Top 100 Python Interview Questions

Looking for REAL Python interview questions asked in FAANGs and startups? Here's a comprehensive guide with REAL questions!

These questions are frequently used in interviews for data analyst, data scientist, and data engineer positions. Companies like Google, Amazon, Meta, Microsoft, and many more use Python questions to assess candidates' technical skills.

In this guide, we will explore key Python question areas, uncover preparation tips, and provide a list of questions to help you ace your interviews!

Let's dive in ⤵

✍️ What is a Python Coding Interview?

Here are common Python topics assessed in interviews across data roles.

Area 1 - Core Python Concepts

Understanding the fundamentals of Python is essential. Expect questions that test your knowledge of basic syntax, data structures (lists, dictionaries, sets), and control flow (loops, conditionals). This foundational knowledge is critical for building logic in problem-solving.

Sample Questions

• What is the difference between a list and a tuple?
• How do you reverse a string in Python?
• What are list comprehensions, and how do you use them?
• Explain the difference between == and is.
• How do you merge two dictionaries in Python?
• What is the purpose of the lambda function?

Area 2 - Data Manipulation with Libraries

For data analyst and data scientist roles, proficiency in libraries like Pandas and NumPy is crucial. You’ll likely face questions involving data cleaning, transformation, and analysis.

Sample Questions

• How do you remove missing values from a DataFrame?
• What is the difference between iloc and loc in Pandas?
• How do you group data in a DataFrame and apply an aggregation function?
• Explain the concept of broadcasting in NumPy.
• How do you concatenate multiple DataFrames?

Area 3 - Algorithms and Problem-Solving

Data engineer and data scientist interviews often assess your algorithmic thinking. Expect to solve coding problems that test your ability to use Python effectively.

Sample Questions

• Write a function to find the longest substring without repeating characters.
• Implement a function to check if a linked list is a palindrome.
• How do you perform binary search in Python?
• Solve the two-sum problem using a dictionary.
• What is the time complexity of common list operations like append() and insert()?

Area 4 - Python for Data Analysis and Visualization

Proficiency in using Python for data analysis and visual representation is important. Expect questions that combine coding skills with data interpretation.

Sample Questions

• How do you create a scatter plot using Matplotlib?
• What is the difference between sns.histplot() and sns.distplot() in Seaborn?
• How do you perform a pivot table operation in Pandas?
• Explain the use of groupby() and apply() for custom aggregations.
• How do you plot multiple subplots in Matplotlib?

Area 5 - Python for ETL and Data Pipelines

For data engineering roles, expect questions around using Python for building ETL (Extract, Transform, Load) pipelines and handling large-scale data.

Sample Questions

• How do you read a CSV file in chunks using Pandas?
• What is the role of generators in building efficient data pipelines?
• Explain how to use Python to interact with databases.
• How do you parallelize data processing using multiprocessing or multithreading?
• What is the purpose of the Airflow library, and how do you use it?

Area 6 - Python in Machine Learning and Statistical Modeling

In data science roles, you will be tested on your ability to implement and tweak machine learning models using Python libraries.

Sample Questions

• How do you handle missing data in a dataset before training a model?
• What is the difference between fit() and transform() in Scikit-Learn?
• Explain how to implement cross-validation in Python.
• How do you choose the right metric for evaluating a regression model?
• What are the main steps in preprocessing text data for NLP tasks?

⭐ Python Interviews Across Data Roles

Here's how python interviews vary across data analyst, data scientist and data engineer roles.

Data Analyst Interview

• Format: Usually includes a mix of technical screens and project-based discussions.
• Duration: 30 to 60 minutes per session.
• Number of Questions: Typically 3-5 coding and data manipulation questions.
• Focus Areas: Questions centered around data manipulation, visualization, and interpreting data insights using libraries like Pandas and Matplotlib. Expect interviewers to ask you to explain your code and justify your approach.

Data Scientist Interview

• Format: Hands-on coding assessments, take-home projects, or live coding sessions.
• Duration: 45 to 90 minutes for technical screens; take-home projects may have a submission window of a few days.
• Number of Questions: 2-4 coding problems plus follow-up questions.
• Focus Areas: Expect coding questions involving data cleaning, feature engineering, and ML model implementation. The interviewer may also ask about model evaluation, data preprocessing, and problem-solving approaches.

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Data Engineer Interview

• Format: Includes live coding, system design questions, and optimization discussions.
• Duration: 60 to 90 minutes per technical session.
• Number of Questions: 2-4 technical problems, often with deep dives into performance considerations.
• Focus Areas: Building efficient scripts, data pipeline development, and database interaction. The interviewer may probe your ability to handle large data volumes and optimize Python code for performance.

✍️ Python Interview Technical Screen

Here's an example of a coding technical screen in a data engineering interview. Assume the round is about 45 minutes with 35 minutes of problem-solving time, conducted by a senior or staff data engineer.

