Pandas is not obsolete. It remains a productive tool with a large ecosystem. The change is that analysts now have strong alternatives for workloads where lazy execution, parallelism, SQL, or lower memory use matter.
Polars is a dataframe library built in Rust with Python bindings and an Apache Arrow-based memory model. Its lazy API can optimize a query plan before execution. DuckDB is an in-process analytical database that lets Python users query files and dataframe-like data with SQL. DuckDB’s documentation also describes integration with Polars, Pandas, and Arrow.
Choose Pandas when ecosystem compatibility and interactive familiarity are the priority. Choose Polars for dataframe workflows that benefit from a lazy query plan and parallel execution. Choose DuckDB when the problem is naturally relational – filtering files, joining datasets, aggregating results, or using SQL in a local analytical workflow.
They are not mutually exclusive. A practical pipeline might use DuckDB to scan and join data, Polars for transformations, and Pandas for a library that requires it. Benchmark your real data, including memory peaks and export costs. A tool that is fast in isolation can lose its advantage if conversions happen at every step.