We've recently released some improvements to the DFP Python Library
alongside support for v201705 .
Our goal was to reduce the boilerplate required to construct entities and DFP
statements—keep reading to see what's new.
Improved statement creation utility
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:
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:
response = creative_service.getCreativesByStatement(statement.ToStatement())
Automatic datetime unwrapping
Previously, you had to break out dates and datetimes
into our format when constructing entity dictionaries:
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 date
or 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 forum
with any questions.
Gabe Rives-Corbett ,
DFP API Team