Instruction for Technical Interview

On a Coderpad, you must write solutions in Python to the three problems below.

Set 1 - Data Structures

1. Write a function that takes a string and returns the length of the longest substring without repeating characters.

Set 2 - Data Manipulation

You are provided with the following SalesData table that records daily product sales across different regions:

Date	Product	Revenue	Region
2024-01-01	Product A	1000	North America
2024-01-02	Product B	1500	Europe
2024-01-03	Product C	800	Asia
2024-01-04	Product A	1200	North America
2024-01-05	Product B	1100	Europe
2024-01-06	Product C	900	Asia

2. Write a Python function to filter the data to include only records from a specified region and date range. The function should return a DataFrame containing the filtered data.
3. Modify your function to calculate the total revenue for each Product within the filtered data.

Solutions:

# Solution 1:
def longest_unique_substring(s):
    char_index = {}
    max_length = 0
    start = 0

    for i, char in enumerate(s):
        if char in char_index and char_index[char] >= start:
            start = char_index[char] + 1
        char_index[char] = i
        max_length = max(max_length, i - start + 1)

    return max_length

# Solution 2: Filtering data by region and date range
def filter_and_aggregate_revenue(df, start_date, end_date, region):
    # Filter DataFrame for the given date range and region
    filtered_df = df[(df['Date'] >= start_date) & (df['Date'] <= end_date) & (df['Region'] == region)]
    
    # Group by product and sum revenue
    revenue_by_product = filtered_df.groupby('Product')['Revenue'].sum().reset_index()
    
    return revenue_by_product

# Example usage
start_date = '2024-01-01'
end_date = '2024-01-05'
region = 'Europe'
print(filter_and_aggregate_revenue(df, start_date, end_date, region))

# Solution 3: Function to create an ETL pipeline for filtering and aggregating revenue
def etl_pipeline(df, start_date, end_date, region):
    # Step 1: Extract - Filter data based on date range and region
    filtered_df = df[(df['Date'] >= start_date) & (df['Date'] <= end_date) & (df['Region'] == region)]
    
    # Step 2: Transform - Group by 'Product' and aggregate total revenue
    aggregated_df = filtered_df.groupby('Product')['Revenue'].sum().reset_index()
    
    # Step 3: Load - Return the transformed DataFrame
    return aggregated_df

# Example usage
start_date = '2024-01-01'
end_date = '2024-01-05'
region = 'Europe'
result = etl_pipeline(df, start_date, end_date, region)
print(result)

📝 More Python Interview Questions

Basic Python Interview Questions

1. How do you remove duplicates from a list while maintaining order?
2. What is the purpose of the enumerate() function, and how do you use it?
3. Explain how list slicing works, including the use of negative indices.
4. How do you flatten a nested list in Python?
5. What is the difference between append() and extend() in lists?
6. How do you use the zip() function in Python?
7. How do you find the frequency of elements in a list?
8. What are Python comprehensions, and how do they improve code readability?
9. Explain the difference between isinstance() and type checking with type().
10. How do you merge multiple dictionaries in Python 3.9+?
11. What are Python closures, and how do they work?
12. How do you reverse the order of words in a string?
13. What is the purpose of collections.defaultdict()?
14. How do you check if all elements in a list are unique?
15. What is the purpose of collections.OrderedDict()?
16. Explain how to create a recursive function in Python.
17. What are lambda functions, and when are they most useful?
18. How do you create a simple iterator class in Python?
19. What is the itertools.product() function, and where is it useful?
20. How do you handle large files in Python without loading the entire file into memory?

Intermediate Python Interview Questions

1. How do you implement a binary search algorithm in Python?
2. Explain the concept and use of decorators in Python.
3. How do you implement a custom sorting function using sorted() with a lambda?
4. What is the functools.reduce() function, and how does it work?
5. How do you implement memoization in Python?
6. How do you use the collections.Counter() for counting elements in an iterable?
7. Implement a function that generates all permutations of a given string.
8. What is the use of heapq, and how do you create a min-heap?
9. Explain the with statement and how it manages resources.
10. How do you implement a queue using Python's deque?
11. What is multithreading, and how does Python handle it with the GIL?
12. Explain how to build a context manager using contextlib.
13. How do you work with JSON data in Python?
14. Write a function that merges overlapping intervals in a list of tuples.
15. How do you parallelize code execution using Python's multiprocessing module?
16. How do you handle CSV data with large file sizes using Pandas efficiently?
17. Explain the concept of method chaining in Python.
18. How do you use the pathlib module for file system operations?
19. What is asyncio, and how do you use it for asynchronous programming?
20. How do you use pandas.groupby() to perform complex data aggregation?

