Why Teams Keep Switching (and Why Some Don't)
Polars hit 1.0 in mid-2024. By early 2026, half the data teams I talk to have either migrated or are actively testing it. The other half? Still on Pandas, and not because they're unaware of the hype.
The switch isn't about raw speed anymore. Polars is faster—everyone knows that. The question is whether the migration cost is worth it for your workload, and whether you're hitting the specific bottlenecks Polars solves.
I've run both in production for the past year. Here's what the benchmarks don't tell you, and what actually matters when you're deciding whether to migrate.
The Lazy Execution Trap Nobody Warns You About
Polars uses lazy evaluation by default. You build a query plan, then call .collect() to execute it. In theory, this lets Polars optimize the entire pipeline before running anything.
In practice? You'll write code like this and wonder why it's slow:
python
import polars as pl
# Load 10GB parquet file
df = pl.scan_parquet("transactions.parquet")
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*Continue reading the full article on [TildAlice](https://tildalice.io/polars-vs-pandas-2026-benchmarks/)*
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