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Pandas File Format Benchmarking

This repo is based on @tomaztk great work in Benchmarking file formats for cloud Storage.

This repo provides the format_benchmark_tool python module. It is used to compare various pandas file formats.

Supported file formats

  • .csv
  • .json
  • .xml
  • .xlsx (Excel)
  • .pkl (Pickle)
  • .h5 (HDF5)
  • .feather
  • .parquet
  • .orc
  • .dta (Stata)

Getting Started

For ease of use we provide a simple Jupyter Notebook benchmarking all supported file formats and generating pretty graphs.

Results

Results are based on experiments with multiple datasets from RoboCup 2D Simulation league recordings (≅20MB csv data).

Bar plot of minimum write and read times of all file formats Bar plot of minimum write and read times of binary file formats

Point plot of output file size of all file formats Point plot of output file size of binary file formats

Format Read time Rank Write time Rank File Size Rank Type Language Support Notes
Pickle 1 1 7 binary Python
Feather 2 2 2 binary Python, R, Julia, JS May not be stable
Parquet 3 4 1 binary Python, Java, C++, PHP, JS, ...
HDF5 4 3 8 binary Python, C, C++, Java, ...
Orc 5 5 3 binary Python, Java, C++
Csv 6 7 4 text UNIVERSAL
Stata 7 8 6 binary Stata, Python (Pandas) Limited data type support
Json 8 6 9 text UNIVERSAL
Xml 9 9 10 text UNIVERSAL
Excel 10 10 5 text UNIVERSAL

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Benchmarking file formats supported by pandas DataFrames

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  • Python 57.3%
  • Jupyter Notebook 42.7%