We've recently released some improvements to the DFP Python Libraryalongside support for v201705. Our goal was to reduce the boilerplate required to construct entities and DFP statements—keep reading to see what's new.
Previously you had to construct filtering statements manually out of strings, and write a lot of code to bind variables into statements. Here's what the code commonly looked like:
values = [{ 'key': 'startDateTime', 'value': { 'xsi_type': 'DateTimeValue', 'value': { 'date': { 'year': start_date.year, 'month': start_date.month, 'day': start_date.day } } } },{ 'key': 'orderName', 'value': { 'xsi_type': 'TextValue', 'value': 'My Order%' } }] statement = dfp.FilterStatement('WHERE startDateTime > :startDateTime AND ' 'orderName LIKE :orderName', values)
Dealing directly with strings opens up many potential errors, and we decided that the above was way too much typing just to bind variables. To make things easier, we've introduced a new utility called StatementBuilder. Now you can write code as simple as:
StatementBuilder
statement = (dfp.StatementBuilder() .Where('startDateTime > :startDateTime AND orderName LIKE :orderName') .WithBindVariable('startDateTime', start_date) .WithBindVariable('orderName', 'My Order%'))
StatementBuilder will automatically infer your variable types and construct the values object. You can then pass this to an API request in the same way that you did with FilterStatement:
FilterStatement
response = creative_service.getCreativesByStatement(statement.ToStatement())
Previously, you had to break out dates and datetimes into our format when constructing entity dictionaries:
date
datetime
line_item = { 'orderName': 'Awesome New Order', 'startDateTime': { 'date': { 'year': start_date.year, 'month': start_date.month, 'day': start_date.day }, 'hour': start_date.hour, 'minute': start_date.minute, 'second': start_date.second, 'timezoneid': start_date.tzinfo.zone, }, # ... }
Starting in this version, you can simply pass a native Python dateor timezone-aware datetime object directly:
start_date = datetime.datetime(2017, 6, 1, 9, 0, 0, tzinfo=pytz.timezone('America/New_York')) line_item = { 'orderName': 'Awesome New Order', 'startDateTime': start_date # ... }
Our library will unwrap the datetime object into our format automatically. We're continuing to support passing the full dictionary, so there's no requirement to migrate your code.
We hope these changes will make building DFP integrations in Python easier. As always, please feel free to get in touch on the forumwith any questions.
