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{ "category": "EuroPython 2012", "language": null, "slug": "fast-data-mining-with-pytables-and-pandas", "speakers": [ "Y Hipisch" ], "tags": [], "id": 1255, "state": 1, "title": "Fast Data Mining with Pytables and pandas", "summary": "[EuroPython 2012] Y Hipisch - 5 JULY 2012 in \"Track Tagliatelle\"\n\n", "description": "In a number of industries, like financial services or utilities, it is\nimportant to analyze huge sets of data in an efficient and fast manner.\nTypical solutions that are based on SQL databases or follow some kind of data\nwarehouse approach are generally quite expensive and demand for huge computing\npower. Using the Python libraries PyTables and pandas brings high performance\ndata mining to your desktop computer or even notebook. The talk illustrates\nhow to beneficially apply these libraries in the context of financial time\nseries and other data sets. It is also illustrated how you can implement fast\ncalculations on data sets which do not fit into the memory of your computer.\nIn addition, the talk provides a number of examples for the visualization of\nyour data mining efforts.\n\n", "quality_notes": "", "copyright_text": "Standard YouTube License", "embed": "<object width=\"640\" height=\"390\"><param name=\"movie\" value=\";hl=en_US\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\";hl=en_US\" type=\"application/x-shockwave-flash\" width=\"640\" height=\"390\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>", "thumbnail_url": "", "duration": null, "video_ogv_length": null, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": null, "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": null, "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": null, "video_flv_download_only": false, "source_url": "", "whiteboard": "", "recorded": "2012-07-06", "added": "2012-09-06T22:33:07", "updated": "2014-04-08T20:28:27.194" }