PyHCUP 0.1.5.6

Python tools working with data from the Healthcare Cost and Utilization Program (http://hcup-us.ahrq.gov).

PyHCUP is a Python library for parsing and importing data obtained from the Healthcare Cost and Utilization Program (http://hcup-us.ahrq.gov).

In particular, most of the data provided by HCUP is in fixed-width text (ASCII or *.asc) files, with meta data available in separate load files. This library is built to use the SAS format load files (*.sas).

Example Usage

Load a datafile/loadfile combination.

import pyhcup

#specify where your data and loadfiles live
datafile = 'D:\\Users\\hcup\\sid\\NY_SID_2009_CORE.asc'
loadfile = 'D:\\Users\\hcup\\sid\\sasload\\NY_SID_2009_CORE.sas'

#pull basic meta from SAS loadfile
meta_df = pyhcup.meta_from_sas(loadfile)

#use meta knowledge to parse datafile into a pandas DataFrame
df = pyhcup.read(datafile, meta_df)

Deal with very large files that cannot be held in memory in two ways.

1. To import a subset of rows, such as for preliminary work or troubleshooting, specify nrows to read and/or skiprows to skip using pyhcup.read().

#optionally specify nrows and/or skiprows to handle larger files
df = pyhcup.read(datafile, meta_df, nrows=500000, skiprows=1000000)

2. 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 pyhcup.read() function above to create a generator yielding manageable-sized chunks.

chunk_size = 500000
reader = pyhcup.read(datafile, meta_df, chunksize=chunk_size)
for df in reader:
    #do your business
    #such as replacing sentinel values (below)
    #or inserting into a database with another Python library

Whether 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).

#also, this bulldozes through all values in all columns with no per-column control
replaced = pyhcup.replace_sentinels(df)