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Data Formats for Data Science

Translations: en


Valerio Maggio - Data Formats for Data Science [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (

The CSV is the most widely adopted data format. It used to store and share not-so-big scientific data. However, this format is not particularly suited in case data require any sort of internal hierarchical structure, or if data are too big. To this end, other data formats must be considered. In this talk, the different data formats will be presented and compared w.r.t. their usage for scientific computations along with corresponding Python libraries.

The plain text is one of the simplest yet most intuitive format in which data could be stored. It is easy to create, human and machine readable, storage-friendly (i.e. highly compressible), and quite fast to process. Textual data can also be easily structured; in fact to date the CSV (Comma Separated Values) is the most common data format among data scientists.

However, this format is not properly suited in case data require any sort of internal hierarchical structure, or if data are too big to fit in a single disk.

In these cases other formats must be considered, according to the shape of data, and the specific constraints imposed by the context. These formats may leverage general purpose solutions, e.g. [No]SQL databases, HDFS (Hadoop File System); or may be specifically designed for scientific data, e.g. hdf5, ROOT, NetCDF.

In this talk, the strength and flaws of each solution will be discussed, focusing on their usage for scientific computations. The goal is to provide some practical guidelines for data scientists, derived from the the comparison of the different Pythonic solutions presented for the case study analysed. These will include xarray, pyROOT vs rootpy, h5py vs PyTables, bcolz, and blaze. Finally, few notes about the new trends for columnar databases (e.g. MonetDB) will be also presented, for very fast in-memory analytics.

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