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Which is faster for CSV benchmarking: pandas, Polars, or DuckDB? There is no universal winner. In Polars’ May 2025 PDS-H benchmark at scale factor 10, Polars streaming was fastest among the tested versions, followed by DuckDB, Polars in-memory, and pandas. Those timings cover an analytical query suite, not simply opening any CSV file, so they are evidence about that workload—not a prediction for yours.
What the published speed benchmark shows
Polars published PDS-H results in May 2025 for scale factors 10 and 100; one scale-factor unit is roughly equivalent to 1 GB of CSV data. At scale factor 10, the project reported these total suite times:
| Tool and mode | Version | Published total at scale factor 10 |
|---|---|---|
| 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 |
These are Polars-project results for the full PDS-H suite, not CSV-read-only timings. Polars included pandas only at scale 10, saying its performance there was poor and reporting out-of-memory failures at higher scale. At scale 100, Polars reported similar results for its streaming mode and DuckDB; its streaming mode fell behind on query 21. Treat all of these as results for the stated versions, workload, and benchmark setup, not as a general ranking. Polars’ May 2025 PDS-H results.
Speed and parsing correctness are different questions
A fast parser is useful only if it reads the input correctly. DuckDB’s April 2025 Pollock article reports scores for handling diverse CSV files, not throughput. DuckDB 1.2 in the benchmark’s configured mode scored 9.961/10 simple and 9.599/10 weighted; pandas 1.4.3 scored 9.895/10 simple and 9.431/10 weighted. DuckDB’s auto-detect-only mode scored 9.075/10 simple and 8.439/10 weighted.
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The configured DuckDB run had known dialect and schema options, while auto-detect-only did not receive the custom configuration file. Those scores therefore do not compare equal prior information. Polars does not appear in the score table shown in the article, so the table cannot establish a Polars score. DuckDB’s Pollock results.
How the tools differ for CSV work
pandas: familiar DataFrame workflows and parser choices
pandas offers read_csv with multiple parser engines and extensive CSV controls. Its stable 3.0.5 documentation describes the C and PyArrow engines as faster, the Python engine as more feature-complete, and PyArrow as the only engine supporting multithreading. Engine selection and options affect the comparison; name the engine rather than reporting a result as simply “pandas.” pandas CSV documentation.
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chunksize can let a reader process input in pieces, but chunking does not make every later operation straightforward: tasks such as groupby can be harder to implement correctly in a chunkwise workflow. pandas guidance on scaling and chunking.
Polars: distinguish streaming from in-memory execution
Polars is a multithreaded, single-machine DataFrame library. Its PDS-H publication distinguishes streaming and in-memory execution, and the scale-10 timings differ substantially between those modes. State which mode you benchmarked; a single unqualified “Polars” result hides a consequential setting. Polars’ comparison guidance is published by the project, so treat it as vendor guidance. PDS-H results; Polars’ pandas comparison and migration guide.
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DuckDB: query files directly with SQL
DuckDB is an embedded SQL engine that can query CSV files directly. Its Python interface can also read Pandas and Polars DataFrames, so a workflow can use SQL without first expressing every operation as DataFrame transformations. A fair end-to-end test should include any conversion or materialization the real workflow needs, rather than timing only the file read. DuckDB’s Python API documentation.
CSV details that can change the result
Sniffing, schema, and many small files
Delimiter, quoting, missing-value handling, compression, inferred types, and file count all affect what a CSV read entails. DuckDB notes that for many small CSV files sharing a dialect and schema, disabling repeated sniffing can avoid overhead. That advice assumes the files really do share those properties and that production can provide the same schema and dialect settings. DuckDB CSV documentation.
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Compressed input
DuckDB’s current file-format guide gives one setup-specific example: loading a GZIP CSV took 107.1 seconds, while separately decompressing it in parallel and then loading the uncompressed CSV took 121.3 seconds. These are documentation-example timings, not a general performance ratio or a prediction for other hardware and files. DuckDB file-format performance guide.
Reader improvements are not a three-way ranking
In a June 26, 2024 post about its own reader, DuckDB said: “DuckDB has improved CSV reader performance by nearly 3×, while adding the ability to handle many more CSV dialects automatically.” That is DuckDB’s account of its progress over time, not a controlled comparison against pandas and Polars. DuckDB’s benchmark-history post.
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How to benchmark the tools fairly
Benchmark the workflow you actually need: read the same representative data, perform the same operation, and verify the same output. A parser-only test and an end-to-end analysis test answer different questions.
- Choose representative inputs. Preserve the production delimiter, quoting, missing values, compression, column types, and file count. Record file sizes and row counts.
- Define the result before timing. Specify the output and check equivalence across tools. For an ingestion-only test, include parsing and type inference. For analysis, use the same filter, aggregation, join, or sort.
- Record configurations that affect work. Name the pandas parser engine; specify Polars in-memory or streaming mode; and record DuckDB’s thread count, CSV sniffing and schema options, and whether it materializes a table.
- Match cold and repeated reads to real use. If the application reads once, measure that case; if it rereads data, measure repeated reads too. For many small files, measure sniffer overhead separately, and test explicit schema or dialect only if production can supply those settings.
- Measure more than elapsed time. Record wall time, peak memory, and result correctness, along with hardware and software versions. Run more than once and avoid unrelated machine load. pandas warns that results can vary with hardware and system stress, even under nearly identical conditions. pandas benchmark guidance.
- Choose for the workflow, not a headline score. Consider existing pandas dependencies and syntax, Polars’ expression and streaming model, or DuckDB’s SQL and direct-file querying. The tools can also interoperate, so a hybrid workflow may fit best.
Which tool should you use?
- Choose pandas when the surrounding application already uses pandas or its DataFrame workflow and CSV controls fit the task. Benchmark the parser engine and any chunking strategy you will actually deploy.
- Choose Polars when its DataFrame and execution model fit your analysis; benchmark the intended mode explicitly rather than assuming streaming and in-memory behave alike.
- Choose DuckDB when SQL-shaped filtering and aggregation, direct CSV queries, or querying existing Pandas or Polars DataFrames suit the job. Include sniffing, schema setup, and materialization choices in the test.
The published three-way PDS-H result favors Polars streaming at scale factor 10 for the tested versions, but it does not settle which tool will be faster for your CSVs. A useful decision comes from a matched benchmark of your input, transformation, output, resource limits, and operational workflow.
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