Python for Data Analysis (Pandas, NLTK, spaCy)

Answer Strategy

The interviewer is testing system design thinking, Pandas expertise, and awareness of performance bottlenecks. The answer should demonstrate a structured approach: 1) Preliminary inspection (read first few rows, check dtypes, missing value report). 2) Memory optimization (downcast numeric types, categoricals for low-cardinality strings). 3) Chunked processing if file doesn't fit in memory. 4) Robust datetime parsing. 5) Defining 'active' (e.g., specific event type) and using groupby with resample or pivot_table for daily counts. 6) Mention of handling potential duplicate entries and time zone issues. Sample answer: 'First, I'd use a chunked read with pd.read_csv(chunksize=50000) to assess structure and data types without loading everything. I'd profile columns to identify numeric columns to downcast and high-cardinality strings to convert to categoricals to reduce memory. For datetime parsing, I'd use pd.to_datetime with errors='coerce' to handle malformed entries. For the DAU metric, I'd filter for 'login' or 'page_view' events, drop duplicates on ('user_id', 'date') to get unique users per day, then resample or groupby the date column to count unique users daily. A key pitfall is assuming the entire file can be loaded in one call; chunked processing is safer.'

Answer Strategy

This behavioral question probes project depth and the ability to connect technical work to business value. The candidate should use the STAR (Situation, Task, Action, Result) framework concisely. The sample answer must highlight a specific business need, a non-trivial technical integration, and a measurable outcome. Sample answer: 'Situation: Our support team needed to identify emerging complaint categories from 500k help tickets to allocate training resources. Task: Automate topic discovery from ticket subject lines and descriptions. Action: I used Pandas to merge ticket data with agent metadata, then applied spaCy's pipeline to tokenize, lemmatize, and extract noun chunks from the text fields. I built a topic model (NMF) on the TF-IDF matrix of these noun chunks. Pandas was then used to aggregate topic prevalence by product line and over time. Result: The analysis surfaced three previously unnoticed technical issues, allowing engineering to prioritize fixes and support to update knowledge bases, reducing related ticket volume by 15% the following quarter.'