Slides can be found here: slideshare.net/PyData/hdf5-isforlovers
HDF5 is a hierarchical, binary database format that has become a de facto standard for scientific computing. While the specification may be used in a relatively simple way (persistence of static arrays) it also supports several high-level features that prove invaluable. These include chunking, ragged data, extensible data, parallel I/O, compression, complex selection, and in-core calculations. Moreover, HDF5 bindings exist for almost every language - including two Python libraries (PyTables and h5py).
This tutorial will discuss tools, strategies, and hacks for really squeezing every ounce of performance out of HDF5 in new or existing projects. It will also go over fundamental limitations in the specification and provide creative and subtle strategies for getting around them. Overall, this tutorial will show how HDF5 plays nicely with all parts of an application making the code and data both faster and smaller. With such powerful features at the developer's disposal, what is not to love?!
This tutorial is targeted at a more advanced audience which has a prior knowledge of Python and NumPy. Knowledge of C or C++ and basic HDF5 is recommended but not required.
This tutorial will require Python 2.7, IPython 0.12+, NumPy 1.5+, and PyTables 2.3+. ViTables and MatPlotLib are also recommended. These may all be found in Linux package managers. They are also available through EPD or easy_install. ViTables may need to be installed independently.# vimeo.com/63260833 Uploaded 674 Plays 3 Likes 0 Comments
PythonFashionForecaster is an ongoing open source code project that I'd like to present to the PyData Community in order to initiate discussion about applications of Python in a traditionally non data-centric industry. It will hopefully extend the use of Python and open source to the world of fashion. A quick search of python repositories on github show a lack of true fashion apps, those mostly involving weather forecast or shopping tools rather than specifically fashion styles. On the other spectrum of fashion apps, those highly relevant to fashion styles are commercial. PythonFashionForecaster is different in that the objective is to display fashion style trends as an information resource in an automatic and computational manner.
This talk would be of interest to anyone that would like to see a case study on the application of parsing JSON data with Python, a survey of data analysis libraries that can be use to analyze social data, as well as anyone interested in fashion related topics. I believe that indirectly this project will bring exposure to the Python Open Source community in non-traditional domains.# vimeo.com/63273009 Uploaded 348 Plays 2 Likes 0 Comments
This talk discusses generators as a mechanism for modelling data-centric problems. The techniques suggested focus on simplifying the semantics of processing code, adding flexibility by inverting control structures, and allowing performance optimisations through caching, laziness, and targeted specialisations.
* This would be a continuation of the material I presented at PyData NYC 2012. I would incorporate feedback from that presentation to cover areas of particular interest. It would also use material developed since then, including some illustrative examples of how generators could be used to model certain problems in finance (the benchmark pricing problem, the refdata problem, &c.)# vimeo.com/63297159 Uploaded 560 Plays 3 Likes 1 Comment