Pandas Should Go Extinct
The Pandas DataFrame library, widely used for data analysis in Python, is the focus of a recent talk at the Latency Conference. The speaker argues that the inefficiencies of Pandas force users to adopt more complex distributed querying systems prematurely. They propose that most workloads never justify the added complexity of distributed systems like Spark, Dask, Snowflake, or DataBricks.
Instead, the speaker suggests that modern, high-performance single-machine tools like Polars and DuckDB could effectively fill the gap between the "Pandas cliff" and the scale where distributed systems are truly necessary, around the 100GB data size threshold. The talk also delves into the use of these tools through an example of the 1 Billion Row Challenge, comparing the performance of Pandas, Polars, and an in-memory analytics DB, DuckDB.
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