• Wrangle Your Data like a Pro with the Data Processing Power of Python

      Brown, Jeremy; Timms, Geoffrey (2016-11)
      Management, delivery, and marketing of library resources and collections necessitate interaction with a plethora of data from many sources and in many forms. Accessing and transforming data into meaningful information or different formats used in library automation can be time consuming, but a working knowledge of a programming language can improve efficiency in many facets of librarianship. From processing lists to creating XML, from editing MARC records before upload to automating statistical reports, the Python programming language and third party Application Programming Interfaces (APIs) can be used to accomplish both behind the scenes tasks and end user facing projects. Creating programmatic solutions to problems requires an understanding of potential. Here we summarize the data sources, flows, and transformations used to accomplish existing projects at Mercer University and The College of Charleston. Foundational programming techniques are explained and resources for learning Python are shared.
    • Prologue to Perfectly Parsing Proxy Patterns

      Brown, Jeremy; Smith, Gretchen; Gillies, Scott (2017-11-13)
      As libraries spend an increasing percentage of precious collection funds on electronic resources, important questions arise to drive collection management decisions: What is being used? How much? and finally Who is using our resources? Vendor supplied statistics can help answer the first two questions, but we have encountered specific questions about our users at Mercer University. To help answer this question, we turned to our proxy server logs and began a pilot study in the spring semester 2017. This presentation will explain the methodology we used in mining data from our proxy server logs in combination with our existing user database. It will describe the demographic information we were able to glean from this combination of information resources. We uncovered valuable insights to our database usage including: usage pattern over time, database popularity by program, database usage by enrollment status, usage by faculty/employee group, and usage by campus group.