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Python Data Science jobs, interview tips, and career insights for aspiring professionals.

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Interview Question

What is the GIL (Global Interpreter Lock) in Python, and how does it impact the execution of multi-threaded programs?

Answer: The Global Interpreter Lock (GIL) is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. This means that in a CPython process, only one thread can be executing Python bytecode at any given moment, even on a multi-core processor.

This has a significant impact on performance:

For CPU-bound tasks: Multi-threaded Python programs see no performance gain from multiple CPU cores. If you have a task that performs heavy calculations (e.g., image processing, complex math), creating multiple threads will not make it run faster. The threads will execute sequentially, not in parallel, because they have to take turns acquiring the GIL.

For I/O-bound tasks: The GIL is less of a problem. When a thread is waiting for Input/Output (I/O) operations (like waiting for a network response, reading from a file, or querying a database), it releases the GIL. This allows another thread to run. Therefore, the threading module is still highly effective for tasks that spend most of their time waiting, as it allows for concurrency.

How to achieve true parallelism?

To bypass the GIL and leverage multiple CPU cores for CPU-bound tasks, you must use the multiprocessing module. It creates separate processes, each with its own Python interpreter and memory space, so the GIL of one process does not affect the others.

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By: @DataScienceQ
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Interview question

What objects can be put into a set?

Answer: In Python, a set can only contain hashable (i.e., immutable) objects. This means you can put numbers, strings, tuples (if all their elements are also hashable), boolean values, and other immutable types into a set.

Objects like list, dict, set, and other mutable structures cannot be put in: they do not have a hash function (hash) and will cause a TypeError.

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Interview Question

What can be a key in a dictionary?

Answer: Dictionaries in Python are hash tables. Instead of keys, hashes are used in the dictionary. Accordingly, any hashable data type can be a key in the dictionary, which means all immutable data types.

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Interview Question

What is a deep copy?

Answer: A deep copy is a complete duplication of an object along with all nested structures. Changes to the original do not affect the copy, and vice versa.

In Python, this is done using copy.deepcopy(), which creates a fully independent data structure, including nested lists, dictionaries, and other objects.


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Interview Question

Why does list.sort() return None instead of the sorted list?

Answer: The list.sort() method modifies the list in place and intentionally returns None to clearly indicate that the sorting was done but no new list was created. This prevents confusion between modifying the object and creating its copy.

If a new sorted list is needed, the built-in sorted() function is used, which returns the result without changing the original.


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Interview Question

Are there generics in Python like in Java or C++?

Answer: Yes, but only at the annotation level. Since Python 3.5, generic types (List[T], Dict[K, V]) have appeared through the typing module, but they are intended for static checking and do not affect the program's behavior at runtime.

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Interview Question

What do you know about NoSQL databases?

Answer: NoSQL databases do not use a rigid tabular model and work with more flexible data structures. They do not require a fixed schema, so different records can have different sets of fields.

These databases scale well horizontally: data is distributed across cluster nodes, which helps handle high loads and large volumes. Different storage models are supported — key-value, document, columnar, and graph. This allows choosing the appropriate structure for a specific task.

Common systems include MongoDB (documents), Cassandra (columns), Redis (key-value), and Neo4j (graphs). They are used where scalability, speed, and data flexibility are important.


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Interview Question

What does it mean that a QuerySet in Django is "lazy"?

Answer: "Lazy" QuerySet means that Django does not make a database query at the moment of creating the QuerySet. When you call .filter(), .exclude(), .all(), etc., the query itself is not executed yet — only an object describing the future SQL is created.

The actual database access happens only when the results are really needed: when iterating over the QuerySet, calling list(), count(), first(), exists(), and other methods that require data.

This approach helps avoid unnecessary database hits and improves performance — queries are executed only at the moment of real necessity.


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Interview question

What is the difference between calling start() and run() on threading.Thread?

Answer: The start() method creates a new thread and automatically calls run() inside it.

If you call run() directly, it will execute in the current thread like a normal function — without creating a new thread and without parallelism.

This is the key difference: start() launches a separate execution thread, while run() just runs the code in the same thread.


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Interview Question

What does nonlocal do and where can it be used?

Answer: nonlocal allows you to modify a variable from the nearest enclosing function without creating a new local one. It only works inside a nested function when you need to change a variable declared in the outer, but not global, scope.

This is often used in closures to maintain and update state between calls to the nested function.


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