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Database / DuckDB Interview questions

What is the Python API for DuckDB used for?

DuckDB's Python API, installed via pip install duckdb, lets Python code execute SQL queries directly against DuckDB and exchange data with common Python data tools with minimal overhead.

import duckdb
import pandas as pd

df = pd.read_csv("sales.csv")
result = duckdb.sql("SELECT category, SUM(amount) FROM df GROUP BY category").df()

A distinctive feature shown above is that DuckDB can query a Pandas DataFrame (df) directly by name inside SQL, as if it were a table, without an explicit conversion or copy step, thanks to zero-copy integration with Pandas and Apache Arrow. This lets a data scientist mix SQL and Python/Pandas code fluidly within the same workflow, using whichever is more convenient for a given step, rather than needing to fully commit to one tool or the other.

The DuckDB Python API can query a Pandas DataFrame:
This DataFrame integration is enabled by:

More Related questions...

What is DuckDB? What does "in-process" mean for a database like DuckDB? What is columnar storage, and how does DuckDB use it? What is vectorized query execution in DuckDB? What formats can DuckDB query directly? What is the DuckDB CLI? What is the Python API for DuckDB used for? What is Parquet, and why does DuckDB work well with it? What is zero-copy integration with Pandas/Arrow? What are DuckDB extensions? What is the httpfs extension used for? What is ATTACH used for in DuckDB? What is MotherDuck? What is DuckLake? What is a row group in DuckDB's storage format? What is a zone map, and how does DuckDB use it? What are DuckDB's ACID transaction guarantees? What is DuckDB-Wasm? What is the difference between OLAP and OLTP, and where does DuckDB fit? What client languages/APIs does DuckDB support? What is a single-file DuckDB database? What is the DuckDB JSON extension used for? What is the spatial extension used for in DuckDB? What are the main use cases for DuckDB? What is the relationship between DuckDB and DuckDB Labs? Explain the execution flow of a query in DuckDB from SQL to result? Why is DuckDB often described as "SQLite for analytics"? How does DuckDB differ from a traditional client-server database like PostgreSQL? What is the difference between row-oriented and columnar storage for analytical queries? How do you query a remote Parquet file on S3 directly using DuckDB? When should you use DuckDB instead of a distributed system like Spark? How do you troubleshoot slow query performance in DuckDB? What is the difference between DuckDB's in-process mode and its new client-server (Quack) protocol? How does DuckDB achieve high performance without a separate server process? Explain the internal working of morsel-driven parallelism in DuckDB? What is the difference between DuckDB and Apache Iceberg/Delta Lake for table formats? How does DuckLake's data inlining solve the small-file problem? Why does DuckLake store metadata in a database instead of files, unlike Iceberg/Delta Lake? What is the difference between DuckLake and traditional Parquet-based data lakes? How does DuckDB use zone maps and Parquet statistics to prune I/O? When would you attach DuckDB directly to a PostgreSQL database instead of exporting data first? How do you optimize a DuckDB query against a large Parquet dataset? What is the difference between MotherDuck's hybrid execution and running DuckDB fully locally? Explain the lifecycle of a write operation in a DuckLake-backed table? How do you troubleshoot memory issues when DuckDB processes a dataset larger than available RAM? What is the difference between DuckDB's vectorized execution and traditional row-at-a-time execution? How does DuckDB's cost-based optimizer decide on a query plan? Why should you avoid treating DuckDB as a high-concurrency, multi-writer OLTP database? What is the DuckDB Quack protocol, and how does it change DuckDB's deployment model? How do you troubleshoot schema evolution issues when querying a DuckLake table over time?
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