Python Data Science Jobs & Interviews
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Your go-to hub for Python and Data Science—featuring questions, answers, quizzes, and interview tips to sharpen your skills and boost your career in the data-driven world.

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Interview Question

What types of file objects are there?

Answer: In Python, file objects are abstractions that provide a unified interface for working with different data sources. They are divided into three types:

▶️Text (TextIO) — work with strings (str) and automatically encode/decode data. For example: open("file.txt", "r", encoding="utf-8").

▶️Binary (BufferedIO) — operate with bytes and are often used for images, videos, or arbitrary data. For example: open("image.jpg", "rb").

▶️Low-level (raw) (RawIO) — provide direct access to devices or files without buffering. Usually used inside the standard library, rarely applied directly.

All these types implement interfaces from io — io.TextIOBase, io.BufferedIOBase, and io.RawIOBase. The standard open() function under the hood returns the appropriate object depending on the mode.


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https://t.iss.one/DataScienceQ
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Interview question

What is a hash table and where is it used in Python?

Answer: A hash table is a data structure that stores key–value pairs and provides fast access by key in time close to O(1).

In Python, the built-in dict and set structures are implemented based on hash tables:

▶️ Keys are hashed using __hash__() and compared via __eq__();

▶️ The hash code is used to compute the index in the array where the element is placed;

▶️ Starting from Python 3.6 (and guaranteed from 3.7), dict preserves the insertion order of keys thanks to the compact dict.

Important: the key must be hashable — that is, have an immutable hash and a consistent implementation of __hash__() and __eq__().


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Interview question

Why is None a singleton object in Python?

Answer: None is the sole instance (singleton) of the NoneType, and all variables containing None refer to the same object. This saves memory because new instances are not created.

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https://t.iss.one/DataScienceQ

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