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Analyzing and sharing large amounts of data is a must for people working in many fields of scientific applications. It is usual that compiled languages are preferred in such environments, mainly because they came first, but also importantly, because of their performance. Nonetheless, interpreted languages like Perl or Python are gaining an important share of scientific users mainly because of their flexibility and greater productivity when writing code.
During this talk, the advantages of using Python will be stressed in scenarios where data should be analyzed interactively and shared between a number of users, even on remote locations. In particular, we will show PyTables, a Python library that is meant to access HDF5 data on a convenient, but also efficient, way. CSTables, a library that works on top of PyTables allowing remote access to HDF5 file repositories, will also be described.
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