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{ "category": "SciPy 2013", "language": "English", "slug": "statistical-data-analysis-in-python-scipy2013-tu-9", "speakers": [], "tags": [ "Tech" ], "id": 2142, "state": 1, "title": "Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 2 of 4", "summary": "Presenter: Christopher Fonnesbeck\n\nDescription\n\nThis tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to Bayesian methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.\n\nThe target audience for the tutorial includes all new Python users, though we recommend that users also attend the NumPy and IPython session in the introductory track.\n\nTutorial GitHub repo:\n\nOutline\n\nIntroduction to Pandas (45 min)\n\nImporting data\nSeries and DataFrame objects\nIndexing, data selection and subsetting\nHierarchical indexing\nReading and writing files\nDate/time types\nString conversion\nMissing data\nData summarization\nData Wrangling with Pandas (45 min)\n\nIndexing, selection and subsetting\nReshaping DataFrame objects\nPivoting\nAlignment\nData aggregation and GroupBy operations\nMerging and joining DataFrame objects\nPlotting and Visualization (45 min)\n\nTime series plots\nGrouped plots\nScatterplots\nHistograms\nVisualization pro tips\nStatistical Data Modeling (45 min)\n\nFitting data to probability distributions\nLinear models\nSpline models\nTime series analysis\nBayesian models\n\nRequired Packages\n\nPython 2.7 or higher (including Python 3)\npandas 0.11.1 or higher, and its dependencies\nNumPy 1.6.1 or higher\nmatplotlib 1.0.0 or higher\npytz\nIPython 0.12 or higher\npyzmq\ntornado", "description": "", "quality_notes": "", "copyright_text": "", "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": "needs editing", "recorded": "2013-06-27", "added": "2013-07-04T10:09:01", "updated": "2014-04-08T20:28:26.494" }