Top 25 Python Coding Questions for Interview

We had to process huge datasets, but our existing tools couldn’t handle the volume efficiently. At my previous job, our team faced a challenge with data analysis. Lastly, I used Jupyter Notebooks for organizing my code and findings, making it easy for my team to understand and replicate my work. I built predictive models for customer behavior, which increased our marketing efficiency.
Mutable data types (e.g., lists, dictionaries) can be changed after creation, whereas immutable data types (e.g., tuples, strings) cannot be altered once defined. Classifiers are hypotheses that assign labels to data items based on their classification. Df.tail() returns the top 5 rows by default, and df.tail(n) will fetch the last n rows.
Additionally, python supports objects, modules, threads, exception-handling, and automatic memory management which help in modelling real-world problems and building applications to solve these problems. The with statement ensures that cleanup happens even if an error occurs. This approach ensures informative error handling without sacrificing code readability, making the codebase more navigable.” It tests knowledge of defining models and basic operations like creating new instances. This series of 5 courses takes you through the fundamentals of Python syntax, algorithms, data structures, and problem-solving so you can ace your next interview—and it’s free to get started.

• You use a queue, start by putting the root in it, then pop one node at a time and add its children, this goes level by level, this is used in many tree problems like shortest path and views.
• Whether you’re building backend systems, data pipelines, or automation tools, Python remains one of the most in-demand languages across tech.
• Generators yield items one at a time and only compute them when requested, which can lead to significant memory savings.
• This pattern ensures that only one instance of a class exists throughout the application’s lifecycle.

This prevents Python from creating a separate __dict__ for each instance, which helps in making the classes memory efficient and faster. It’s easy syntax and large library support make it ideal for a wide range of real-world applications. It helps in simplifying the code https://uvik.io/ by automatically opening and closing the files, even if there is an error during the execution. The ‘with’ statement in Python is used for managing resources efficiently and helps in file handling operations.
When a yield statement is encountered, the generator function produces a value and suspends its execution, saving its state for later. In a regular dictionary, the order of elements is determined by the hash values of their keys, which can change over time as the dictionary grows and evolves. A result is a new dictionary that evaluates the key-value pair in the context of the for and if clauses. Dict comprehension is a concise way to create a dictionary.
Reverse a list using the enumerate() function in Python by iterating over the original list and creating a new list with the elements in reverse order. For fetching multiple URLs concurrently you can simply use aiohttp and asyncio as it results in improved performance for I/O related tasks. Here, the problem statement says that we have to find out the largest and second-largest elements from a list. But __slot__ cannot be used to add new attributes to the instances, which reduces the flexibility.