Simplify Python coding interview prep by learning these patterns
Simplify Python coding interview prep by learning these patterns
3 key patterns to understand and solve common algorithmic coding questions
Hey Grokking Python readers, and happy Thursday!
When was the last time you brushed up on your Python interview skills? In recent editions, we reviewed questions about fundamental Python knowledge and concepts to help you get interview-ready but didn’t cover coding problems. Well, today we’ll talk about algorithmic coding questions that could show up in other parts of your interview process. With thousands of potential questions to account for, preparing for the coding interview can feel like an impossible challenge. Yet with a strategic approach, coding interview prep doesn’t have to take more than a few weeks. Even if you're not looking to switch jobs, it never hurts to be prepared in case your circumstances change down the road.
Today, we’ll explore three key patterns to help you understand and solve common coding interview questions. By learning to recognize these underlying patterns, you'll be equipped to tackle any problem that comes your way. Let's get started!
Sliding Window pattern
The Sliding Window pattern is a computational method aimed at improving the efficiency of algorithms by reducing the use of nested loops. It's a variation of the Two Pointers pattern but uses window bounds to delimit the scope of the search. This approach can be useful for solving problems that involve searching through large datasets for specific patterns or values.
The segment, or window size, can be set according to the problem’s requirements. For example, if we have to find three consecutive integers with the largest sum in an array, we can set the window size to 3. This will allow us to process the data of three elements at a time.
Real-world applications
Many problems in the real world use the sliding window pattern. Let’s look at some examples.
Telecommunications: We can find the maximum number of users connected to a cellular network’s base station within a given time interval (e.g., every k milliseconds).
E-commerce: Given a dataset of product IDs in the order they were viewed by the user and a list of products that are likely to be similar, we can find how often these products occur together in the dataset.
Video streaming: Given a stream of numbers representing the number of buffering events in a given user session, we can calculate the median number of buffering events in each one-minute interval.
Fast and Slow Pointers pattern
The Fast and Slow Pointers pattern involves using two pointers to traverse an iterable data structure at different speeds. This technique is often used to identify unique characteristics of directional data structures, such as linked lists or arrays.
We can use the two pointers to traverse an array or list in either direction. One of the pointers, the fast pointer, moves at a quicker speed than the other, the slow pointer. Typically, the slow pointer moves forward by a factor of one in each step, while the fast pointer moves by a factor of two. However, the relative speed of the pointers can be adjusted based on the specific problem being addressed.
Real-world applications
Let’s look at examples of where you might use the Fast and Slow Pointers pattern in the real world.
Symlink verification: A symlink verification utility in an operating system can utilize fast and slow pointers to detect loops or cycles in symbolic links. Symlinks are shortcuts that point to other files or directories, but they can sometimes create loops where one shortcut points to another and so on. To avoid these issues, the utility can use fast and slow pointers, which work similarly to the way they do in a Linked List. The fast pointer moves through the symlinks at a faster rate than the slow pointer, and if they ever meet, it indicates that there is a loop in the symlinks. The utility can then take appropriate action to resolve the issue.
Compiling an object-oriented program: Fast and Slow Pointers can be used to identify and remove cyclic dependencies in programs that are divided into multiple files. Large applications often have many modules that are separated into different files for easier maintenance. Dependency relationships are established to specify the order in which these files should be compiled. However, sometimes these dependencies can form a cycle, which can cause errors during compilation. In such cases, Fast and Slow Pointers can be used to detect the cycle and break it, allowing the program to be compiled and executed smoothly.
Figure 2: Using pointers to check if a linked list contains a cycle
Merge Intervals pattern
The Merge Intervals pattern mainly deals with overlapping time intervals. Each interval is represented by a start time and an end time. For example, an interval of [10, 20] seconds means that the interval starts at 10 seconds and ends at 20 seconds, including both 10 and the time 20 seconds in the interval.
This pattern is commonly used to solve scheduling problems. The key to understanding and effectively using the merge interval pattern is understanding how any two intervals may overlap.
Real-world applications
The Merge Intervals pattern can be applied to many situations. Here are a few examples:
Scheduling meetings or appointments: When scheduling meetings, it is important to make sure that no two meetings overlap. By representing each meeting as an interval and using the Merge Intervals pattern, we can quickly and easily find times that are available for scheduling.
Allocating resources: In many situations, we must allocate people, equipment, materials, or other resources to different tasks. We can determine the optimal allocation of resources to minimize conflicts and overlap by using the Merge Interval pattern.
Optimizing transportation routes: In transportation, we often need to plan routes that minimize the time spent traveling and maximize the amount of cargo that can be delivered. By representing each leg of a journey as an interval, we can find the most efficient route.
Take your coding interview prep to the next level
That concludes this edition of Grokking Python! We hope you've enjoyed our overview of some common coding interview patterns. Keep in mind that there are many more patterns to learn beyond the ones we covered today.
To continue improving your coding interview skills, we recommend checking out the all-new Grokking Coding Interview Patterns in Python course, which covers 21 additional patterns, includes free practice problems to test your knowledge, and features a hands-on, setup-free coding environment.
Good luck with your coding interview prep!
And as always, happy learning!