Essential Python Interview Questions & Answers For Data Science

2. Can You Explain Common Searching and Graph Traversal Algorithms in Python?

Yes. Common algorithms include:

Libraries Used: NetworkX (for graphs), collections.deque (for BFS queues).

3. What is the Python “with” Statement Designed for?

The with statement makes it easier to handle resources like database connections and file streams.

Example:

              
                with open("data.txt", "r") as file:
                content = file.read()
              
            

This ensures the file is automatically closed, even if an exception occurs.

4. How Does Python Handle Memory Management, and What Role Does Garbage Collection Play?

Python employs a cyclic garbage collector and automated memory management with reference counting.

The gc module allows manual interaction with the garbage collector if needed.

5. What is the Difference Between Shallow Copy and Deep Copy in Python, and When Would You Use Each?

Use shallow copy when the inner elements are immutable or shared intentionally. When a comprehensive, separate copy of every nested item is needed, use deep copy.

6. How Can You Use Python’s Collections Module to Simplify Common Tasks?

The collections module provides alternatives to built-in types:

Example:

              
                from collections import Counter
                Counter(['apple', 'banana', 'apple'])
              
            

7. What is Monkey Patching in Python?

Monkey patching refers to modifying or extending code at runtime, often by replacing methods or classes.

8. Why Use else in the try/except construct in Python?

The else block runs only if the try block succeeds without exceptions.

Purpose: It separates code that should run only on success, improving readability.

Example:

              
              try:
                result = risky_function()
              except ValueError:
                handle_error()
              else:
                use_result(result)
                
            

9. What are Context Managers in Python, and How Are They Implemented?

Context managers can be used with statements to automatically handle setup and takedown operations.

Implemented via: Example:

              
              class CustomContext: 
                def __enter__(self):
                  # setup 
                def __exit__(self, exc_type, exc_value, traceback): 
                  # cleanup
              
            

10. What Library Would you Prefer for Plotting, Seaborn or Matplotlib?

Seaborn is built on top of Matplotlib, offering a high-level API and better aesthetics for statistical plots.

Use Seaborn for: Use Matplotlib when:

11. Which Python Libraries Have You Used for Visualization?

12. How Would You Normalise or Standardise a Dataset in Python?

Example:

              
              from sklearn.preprocessing import StandardScaler
              scaler = StandardScaler()
              scaled_data = scaler.fit_transform(data)
              
            

13. How Would you Find Duplicate Values in a Dataset for a Variable in Python?

Using Pandas:

duplicates = df[df.duplicated('column_name')]

This returns rows with duplicate values in the specified column.

To remove duplicates:

df.drop_duplicates('column_name', inplace=True)

14. How Do You Handle Missing Data in a Dataset Using Python?

Common techniques include:

The volume and pattern of missingness determine the strategy.

15. How Would You Clean and Preprocess a Large Dataset for Machine Learning in Python?

Steps include:

Use pandas, numpy, sklearn, and nltk as needed.

16. Compute the Inverse of a Matrix in NumPy?

Use numpy.linalg.inv():

              
              import numpy as np
              A = np.array([[1, 2], [3, 4]])
              inv_A = np.linalg.inv(A)
              
            

Ensure the matrix is non-singular (det ≠ 0) before inverting.

18. Explain How You Would Handle Skewed Data During Preprocessing in Python.

Techniques include:

The choice depends on the severity and context of skewness.

Python remains a cornerstone of modern data science, and mastering its concepts is essential for cracking Python basic interview questions. Understanding the fundamental principles and applying best practices significantly enhances your chances of landing high-impact roles in data-driven industries.
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How should I prepare for a Python interview?

To prepare for a Python interview, start by mastering the fundamentals of Python, including data types, control structures, functions, and OOP concepts. Practice frequently asked questions related to data science, such as list comprehensions, NumPy operations, Pandas transformations, and machine learning workflows. Work on hands-on projects and review your code for clarity and efficiency. Understanding libraries like Scikit-learn, Seaborn, and Matplotlib will help demonstrate your practical skills during technical interviews.

How to crack Python technical interview questions?

Cracking Python interview questions requires a blend of theoretical knowledge and practical application. Focus on Python’s syntax, key libraries, data manipulation techniques, and algorithms. Solve coding problems regularly on platforms like LeetCode or HackerRank to build problem-solving speed. Be ready to explain your logic clearly and demonstrate how you approach debugging or optimising code. Also, revise real-world use cases from past projects to confidently answer scenario-based or applied questions.

What are Python's key features that make it popular in Data Science?

Python's popularity in data science stems from its simplicity, readability, and robust ecosystem of libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn. It supports rapid prototyping, interactive computing, and seamless integration with machine learning frameworks. Python’s interpreted and dynamically typed nature allows for faster development. Additionally, its large community, extensive documentation, and wide use across academia and industry make it a top choice for data-driven tasks and research.

How is Python used in data preprocessing?

Python is critical in data preprocessing and is essential for building accurate machine-learning models. Libraries like Pandas help handle missing values, encode categorical data, and remove duplicates. NumPy is used for numerical computations and array transformations. Scikit-learn offers preprocessing modules for scaling, normalization, and imputation. With these tools, Python allows data scientists to clean, transform, and prepare large datasets efficiently, ensuring they’re ready for training or analysis.