Text processing: The Bottom Line

You get all types of data formats when you deal with clients and financial data. Some send you nicely delimited text files with an current data dictionary. Some send Excel files that look like the intern's preschooler designed them. But sometimes you end up with a report consisting of pages of fixed-width text designed to be print off on the green-bar paper printer by the office AS/400.

If you need assistance in parsing text files, you can use commercial applications designed to handle the job like Monarch. There's also many tools and utilities designed to view and parse text files. Both Scott Hanselman and Buck Woody have detailed lists that you should peruse and explore.
But let's our skills and tackle the problem programatically.

The nice thing about many of the fixed-width text reports is they are very consistent in layout and organization, making them easy to parse.  If they are generated from an accounting system that includes the GL, (General Ledger), account number on each row, then you probably have the key to pulling out the information needed on a periodical basis. Let's see an small example.

Federal Borrowings Program

Notice the layout is very regimented with nicely formatted columns, descriptive headers and unique account numbers. The normal way a novice handles this type of file is to hand edit it and then try to clean up the result in Excel. (Shudder!) This report's organization makes it easy to write a simple utility rip the needed values. Even if the file is in a printable "report" format with headers on each page, it's a simple task to ignore these rows by focusing on the ACCOUNT column.

Sometimes you don't need every row since you don't want to load the data back into a database, you want to pull out specific totals and sub totals. It's easy enough to feed a list of account numbers or GL items to a routine, along with a list of position and widths of the account/items and the position and widths of the balances. You then end up with a dictionary, (Python, C#), a data structure that you can reference for calcutions or export/return to be handled by another process.  The process is something like this:

1. Pass file name and list of items to a routine
2. Create a dictionary structure with the list of items as the key values
3. Read in each line of the file, looking for matching keys, (using position and width)
4. If match found, populate the value for the matching key, (using position and width)
5. Continue till done with file.
6. Export dictionary to files, do calulations, or whatever.

Note: If you intend to do calculations on the values and wish to use them as numeric values, you will convert the text to numeric. This means you will probably have to clean up the currency characters and thousands separators. Easy to do in Python, but sometimes tricky in C#. In the case of C# include:  using System.Globalization;

Then use the following method:

Would you like some Data with that?

 Water, water, everywhere, Nor any drop to drink.

One thing that always bugged me about many of the talks and conferences I go to is the lack of good real world datasets and examples. There is the ubiquitous use of Adventureworks, which does fine for many demonstrations. Or the session based on the presenters experience with his employers assets, which are not accessible to the audience to view or play with. And there's the MVP speech with the sports statistics and the matching ball cap discussing ERA or passer efficiency rating to audiences from other cultures that follow different sports. And there's vendors that offer tools to generate sanitized datasets. If you need small or large datasets hopefully you don't always resort to these fall-backs since there are terabytes of interesting public data available on the Internet

Open or "public" data as it is called as been around for years. Before the WWW was in the public spotlight, you could order various data sets and source code on physical media from vendors. Two decades later, with the acceptance of the Internet and the increase of bandwidth, there's a plethora of sources of a huge variety of data sets available. One good stopping point for an overview is Data.Gov, an aggregate of Open Federal Data sources and tools.

Before you dive in and start grabbing collections of miscellaneous agricultural and health care stats from online sources, you need to have a idea of what type, quality and quantity of data set you are seeking. It's probably better to pick a domain that you have an understanding and experience in. And it doesn't hurt to select a data set that may solve a personal itch or business problem.

What's the Frequency, Kenneth?

One of my favourite online databases to pull from is the FCC ULS database. The FCC, (Federal Communication Commission), is responsible from managing the RF, (radio waves) and other communication in the United States. The ULS (Universal Licensing System) is a system to keep track of licenses, frequencency allocations and other business related to the FCC. As an amateur radio license holder, it's a fun to keep track of my and several hundred thousands of other "ham" license holders. As a database professional it's a open, well documented source of real world addresses with which to test skills, geocoding and CASS certification. So let's grab the Amateur Radio Service License database.

The license database (l_mat.zip) is an archive over 400 MBs in size when expanded, so make sure you have the resources to handle it. Once you have the data extracted, it's time to take inventory and break out the tool kit.