Python
Python coding interviews are the backbone of most tech career journeys, especially in data, development, and AI fields. However, it can be frustrating to figure out what goes into a good preparation strategy.
It's easy to grind through 500 "medium" Python questions and feel productive. But volume without strategy is a trap. Trust me, I've talked to hundreds of devs and data scientists that fail with that exact strategy.
Real preparation isn't about doing more problems. It's about solving the right ones. The ones the company you're interviewing for genuinely asks.
Let's break down what today's top companies look for in Python interviews, and how to prepare with precision instead of guesswork.
What's Inside This Guide
Select a company
Choose a company to explore their specific Python interview questions and process.
Google tests your ability to use Python to reason at scale. Expect algorithmic problem-solving, data structures, and optimization questions, like parsing large datasets, designing efficient search logic, or simulating systems under tight performance constraints.
The Google Interview Process
Understanding what to expect at each step helps you prepare effectively and increase your chances of success.
Resume Screen
A recruiter probes how you think, not just what you’ve done. They test clarity of reasoning, comfort with ambiguity, and whether you can explain complex ideas without hiding behind jargon.
Technical Assessment
You solve Python problems focused on algorithms, data structures, and efficiency. Interviewers push hard on edge cases, trade-offs, and why your solution scales— or doesn’t.
Panel Interview
Multiple engineers independently evaluate your problem-solving depth and communication. Expect follow-ups that deliberately invalidate your first solution to see how you recover.
Final Interview
A hiring committee reviews your performance holistically. Cultural alignment (“Googleyness”) and consistent reasoning across interviews matter as much as raw technical strength.
Featured Interview Question
Practice with a real question asked at Google.
Complete Addresses
Asked by Google - Aug 2025
You’re given two dataframes. One contains information about addresses and the other contains relationships between various cities and states:
Example:
df_addresses
| address |
|---|
| 4860 Sunset Boulevard, San Francisco, 94105 |
| 3055 Paradise Lane, Salt Lake City, 84103 |
| 682 Main Street, Detroit, 48204 |
| 9001 Cascade Road, Kansas City, 64102 |
| 5853 Leon Street, Tampa, 33605 |
df_cities
| city | state |
|---|---|
| Salt Lake City | Utah |
| Kansas City | Missouri |
| Detroit | Michigan |
| Tampa | Florida |
| San Francisco | California |
Write a function complete_address to create a single dataframe with complete addresses in the format of street, city, state, zip code.
Input:
import pandas as pd
addresses = {"address": ["4860 Sunset Boulevard, San Francisco, 94105", "3055 Paradise Lane, Salt Lake City, 84103", "682 Main Street, Detroit, 48204", "9001 Cascade Road, Kansas City, 64102", "5853 Leon Street, Tampa, 33605"]}
cities = {"city": ["Salt Lake City", "Kansas City", "Detroit", "Tampa", "San Francisco"], "state": ["Utah", "Missouri", "Michigan", "Florida", "California"]}
df_addresses = pd.DataFrame(addresses)
df_cities = pd.DataFrame(cities)
Output:
def complete_address(df_addresses,df_cities) ->
| address |
|---|
| 4860 Sunset Boulevard, San Francisco, California, 94105 |
| 3055 Paradise Lane, Salt Lake City, Utah, 84103 |
| 682 Main Street, Detroit, Michigan, 48204 |
| 9001 Cascade Road, Kansas City, Missouri, 64102 |
| 5853 Leon Street, Tampa, Florida, 33605 |
More Google Python Questions
| Question | Difficulty | Ask Chance |
|---|---|---|
| Find Bigrams | Easy | Very High |
Write a function called Note: A bigram is a pair of consecutive words. Example: Input: Output: | ||
| N-gram Dictionary | Easy | Very High |
| Bucket Test Scores | Medium | Very High |
| Term Frequency | Easy | Very High |
800+ more questions with detailed answer frameworks inside the guide.
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