Now more than ever, it’s essential to have a good understanding of algorithms to succeed in coding interviews. Unfortunately, many developers are taught algorithms but not how or why they work.
Today, we will go over the fundamentals of algorithms in Python and walk through some of the most useful algorithms for coding interviews.
What you will learn today:
Prepare for Python coding interviews by learning all the algorithms expected of you in an interview.
Algorithms for Coding Interviews in Python
Algorithms are the backbone of software applications and are indispensable in the field of computer science. Python is a versatile and beginner-friendly programming language that employs algorithms to solve problems and accomplish specific tasks within a program. Mastering Python algorithms is a game-changer, as it enables developers to build efficient, optimized, and robust applications with superior performance. Let’s delve deeper into algorithms in Python below:
Algorithms are well-defined and structured steps that specify how data can be manipulated, analyzed, and processed to produce the optimal result. They serve as a roadmap for solving various types of problems. Algorithms are typically expressed via pseudocode and flowcharts before being implemented in a programming language such as Python.
Some common algorithms used in Python programming are:
Sorting algorithms: We use sorting algorithms to organize data in a specific sequence, such as ascending or descending order. For instance, various sorting algorithms include:
Searching algorithms: These algorithms can be utilized to locate specific elements or values within a dataset—for example, linear and binary search algorithms.
Graph algorithms: These algorithms manage graph data structures, including trees and networks. For example:
Dynamic programming algorithms: Dynamic programming algorithms are used to solve problems by optimizing them into smaller subproblems, and storing the results for future use. Some examples include:
Machine learning algorithms: Python has a rich library ecosystem for implementing machine learning algorithms, such as linear regression, decision trees, and neural networks.
We measure algorithm efficiency in terms of time and space complexity.
Time complexity refers to the time it takes for an algorithm to be completed as a function of its input size. We use big O notation to express time complexity (e.g., O(n), O(log n), O(n^2)).
Space complexity is the amount of memory an algorithm utilizes relative to its input size. Space complexity can also be calculated using Big O notation.
Understanding time and space complexities can assist you in comparing and selecting the most optimal algorithm for a specific problem-type or application as we discuss in detail in the following sections.
Python is a suitable programming language for learning about data structures and algorithms. For one, it’s excellent for algorithmic design, as it’s used extensively in data science and machine learning technologies.
Furthermore, it is a high-level programming language that obscures much of the low-level implementation details, such that your pseudo-code will look very similar to Python.
It also has relatively less syntactic sugar than other languages and can be run in any browser. This is very helpful for those who are just beginning to learn about data structures and algorithms, as low-level implementation details force you to learn unrelated topics to data structures and algorithms.
If you’re new to Python, I recommend you check out our Ace the Python Coding Interview learning path to be guided through 7 curated modules.
Algorithmic paradigms are strategies for solving a problem efficiently. Today, we will talk about the two most common algorithmic paradigms: brute force and divide & conquer. The two other more advanced paradigms are greedy algorithms and dynamic programming. If you want to learn more about these, feel free to check out our course Algorithms for Coding Interviews in Python.
Brute force algorithms are exhaustive methods of solving a problem through pure computing power and trying all possibilities to find a solution rather than using a more advanced strategy to improve overall efficiency.
For example, imagine you are trying to figure out a four-digit password combination. The brute force approach would test every possible combination of four-digit numbers from 0000 to 9999. Linear search, a method to find a target value in a given list, is an example of the brute force method. The search algorithm will traverse through the array and check each element until a match is found.
Advantages: The advantage of using the brute force method is that you are eventually guaranteed to find the solution. It’s also straight-forward and easy to implement compared to more complex algorithmic paradigm strategies.
Disadvantages: Though it’s easy to implement, it’s the most inefficient solution. It’s also difficult to improve performance and find shortcuts with this strategy.
Divide and conquer is an algorithmic paradigm that involves solving a problem by dividing it into $N$ subproblems to an “atomic” level. Once the subproblems are small enough, they will each be solved individually. Finally, the algorithm repeatedly combines the solved subsolutions into a solution for the original problem.
Advantages: It’s very efficient and powerful when dealing with general case solutions where the problem can easily be divided into subproblems. It also is efficient in terms of memory usage, as dividing the problems into atomic subproblems allows the problem to be solved in the cache itself.
Disadvantages: Because it is a recursive approach, it is oftentimes slow. There’s also a possibility that the approach duplicates subproblems leading to large recursive stacks, which will consume extra space.