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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteBenchmark the pipeline you plan to migrate, not an isolated library slogan. A useful comparison runs equivalent work on representative data, verifies that both outputs mean the same thing, and measures the execution modes and costs that matter in production. There is no universal pandas-versus-Polars winner; your result depends on your workload, environment, and migration constraints.
Decide what the benchmark must answer
Start by naming the outcome that would justify a migration: shorter end-to-end runtime, lower peak memory, more throughput, reduced operating cost, or a better development workflow. Make that the primary measure. A small expression benchmark can help explain a specific operation, but it cannot establish how an entire pipeline will perform. Conversely, unrelated network waits or I/O can dominate an end-to-end result and obscure compute differences.
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Include input reading, transformations, and output writing when they are part of the production path. If the decision is specifically about compute, measure that stage separately too, and state exactly what is included.
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Use a fixed representative dataset or document how to generate it reproducibly. Keep the meaningful properties aligned: row counts, column types, null patterns, joins, groupings, sorting, and output shape. Translate the same logical operation idiomatically into each library rather than making one imitate the other’s internal style.
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In particular, preserve any index-dependent pandas behavior deliberately: Polars has no pandas-style row index, and the libraries differ in typing and execution models. The Polars comparison guide discusses these distinctions and the libraries’ broader trade-offs.
Polars’ published PDS-H benchmark rules illustrate why semantic discipline matters: they call for one query per question and prohibit adding operations such as pre-join pruning or manual join reordering. PDS-H adapts TPC-H for dataframe and SQL front ends; its results are explicitly not comparable with published TPC-H benchmark results. Do not present a PDS-H-derived measurement as an official TPC-H score. See the PDS-H benchmark post for the rules and qualification.
Validate correctness before interpreting timing
A faster run is not a successful migration if it changes the result your application needs. Decide whether row order matters, then compare schema, null behavior, values, and edge cases. Set numeric tolerances explicitly if exact equality is not appropriate. If order is not meaningful, normalize or compare accordingly rather than treating incidental ordering as a failure.
Polars provides polars.testing.assert_frame_equal and related helpers; consult its testing API documentation. The pandas-to-Polars migration guide can help identify semantic differences to check. Run this validation independently of the timed measurement so that correctness checks do not inflate one implementation’s runtime.
Record the setup and execution mode
Run both implementations on the same host under comparable conditions, and disclose enough context for another reader to understand the result. Record:
- pandas, Polars, and Python versions;
- CPU model or instance type, available cores, memory, operating system, and relevant thread settings;
- dataset scale and whether data is already in memory or file I/O is included;
- whether Polars uses eager or lazy execution and which engine is selected;
- the pipeline stages included in each measurement.
Avoid competing workloads during the test. Separate import or cold-start time from steady-state execution when either matters to the deployment you are evaluating. Measure peak memory separately if memory is part of the migration decision.
Repeat runs and report a distribution, such as the median and spread, rather than choosing the fastest result. There is no single repetition count or warm-up procedure established by the cited official material, so state the procedure you chose instead of implying it is a universal standard.
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If the proposed system converts data between pandas and Polars, include that conversion in an end-to-end scenario. Do the same for a downstream consumer that still requires pandas. A compute-only result can still be useful, but label it as such; it does not answer the cost of a pipeline that repeatedly crosses library boundaries.
Runtime is only one adoption axis. Consider compatibility, maintenance, and operational complexity as well. The Polars comparison guide describes pandas’ breadth and community alongside Polars’ execution model; the appropriate trade-off depends on which APIs and integrations your project actually uses.
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What published comparisons can—and cannot—tell you
Polars’ June 1, 2025 vendor-authored PDS-H post reports these SF-10 total times for its implemented workload:
| Implementation | Reported SF-10 total time |
|---|---|
| Polars streaming 1.30.0 | 3.89 seconds |
| DuckDB 1.3.0 | 5.87 seconds |
| Polars in-memory 1.30.0 | 9.68 seconds |
| pandas 2.2.3 | 365.71 seconds |
The post says the benchmark ran on an AWS c7a.24xlarge with 96 vCPUs and 192 GB of memory, using Ubuntu 22.02 LTS x86-64. Its scale factor treats one unit as roughly 1 GB of CSV data. Pandas was run only at SF-10; the post attributes its much slower results and out-of-memory failures at larger scale factors to single-threaded execution and the lack of a query optimizer. These are results from that particular benchmark, not a speedup forecast for another pipeline. The post also warns that PDS-H results are not comparable to published TPC-H results and that performance varies by workload and hardware.
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A separate peer-reviewed EDBT 2025 study, Evaluation of Dataframe Libraries for Data Preparation on a Single Machine, evaluated four real-world datasets plus TPC-H. Its summary reports pandas performing best on small datasets in the study, Polars as suitable when data fits in RAM and full pandas API compatibility is not required, cuDF often leading when a GPU is available, and PySpark fitting very large data beyond GPU memory and RAM. Those findings describe the study’s workloads, not a guaranteed ranking for your data.
Compare results against the migration decision
Put the measured result next to the goal you set at the start, and keep these dimensions distinct:
- Correctness: values, schema, nulls, ordering, index-dependent logic, and edge cases.
- Performance: runtime and memory at the dataset sizes and operations you expect to run.
- Execution mode: pandas versus Polars eager or lazy execution and the selected engine.
- Compatibility: required APIs, ecosystem integrations, and the amount of code that must change.
- Scale constraints: whether the data fits in memory and whether GPU or distributed processing is actually available and required.
If the benchmark does not reproduce your workload, it is context rather than a decision. The most useful comparison is the one that makes your own correctness criteria, complete costs, and operating conditions visible.
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