Basic Python Data Science Interview Questions & Answers

Preparing for a Python Data Science interview?  Mastering the fundamentals of Python is the first step before diving into advanced topics like machine learning and AI. Below are some frequently asked basic Python interview questions for data science, explained with examples.

1. What are the core built-in data types in Python?

Python provides several built-in data types essential for data handling:

1. int → integers
2. float → floating-point numbers
3. str → string values
4. bool → Boolean (True/False)
5. list → ordered, mutable collection
6. tuple → ordered, immutable collection
7. set → unordered unique elements
8. dict → key-value pairs

 These are the backbone of Python data manipulation and widely used in data science workflows.

2. Which Python libraries are most used for data analysis?

Python has powerful data analysis libraries:

1. NumPy → numerical computing & multi-dimensional arrays
2. Pandas → DataFrames & Series for structured data
3. Matplotlib → visualization & charts
4. Seaborn → statistical and aesthetic plotting

Together, these form the data science toolkit in Python for analysis and visualization.

3. What is Negative Indexing in Python?

Negative indexing allows access to elements from the end of a list.

a = [10, 20, 30, 40, 50]

print(a[-1])  # 50

print(a[-2])  # 40

This feature makes it easy to retrieve elements from the end without calculating length.

4. Explain dictionary comprehension with an example.

Dictionary comprehension is a concise way to build dictionaries.

squares = {x: x**2 for x in range(6)}

print(squares)  # {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

It improves readability and reduces boilerplate code.

5. Is Python an Object-Oriented Language?

Yes  Python supports object-oriented programming (OOP) principles such as:

1. Encapsulation
2. Inheritance
3. Polymorphism

This allows building reusable, modular, and scalable code – important for large data science projects.

6. Seaborn vs Matplotlib – which one to use?

1. Matplotlib → best for low-level, customized plots.
2. Seaborn → best for quick, stylish, and statistical plots.

 For exploratory data analysis (EDA), Seaborn is faster. For complex customization, Matplotlib is preferred.

7. What’s the difference between Lists and Tuples in Python?

1. List → mutable, elements can be changed.
2. Tuple → immutable, faster in execution, used when data shouldn’t change.

This distinction helps in choosing the right structure for performance.

8. How can you sort a dictionary in Python?

Keys or values can sort dictionaries:

data = {“apple”: 3, “banana”: 1, “cherry”: 2}

sorted_by_key = dict(sorted(data.items()))  

sorted_by_value = dict(sorted(data.items(), key=lambda x: x[1]))  

9. Difference between Series and DataFrame in Pandas?

1. Series → 1D labeled array (like a column).
2. DataFrame → 2D table with rows and columns (like Excel/SQL table).
   

Series = one column, DataFrame = full dataset.

10. How does Python manage memory when the program exits?

Python uses automatic garbage collection to free unused memory. However, in cases of circular references or global variables, some memory may not be fully released depending on the OS.

Mastering these basic Python data science interview questions will give you a strong foundation to tackle advanced concepts in machine learning, AI, and big data.

Intermediate Python Data Science Interview Questions & Answers

Once you master the basics, the next step in preparing for Python data science interviews is strengthening your knowledge of built-in functions, error handling, and data structures like stacks, queues, and string manipulation. Below are some commonly asked intermediate-level Python interview questions with answers.

11. What is a KeyError in Python?

A KeyError occurs when you try to access a dictionary key that does not exist.

Example:

students = {1: “Alex”, 2: “Riya”, 3: “John”}

print(students[5])  # KeyError

To avoid this, you can:

1. Use .get(key) which returns None if the key is missing.
   
2. Use try-except to handle errors gracefully.

12. How to find the intersection of two arrays in Python?

You can use Python’s set operations to find common elements.

X = [1, 5, 9, 0]

Y = [3, 0, 2, 9]

intersection = list(set(X) & set(Y))

print(intersection)  # [0, 9]

This is useful for data comparison tasks in data science.

13. Explain map(), reduce(), and filter() in Python.

    1.map() → Applies a function to each element in a list.

items = [1, 2, 3, 4]

squared = list(map(lambda x: x**2, items))

    2.reduce() → Performs cumulative computation.

from functools import reduce

result = reduce(lambda x, y: x*y, items)  # multiplies all elements

   3.filter() → Returns elements that satisfy a condition.

even = list(filter(lambda x: x % 2 == 0, items))

These functions make functional programming in Python efficient.

14. Difference between del(), clear(), remove(), and pop().

1. del() → Deletes a variable or specific list element.
   
2. clear() → Empties the entire list.
   
3. remove() → Removes the first occurrence of a value.
   
4. pop() → Removes and returns an item at a given index.
   

15. How to generate combinations in Python?

You can use itertools.combinations() to find all k-combinations from numbers 1 to n.

from itertools import combinations

def find_combinations(k, n):

    return list(combinations(range(1, n+1), k))

print(find_combinations(2, 3))  

# [(1, 2), (1, 3), (2, 3)]

16. How to check if one string is a subsequence of another?

You can use an iterator to check if string1 appears in order within string2.

def is_subsequence(s1, s2):

    it = iter(s2)

    return all(c in it for c in s1)

print(is_subsequence(“abc”, “aebdc”))  # True

17. Difference between pass, continue, and break in Python.

1. pass → Does nothing (used as a placeholder).
   
2. continue → Skips current loop iteration.
   
3. break → Exits the loop completely.

18. Write a function to return bigrams from a string.

Bigrams are consecutive word pairs.

def find_bigrams(text):

    words = text.split()

    return [(words[i], words[i+1]) for i in range(len(words)-1)]

print(find_bigrams(“Python data science interview”))  

# [(‘Python’, ‘data’), (‘data’, ‘science’), (‘science’, ‘interview’)]

19. What are namespaces in Python?

A namespace is a container that maps names (identifiers) to objects. They prevent naming conflicts.

Types of namespaces:

1. Local → inside functions
   
2. Global → across a script
   
3. Built-in → reserved by Python (e.g., len, print)

20. Difference between is and == in Python.

1. is → checks if two variables point to the same object in memory.
   
2. == → checks if two objects have the same value.

a = [1, 2]

b = [1, 2]

print(a == b)  # True (same values)

print(a is b)  # False (different objects)

These intermediate I strengthen your understanding of functions, data structures, and control statements—skills crucial for data science interview preparation.

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