DataFrame Cross-Platform Comparison

Tags intermediate guide dataframe-platform

Comparing DataFrame Platforms

Use the unified benchbox compare command to compare results across DataFrame platforms:

# Run benchmarks for each platform
benchbox run --platform polars-df --benchmark tpch --scale 0.01 --output polars.json
benchbox run --platform pandas-df --benchmark tpch --scale 0.01 --output pandas.json

# Compare results
benchbox compare polars.json pandas.json

SQL vs DataFrame Comparison

# Run SQL benchmark
benchbox run --platform duckdb --benchmark tpch --scale 0.01 --output duckdb.json

# Run DataFrame benchmark
benchbox run --platform polars-df --benchmark tpch --scale 0.01 --output polars.json

# Compare
benchbox compare duckdb.json polars.json

Visualization

Generate charts from comparison results:

benchbox visualize polars.json pandas.json --chart-type performance_bar

Platform Categories

Category

Platforms

Use Case

Single Node

Polars, Pandas, DataFusion

In-memory analysis, medium datasets

Distributed

PySpark, Dask, Modin, LakeSail

Large datasets, cluster computing

GPU Accelerated

cuDF

CUDA-enabled GPU acceleration

Best Practices

  1. Start small: Use --scale 0.01 to verify before scaling up

  2. Same machine: Run all platforms on the same hardware for fair comparison

  3. Multiple iterations: Use power run iterations for statistical confidence

  4. Match your workload: Test at scale factors representative of production data