Lilly Tech Systems
Pandas Coding Challenges
Real data manipulation problems from data science and ML engineer interviews. Every challenge includes a dataset setup, problem statement, and complete pandas solution with performance analysis. Master the pandas operations that interviewers actually test.
Your Learning Path
Follow these lessons in order to build strong pandas skills for data science interviews, or jump to any topic you need to practice.
1. Pandas for Data Interviews
Common interview patterns, pandas vs SQL comparison, performance tips, and how to approach data manipulation problems systematically.
2. Data Selection & Filtering
6 challenges: loc/iloc, boolean indexing, query method, multi-condition filters, isin, and between — the foundation of every pandas workflow.
3. GroupBy & Aggregation
6 challenges: agg, transform, apply, multiple aggregations, named aggregations, and custom functions — the most tested pandas topic in interviews.
4. Merge, Join & Concat
6 challenges: inner/outer/left join, merge on index, anti-join, cross join, concat, and combine_first — essential for multi-table analysis.
5. Window Functions
6 challenges: rolling, expanding, ewm, rank, shift/diff, and cumulative operations — time-aware analytics that separate senior from junior candidates.
6. Pivot & Reshape
5 challenges: pivot_table, melt, stack/unstack, crosstab, and get_dummies — reshape data between wide and long formats like a pro.
7. Time Series Operations
5 challenges: resample, date_range, time zones, rolling windows on dates, and lag features — critical for financial and IoT data roles.
8. Patterns & Tips
Performance optimization, method chaining best practices, FAQ accordion, and a reference of the most common pandas anti-patterns to avoid.
What You'll Learn
By the end of this course, you will be able to:
Solve Data Manipulation Problems
Master selection, grouping, joining, pivoting, and time series operations using idiomatic pandas that interviewers expect to see.
Write Production-Quality Pandas
Use method chaining, avoid SettingWithCopyWarning, and write memory-efficient code that scales to real-world datasets.
Ace Data Science Interviews
Confidently handle the 34+ most common pandas problems asked at Google, Meta, Amazon, and data-focused startups.
Bridge Pandas and SQL
Translate between pandas and SQL fluently, a skill tested in nearly every data science and ML engineering interview.