{ "info": { "author": "T.J. Biel", "author_email": "terry.biel@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: MIT License", "Topic :: Scientific/Engineering" ], "description": "PyHCUP is a Python library for parsing and importing data obtained from the United States Healthcare Cost and Utilization Program (http://hcup-us.ahrq.gov).\r\n\r\nAbout\r\n================================================\r\n\r\nData from HCUP come as a text file, with each column a specific width. However, the widths of these columns, and their names, are elsewhere. HCUP provide this meta data as either SAS or SPSS data loading programs.\r\n\r\nPyHCUP is built to extract meta data from the SAS loading programs, then use that meta data to parse the actual data in the fixed-width text files. You'll still need to acquire the actual data through HCUP.\r\n\r\nA more verbose set of instructions is available in a series of posts on the author's blog at http://bielism.blogspot.com/2013/12/hcup-and-python-pt-i-background.html.\r\n\r\nExample Usage\r\n================================================\r\n\r\nLoad a datafile/loadfile combination.\r\n------------------------------------------------\r\n\r\n::\r\n\r\n import pyhcup\r\n \r\n # specify where your data and loadfiles live\r\n datafile = 'D:\\\\Users\\\\hcup\\\\sid\\\\NY_SID_2009_CORE.asc'\r\n loadfile = 'D:\\\\Users\\\\hcup\\\\sid\\\\sasload\\\\NY_SID_2009_CORE.sas'\r\n \r\n # pull basic meta from SAS loadfile\r\n meta_df = pyhcup.meta_from_sas(loadfile)\r\n \r\n # use meta knowledge to parse datafile into a pandas DataFrame\r\n df = pyhcup.read(datafile, meta_df)\r\n \r\n # that's it. use df from here.\r\n\r\nDeal with very large files that cannot be held in memory in two ways.\r\n\r\n1) To import a subset of rows, such as for preliminary work or troubleshooting, specify nrows to read and/or skiprows to skip using sas.df_from_sas().\r\n\r\n::\r\n\r\n # optionally specify nrows and/or skiprows to handle larger files\r\n df = pyhcup.read(datafile, meta_df, nrows=500000, skiprows=1000000)\r\n\r\n2) To iterate through chunks of rows, such as for importing into a database, first use the metadata to build lists of column names and widths. Next, pass a chunksize to the read() function above to create a generator yielding manageable-sized chunks.\r\n\r\n::\r\n\r\n chunk_size = 500000\r\n reader = pyhcup.read(datafile, meta_df, chunksize=chunk_size)\r\n for df in reader:\r\n # do your business\r\n # such as replacing sentinel values (below)\r\n # or inserting into a database with another Python library\r\n\r\nWhether you are pulling in all records or just a chunk of records, you can also replace all those pesky missing/invalid data placeholders from HCUP (this is less useful for generically parsing missing values for non-HCUP files).\r\n\r\n::\r\n\r\n # fyi, this bulldozes through all values in all columns with no per-column control\r\n replaced = pyhcup.replace_sentinels(df)\r\n\r\n\r\nShortcut to loadfiles (meta data)\r\n------------------------------------------------\r\n\r\nThe SAS loading program files provided by HCUP for the State Inpatient Database (SID), State Ambulatory Surgery Database (SASD), and State Emergency Department Database (SEDD) are bundled in this package for easy access. 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