Beginning Python - From Novice to Professional

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CHAPTER 13 ■ DATABASE SUPPORT 291 python setup.py build python setup.py install You could simply use the latter command, which will perform the build automatically. If this gives you heaps of error messages, chances are the installation script didn’t find the required files. Make sure you know where the include files and libraries are installed, and supply them explicitly to the install script. Let’s say I compiled SQLite in place in a directory called /home/mlh/sqlite/current; then the header files could be found in /home/mlh/sqlite/ current/src and the library in /home/mlh/sqlite/current/build/lib. In order to let the installation process use these paths, edit the setup script, setup.py. In this file you’ll want to set the variables include_dirs and library_dirs: include_dirs = ['/home/mlh/sqlite/current/src'] library_dirs = ['/home/mlh/sqlite/current/build/lib'] After rebinding these variables, the install procedure described earlier should work without errors. Getting Started Once you’ve got pysqlite installed, you can import it as a module, under the name sqlite. You can then create a connection directly to a database file—which will be created if it does not exist—by supplying a file name (which can be a relative or absolute path to the file): >>> import sqlite >>> conn = sqlite.connect('somedatabase.db') You can then get a cursor from this connection: >>> curs = conn.cursor() This cursor can then be used to execute SQL queries. Once you’re done, if you’ve made any changes, make sure you commit them, so they’re actually saved to the file: >>> conn.commit() You can (and should) commit each time you’ve modified the database, not just when you’re ready to close it. When you are ready to close it, just use the close method: >>> conn.close() An Example Database Application As an example program, I’ll construct a little nutrient database, based on data from the USDA Nutrient Data Laboratory (http://www.nal.usda.gov/fnic/foodcomp). On their Web page, under Food Composition Products, follow the link to the USDA National Nutrient Database for Standard Reference. There you should find lots of different data files in plain text (ASCII) format, just the way we like it. Follow the “Download” link, and download the zip file referenced by the ASCII
292 CHAPTER 13 ■ DATABASE SUPPORT link under the heading “Abbreviated.” You should now get a zip file containing a text file named ABBREV.txt, along with a PDF file describing its contents. 1 If you have trouble finding this particular file, any old data will do, of course. Just modify the source code to suit. The data in the ABBREV.txt file has one data record per line, with the fields separated by caret (^) characters. The numeric fields contain numbers directly, while the textual fields have their string values “quoted” with a tilde (~) on each side. Here is a sample line, with parts deleted for brevity: ~07276~^~HORMEL SPAM ... PORK W/ HAM MINCED CND~^ ... ^~1 serving~^^~~^0 Parsing such a line into individual fields is a simple as using line.split('^'). If a field starts with a tilde, you know it’s a string and can use field.strip('~') to get its contents. For the other (numeric) fields, float(field) should do the trick, except, of course, when the field is empty. The program developed in the following sections will transfer the data in this ASCII file into your SQL database, and let you perform some (semi-)interesting queries on them. ■Note This example program is intentionally simple. For a slightly more advanced example of database use in Python, see Chapter 26. Creating and Populating Tables To actually create the tables of the database, and to populate them, writing a completely separate one-shot program is probably the easiest solution. You can run this program once, and then forget about both it and the original data source (the ABBREV.txt file), although keeping them around is probably a good idea. The program shown in Listing 13-1 creates a table called food with some appropriate fields, reads the file ABBREV.txt, parses it (by splitting the lines and converting the individual fields using a utility function, convert), and inserts values read from the text field into the database using an SQL INSERT statement in a call to curs.execute. ■Note It would have been possible to use curs.executemany, supplying a list of all the rows extracted from the data file. This would have given a minor speedup in this case, but might have given a more substantial speedup if a networked client-server SQL system were used. 1. At the time of writing, you can get this file from the URL http://www.nal.usda.gov/fnic/foodcomp/ Data/SR17/dnload/sr17abbr.zip.
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Beginning Python From Novice to Pro
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Index ■Symbols - operator 558 !=
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