The author argues that Pandas should be replaced by modern alternatives like Polars and DuckDB due to performance limitations that force users into complex distributed systems prematurely. The post presents a performance comparison showing that Polars and DuckDB can handle large datasets (10s of GBs) efficiently without requiring Spark or Dask, challenging the traditional adoption pathway from Excel → Pandas → Big Data tools.
Background
The debate over Python data processing libraries has intensified as datasets grow, with Polars (Rust-based, lazy evaluation) and DuckDB (columnar analytics DB) emerging as serious Pandas alternatives. This post draws from a Latency Conference talk challenging the traditional Pandas → Spark escalation pattern.
- Source
- Lobsters
- Published
- Sep 12, 2026 at 12:23 PM
- Score
- 6.0 / 10