Python Interview Questions for Data Science: An Essential Read for Job Seekers!

Python has been around for almost four decades now. And during this long existence, the language has become a popular one in a plethora of fields. Python is easy to learn. And, even the technologically challenged can master it with the right guidance and practice. The syntax of Python is easy and can be understood with ease. Furthermore, Python is free. It comes pre-installed with Linux and can be installed on Windows in a couple of minutes. And the libraries of Python are often extremely aligned with the needs of data professionals. Making it essential to learn and master Python before embarking on a fulfilling venture in data science.

This article will thus concentrate on discussing the most popular Python interview questions for data science. And add to the interview preparation of a budding data scientist, almost ready to embark on a job search.

Q: Explain scopes in Python

A: Scopes in Python define the visibility and accessibility of objects within a program. Every object in Python functions within a specific scope. The three main types of scopes in Python are:

1. Local scope: This scope pertains to objects that are available within a specific function. They are accessible only within the function where they are defined.
2. Global scope: Global scope encompasses objects that are accessible throughout the entire code. These variables can be accessed and modified from any part of the program.
3. Module-level scope: This scope refers to objects that are functional within the current module of the code. They are accessible within the module but not outside of it.

Q: What are the three main attributes of Python?

A: Python has three main attribute types:

1. Global attributes: These are public variables that are functional in the global scope. They can be accessed and modified from anywhere in the code.
2. Protected attributes: Attributes defined with an underscore prefix to their identifier are considered protected attributes. They indicate that these attributes should be treated as non-public parts of the code, although they can still be accessed from outside the class or module.
3. Private attributes: Objects that cannot be accessed from the outside are prefixed with a double underscore, making them private attributes. Private attributes are intended to be strictly internal to the class or module and are inaccessible from outside the object.

Q: What are unit tests?

A: Unit tests are a crucial part of software development in Python. In the process of developing software, the code is divided into units or components. Each component is tested individually to ensure its functionality. By testing components in isolation, developers can identify and address issues in specific parts of the code without the need to analyze the entire codebase. Unit tests help maintain code quality and enable efficient debugging and error correction.

Q: How does slicing work in Python?

A: Slicing in Python involves extracting specific elements from strings, arrays, lists, or tuples. It is achieved by specifying a start, stop, and step value within square brackets. The start indicates the starting point, the stop indicates the ending index where the process will stop, and the step determines the number of elements to jump in each iteration. Slicing can be applied to various data structures. For example:

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print(numbers[1::2])  # Output: [2, 4, 6, 8, 10]

Q: Explain memory management in Python

A: Python has a dedicated memory manager responsible for memory management. The memory allocated by Python is stored in the form of a private heap space. This private heap is largely inaccessible to the programmer. All objects created during program execution are stored in this heap. Additionally, Python has an inbuilt garbage collector that automatically recycles unused memory from the heap, making memory management more efficient and convenient for developers.

Q: Provide a code example for deleting a file in Python?

A: Certainly! You Deleting a file in Python is a straightforward process. The os module provides the necessary functions to interact with the operating system. Here's an example code snippet that demonstrates how to delete a file in Python:

import os

# Specify the file name or path to be deleted

file_name = "ChangedFile.csv"

# Check if the file exists

if os. path.exists(file_name):

    # Delete the file

    os.remove(file_name)

    print("File Removed!")

else:

    print("File does not exist.")

In the above code, we first import the os module, which provides functions for interacting with the operating system. We then specify the name or path of the file that we want to delete. By using the os. path.exists() function, we check if the file exists in the specified location. If the file exists, we can proceed to delete it using the os. remove() function. After successfully deleting the file, we print a message to confirm the removal. If the file does not exist, we display a message indicating that the file was not found.