Introduction
Most companies, when recruiting new software engineers, include at least one coding interview as part of their selection process. And why not? We’re software engineers, after all; writing (and reading) code is what we do.
Yet, even for the seasoned Pythonista, there is one coding interview that often feels disproportionately — and unnecessarily — stressful: the data structures and algorithms (DSA) interview.
Most of this stress arises from unfamiliarity and nothing else. When was the last time your boss, or the CEO, or anyone, asked you to calculate the nth number in a Fibonacci sequence or traverse a binary tree orimplement a hash table from scratch? The answer is never.
As academic and irrelevant as DSA challenges may seem when compared to the actual day-to-day problem solving that goes on in most software developer jobs, they are still important for at least three reasons:
- Having a solid grasp of data structures and algorithms will make you a better developer.
- Many of the big companies that heavily recruit Python talent — Meta, Netflix, Google, Amazon, Bloomberg — still prioritize DSA questions in coding interviews.
- They’re fun. (Really).
The Bites in the Python Coding Interview Learning Path offer a selection of commonly encountered challenges, arranged in increasing order of difficulty.
Before we begin, we will briefly discuss algorithmic time complexity, a topic that often arises in the context of coding interviews.
A Gentle Introduction to Big O
Big O notation is used to describe the computational complexity of an algorithm. It helps us understand how the algorithm’s performance changes as the input size increases.
In simple terms, Big O notation tells us how long an algorithm takes to execute based on the number of elements it needs to handle.
Big O Complexity Scale
- O(1) -> Constant time; fastest. Algorithm runtime is constant, regardless of input size.
- O(log n) -> Logarithmic time. Faster than linear time, but still grows as n increases. O(log n) is not very common.
- O(n log n) -> Quasi-linear time. More common than O(log n).
- O(n) -> Linear time. Runtime doubles as input doubles.
- O(n^2) -> Exponential time; slowest (generally speaking). Runtime squares as input doubles.
Big O Applied to Python Data Structures
- Lists: Suppose we have a list of n elements, and we want to search for a specific value in that list. In the worst-case scenario, where the value is at the end of the list or not present at all, we would need to traverse the entire list. This operation has a time complexity of O(n), as the time taken to search grows linearly with the number of elements in the list.
- Sets: Imagine we have a set with n elements, and we want to check if a specific value exists in the set. Sets use a hashing mechanism for quick lookups, resulting in an average time complexity of O(1) for membership tests. This means that regardless of the size of the set, the time taken to find an element remains constant on average.
- Dictionaries: If we have a dictionary with n key-value pairs and we want to search for a specific key, dictionaries use a similar hashing mechanism to sets. The average time complexity for searching a key in a dictionary is also O(1). However, it’s important to note that in the worst-case scenario, where there are hash collisions or other factors, the time complexity can degrade to O(n). Therefore, it’s good practice to consider the average case when discussing dictionary lookups.
In closing, while the interview process for software jobs may seem daunting, it is important to approach it with the right mindset and adequate preparation. There is nothing intrinsically magical about DSA problems or the people who do well in DSA interviews. The only difference between them, and you, is practice.
Check them out here on the Pybites Platform: https://codechalleng.es/bites/paths/interviewing