Numpy Interview Questions

1. What is NumPy, and why is it used in Python?
2. How do you create a NumPy array from a Python list?
3. What is the difference between np.array() and np.asarray()?
4. How do you create an array of zeros or ones using NumPy?
5. What is the purpose of np.linspace() and np.arange()?
6. How do you check the shape and size of a NumPy array?
7. How can you change the shape of a NumPy array without modifying its data?
8. What are vectorized operations in NumPy, and why are they important?
9. How do you perform element-wise addition, subtraction, multiplication, and division in NumPy?
10. How do you find the mean, median, and standard deviation of a NumPy array?
11. Explain how slicing works in NumPy arrays.
12. How do you concatenate two arrays in NumPy?
13. What is the difference between np.vstack() and np.hstack()?
14. How do you create a random array using NumPy?
15. How do you find unique values in an array using NumPy?
16. What is broadcasting in NumPy, and how does it work?
17. How do you transpose a matrix using NumPy?
18. How do you flatten a 2D array into a 1D array?
19. What is the use of np.where() in conditional filtering?
20. How do you save and load arrays using NumPy?

Pandas Interview Questions

1. How do you create a DataFrame from a dictionary?

data = {'Name': ['Alice', 'Bob', 'Carol'], 'Age': [25, 30, 27]}
df = pd.DataFrame(data)

2. How do you read a CSV file into a Pandas DataFrame?
   
3. How do you select a specific column or multiple columns from a DataFrame?
4. How would you filter the DataFrame to only include rows where the Department is 'IT'?

Table Example:

ID	Name	Age	Department
1	Alice	25	HR
2	Bob	30	IT
3	Carol	27	Finance
4	Dave	24	HR
5	Eve	29	IT

5. How do you reset the index of a DataFrame?
6. How do you find the correlation between columns in a DataFrame?
7. Show how to perform an inner join to merge these two DataFrames on Dept_ID and explain the result.

Table Example 1 (Employees):

Emp_ID	Name	Dept_ID
101	Alice	D001
102	Bob	D002
103	Carol	D001
104	Dave	D003

Table Example 2 (Departments):

Dept_ID	Dept_Name
D001	HR
D002	IT
D004	Finance

8. How do you reshape a DataFrame using melt()?
9. Transform this DataFrame so that Year remains a column and the products are combined into a single column with corresponding values in another column.

Year	Product_A	Product_B
2021	100	200
2022	150	220
2023	130	210

10. How do you remove duplicate rows from a DataFrame?
11. How do you add a new column to a DataFrame?
12. How do you handle categorical data in Pandas?
13. How do you use merge_asof() in Pandas?
14. How do you pivot a DataFrame, and what is the difference between pivot() and pivot_table()?
15. How do you concatenate or append multiple DataFrames?
16. How do you optimize performance when handling large data sets in Pandas?
17. How do you create a custom function and apply it to a DataFrame?
18. Write a function that categorizes scores into 'Pass' or 'Fail' (threshold: 80) and apply it to the Score column.

Name	Score
Alice	85
Bob	90
Carol	78
Dave	88

19. Use the query() method to filter rows where Score is greater than 80.

ID	Name	Age	Score
1	Alice	25	85
2	Bob	30	90
3	Carol	27	78
4	Dave	24	88

20. Identify and remove outliers from this DataFrame based on the Value column.

ID	Value
1	100
2	102
3	5000
4	105
5	110

💡 How to Prepare for Python Interviews

Tip 1 - Understand the Python Interview Format
Python interviews can vary widely depending on the role—ranging from theoretical questions to hands-on coding challenges on platforms like CoderPad, HackerRank, or whiteboard sessions. Be ready to write code, discuss your approach, and explain Python concepts in depth. Familiarize yourself with popular Python interview formats, which often include problem-solving, data manipulation, and code optimization tasks.

Tip 2 - Practice Writing Python Code Without Running It
Some interviews might require you to write Python code without immediate access to an IDE or execution environment. Practice writing code on paper or in a plain text editor to build confidence in your syntax and logic without relying on real-time feedback. This will enhance your attention to detail and help you spot potential errors before execution.

Tip 3 - Practice Coding Under Timed Conditions
Simulate the interview experience by solving problems within a set time limit. This will help you manage stress and build speed, preparing you for real interviews where time constraints are common. Aim to complete coding problems and explain your thought process clearly under pressure.

Tip 4 - Join Prep Communities
Finding study buddies is one of the best ways to prepare for interviews. Join community groups like DataInterview Premium Community where you can network with coaches and candidates who are currently preparing for interviews at top companies like Google, Meta, and Stripe. Spend some time each week reviewing sample questions together to enhance your understanding and stay

Tip 5 - Get Personalized Coaching
Consider scheduling mock interviews with experienced coaches or peers to simulate the real interview experience. This practice helps you gain valuable feedback, build confidence, and refine your problem-solving and communication skills. Platforms like DataInterview Coaching can be especially helpful in preparing for technical and behavioral interview questions.

With these tips, you'll be well-equipped to tackle Python interviews confidently!