When working with large datasets, consider using vectorized operations instead of loops.

4. Modifying the Iterated Sequence Within the Loop

Let's say you have a list of numbers, and you want to return a list of odd numbers from the list. The bad practice would be to remove even numbers from a list while iterating over it using a for loop. Here is an example of this bad practice:

This is bad practice because it can lead to unintended behavior due to list modifications during iteration. The issue is that when an element is removed from the list using the remove() method, the list's indices shift, and subsequent iterations in the loop may skip or miss elements due to the changed index positions.

One recommended approach in this situation would be to use list comprehension. See below:

By using list comprehension, we avoid modifying the original list (my_list) during iteration, ensuring correct and predictable behavior.

Conclusion

Loops are powerful, and you should not be scared to use them. The most important thing to remember is that when using loops in your Python code, always strive for clarity, efficiency, and code that is not only functional but also performs well and is easy to understand for yourself and others. You should also remember that loops may not be appropriate for every situation. Thanks for reading this article. Please like, share, and subscribe to this newsletter if you are not yet a subscriber. Join my new YouTube channel to learn more about Python and data analysis.

Master Python Fundamentals

The intention of this book was to simplify the process of mastering the fundamentals of Python. The fundamentals are the building blocks of Python. Learning them is essential for anyone trying to learn Python. Once you master the fundamentals, you can start a 50-day Python challenge journey.

Newsletter Sponsorship

You can reach a highly engaged audience of over 215,000 tech-savvy subscribers and grow your brand with a newsletter sponsorship. Contact me at benjaminbennettalexander@gmail.com today to learn more about the sponsorship opportunities.