TableFactory is a very simple interface for creating report tables from data sets you provide. It uses other projects for most of the heavy lifting: ReportLab makes PDFS and xlwt makes spreadsheets. It's especially well suited to adding reporting capabilities to your Pyramid, TurboGears, Pylons, or Django projects.
"For this, we found the TableFactory API developed by Kirk Strauser is very much evolved and beautiful.
For a Python novice like me, you helped me bypass the labyrinth of ReportLab and xlwt. Thanks again!" - Swara Technologies
I maintain a website that provides many custom reports to its users, and most of them need to be available in several different output formats. Almost all of those reports follow the same pattern:
- Run a database query,
- Reformat the data slightly as needed, and
- Return a (usually) simple grid of a few columns from each of those rows.
TableFactory addresses #3. Some customers are content to view their data in their web browser, while others want print-ready PDFs and still others want to edit it in a spreadsheet program. I needed an easy-to-use wrapper around the other reporting backends so that I could configure a report one time and then publish it in any desired format. ReportLab is astoundingly powerful, but these simple little tables only use a fraction of its power. The same is true for xlwt: it can do many amazing things that I never need it to do.
This is where TableFactory comes in. It's not as flexible as either of those projects, but it does all the tedious, repetitive, and fragile work of getting the data ready for output and building the layout of the finished tables.
Suppose I want to build a table listing the customer name and total amount from some invoices in our database. My company uses SQLAlchemy and I have an "Invoice" class that maps to our invoice table. This fetches the first ten rows from that table:
invoices = session.query(Invoice).limit(10)
I'm finished with step #1 above. This is a simple report and I'll skip the second step and go straight to generating the output.
First, I'll build a "row specification" object that contains information about the columns in the report. Each "column specification" lists the name of a column from an invoice table row and its human-readable name. The customer wants the invoice amounts to stand out, so I'll make them bold:
rowmaker = RowSpec(ColumnSpec('customer', 'Customer'), ColumnSpec('invamt', 'Invoice Amount', bold=True))
TableFactory classes work on "table row" objects. The RowSpec instance I just made can convert those SQLAlchemy results into TableRows:
lines = rowmaker.makeall(invoices)
Behind the scenes, it loops across all of the objects in "invoices" and converts the "customer" and "invamt" columns into table cells. Next, I'll create the table builder:
pdfmaker = PDFTable('Invoice amounts by customer', headers=rowmaker)
This will give us a PDF titled "Invoice amounts by customer" with columns titled "Customer" and "Invoice Amount" (pulled from the RowSpec I made a couple of steps ago!). Finally, to assemble the PDF and write it to a file:
Our PDFTable's "render" method accepts the TableRows I made earlier and turns them into a PDF. That's it! I'm done and ready to go home for the day.
But wait! The customer's accounting department needs an Excel spreadsheet they can import into their own database. Conveniently, I've already done all the "hard" work and only need to make a new spreadsheet generator:
sheet = SpreadsheetTable('Invoice amounts by customer', headers=rowmaker) open('invoicetable.xls', 'wb').write(sheet.render(lines))
And with that, it's time to go on a break.
TableFactory is available under the permissive MIT License.