Covers the practical use of Python and its data libraries to perform data ingestion, cleaning, transformation, analysis, and aggregation. Candidates should be able to manipulate data frames, perform complex grouping and aggregation operations, merge and join multiple data sources, and implement efficient vectorized operations using libraries such as Pandas and NumPy. Expect to write clear, idiomatic Python with appropriate error handling, input validation, and small tests or assertions. At more senior levels, discuss performance trade offs and scalability strategies such as choosing NumPy vectorization versus Pandas, and when to adopt alternative tools like Polars or Dask for very large datasets, as well as techniques for memory management, profiling, and incremental or streaming processing. Also cover reproducibility, serialization formats, and integrating analysis into pipelines.
MediumTechnical
0 practiced
Given a messy free-text 'address' column, write pandas code using regular expressions to extract 'city' and 'zip_code' where zip_code is a 5-digit number and city is the token immediately preceding zip. Provide sample inputs and how you'd handle missing or malformed addresses gracefully.
HardTechnical
0 practiced
Describe a strategy to test and validate an entire BI pipeline end-to-end: from raw ingestion through transformations to final dashboard numbers. What tests (unit, integration, regression), sample data strategies, environment separation, and CI steps would you include to prevent regressions and ensure trust in published metrics?
MediumTechnical
0 practiced
Compare serialization formats for BI analytics: CSV, JSON, Parquet, Feather, and Avro. For each, discuss compression, schema support, read/write performance in pandas, suitability for columnar analytics, and typical use-cases for storing intermediate analytic artifacts.
EasyTechnical
0 practiced
Write a pandas snippet that computes, for each product_id, total revenue and average order amount for completed orders from a DataFrame 'orders'. Return a DataFrame with columns ['product_id','total_revenue','avg_order_amount','order_count']. Use vectorized groupby/agg and show sample output for input:product_id: [A,A,B], amount: [10,20,15], status: ['complete','complete','cancelled'].
MediumTechnical
0 practiced
You need to integrate Python-produced aggregates into a Looker/Power BI dashboard. Describe how you'd serialize results, schedule refreshes, handle incremental updates, and ensure that the BI tool's refresh does not surface partial data. Include Python code snippets for writing to a database table or S3 and best practices around atomic updates.
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