Top Python Interview Questions for Data Science

Introduction

Python is the most popular programming language for data science, thanks to its versatility, ease of use, and a rich ecosystem of libraries. If you're preparing for a data science interview in 2025, it's crucial to have a strong grasp of Python fundamentals, data manipulation, visualization, and machine learning concepts. In this article, we will cover top Python interview questions to help you ace your interview and advance your career in data science.

Basic Python Interview Questions for Data Science

1. What is the difference between is and == in Python?

• is: Compares the memory location (identity) of two objects. It returns True if they reference the same object.
• ==: Compares the values of two objects. It returns True if their values are equal, regardless of their memory locations.

2. What are some commonly used Python libraries in data science?

Here are the most widely used Python libraries in data science:

• NumPy: Numerical computations and array manipulation.
• Pandas: Data manipulation and analysis using DataFrames.
• Matplotlib & Seaborn: Data visualization with charts and plots.
• Scikit-learn: Machine learning models and preprocessing.
• TensorFlow & PyTorch: Deep learning and neural networks.
• SciPy: Advanced scientific computing tasks.
• Statsmodels: Statistical analysis and time series modeling.
• NLTK & spaCy: Natural Language Processing (NLP).
• Plotly: Interactive and web-based visualizations.

3. What is NumPy, and why is it important for data science?

NumPy is a Python library for numerical computing, offering efficient handling of large arrays and matrices. It is crucial for data science because:

• It enables fast and memory-efficient array operations.
• It serves as the foundation for libraries like Pandas, SciPy, and scikit-learn.

4. How do we create a NumPy array?

We can create a NumPy array using numpy.array(), passing a list or tuple as input:

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Alternatively, functions like np.zeros(), np.ones(), and np.arange() can be used for specific array values.

5. What are list comprehensions, and how are they useful in data science?

List comprehensions provide a concise way to create lists. They allow generating a new list by applying an expression to each item in an iterable, optionally filtering elements based on a condition.

6. How can we remove duplicates from a list in Python?

By converting the list to a set:

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This is crucial in data science for ensuring datasets are clean and free from redundant entries.

7. What is Pandas, and why do we use it in data science?

Pandas is essential for working with large datasets, performing data wrangling, and conducting exploratory data analysis (EDA). It simplifies handling time-series data, missing values, and more.

Example:

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8. How do we read a CSV file in Pandas?

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This loads the CSV file into a Pandas DataFrame for easy manipulation.

9. How do we filter rows in a DataFrame?

Using conditional expressions:

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This selects rows where column_name values exceed 10.

10. What is the difference between .loc and .iloc?

• .loc: Accesses rows and columns by labels.
• .iloc: Accesses rows and columns by index positions.

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11. What is the purpose of groupby() in Pandas?

The groupby() function allows us to group data by one or more columns and perform aggregate operations (sum, mean, etc.).

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12. What is the difference between a list and a tuple?

• List: Mutable (can be changed).
• Tuple: Immutable (cannot be changed). This is important in data science to ensure certain datasets remain unchanged during processing.

13. Why is data visualization important in data science?

It helps us understand complex data by presenting it in visual formats like charts and graphs, identifying patterns, trends, and outliers.

14. What are the main Python libraries for data visualization?

• Matplotlib: Static, animated, and interactive visualizations.
• Seaborn: Statistical graphics built on Matplotlib.
• Plotly: Interactive and web-based visualizations.
• Altair: Declarative statistical visualization library.

15. How do we create a basic line plot using Matplotlib?

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16. What is __init__?

• __init__ is a constructor method in Python classes that initializes an object's attributes when it is created.

17. What is the difference between Python arrays and lists?

• Lists can store different data types and are flexible, while arrays (from the array module or NumPy) are optimized for numerical operations and store only one type.

18. How can you make a Python script executable on Unix?

• Add #!/usr/bin/env python3 at the top and give execute permissions using chmod +x script.py.

19. What is slicing in Python?

• Slicing extracts specific portions of sequences using start:stop:step, like my_list[1:5].

20. What is a docstring in Python?

• A docstring is a multi-line string inside """ """ or ''' ''' that documents a function, class, or module.

21. What are global, protected, and private attributes in Python?

• Global: Accessible anywhere in the script.
• Protected: Prefixed with _ and intended for internal use.
• Private: Prefixed with __ and inaccessible outside the class.

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Conclusion

Mastering Python for data science is essential for securing a data science role. The questions covered here help build a strong foundation, covering Python basics, data manipulation, visualization, and machine learning. Practice these concepts to increase your confidence in interviews and advance your data science career.

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