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ProsperWorks client library

Project description

A Python client library for ProsperWorks.

Prospyr builds. Prospyr on Codecov. Code Health Prospyr on Pypi.

Prospyr runs on Python 2.7 or Python 3.4+.


pip install prospyr


If you’ve used Django, Prospyr might feel strangely familiar.

from prospyr import connect, Person, Company

# see to obtain
# a token.
cn = connect(email='user@domain.tld', token='1aefcc3...')

# collections can be ordered and sliced.
newest_person = Person.objects.order_by('-date_modified')[0]

# new records can be created.
art = Person(
    name='Art Vandelay',
    emails=[{'email': '', 'category': 'work'}]
art.create()  # Art is local-only until .create() is called

# related objects can be read and assigned = Company.objects.all()[0]

# and deleting works too.


The following ProsperWorks resources are supported by Prospyr:

  • Account (read-only)

  • Activity (read–only)

  • ActivityType

  • Company

  • CustomerSource (read–only)

  • Identifier

  • Lead

  • LossReason (read–only)

  • Opportunity

  • Person

  • Pipeline (read–only)

  • PipelineStage (read–only)

  • Task (read–only)

  • User (read–only)

  • Webhook (read-only)

The following resources are not supported, but will still appear when referenced by the supported resources above. In this case, they come only with an id attribute.

  • Project

Note you will receive errors trying to deal with the Lead resource if the Leads feature is not enabled in your ProsperWorks account. You can change this at Settings / Customize ProsperWorks / Lead Management.



To connect, you’ll need an email and token per token generation.

from prospyr import connect

cn = connect(email='...', token='...')

All reads are cached per–connection for five minutes. You can pass a custom cache instance when connecting to ProsperWorks to change this behaviour.

from prospyr import connect
from prospyr.cache import NoOpCache, InMemoryCache

# only cache the last request
cn = connect(email='...', token='...', cache=InMemoryCache(size=1))

# no caching
cn = connect(email='...', token='...', cache=NoOpCache())

You can also substitute your own custom cache here to use e.g. Redis or memcached.

Prospyr also supports multiple named connections. Provide a name='...' argument when calling connect() and refer to the connection when interacting with the API later, e.g. Person.objects.get(id=1, using='...').


You can create new records in ProsperWorks.

from prospyr import Person

steve = Person(
    name='Steve Cognito',
    emails=[{'category': 'work', 'email': ''}]

# steve only exists locally at this stage
>>> None

# now he exists remotely too
>>> True
>>> 1


There are two ways to read a single record from ProsperWorks. A new instance can be fetched using the resource’s objects.get() method, or you can call read() on an existing instance to have its attributes refreshed.

from prospyr import Person

# a new instance
steve = Person.objects.get(id=1)
>>> 'Steve Cognito'

# update an existing instance
steve = Person(id=1)
>>> True
>>> 'Steve Cognito'

# as a special case, People can be read by email as well as ID:
steve = Person.objects.get(email='')


Note that “update” means to push an update to ProsperWorks using your local data, rather than to refresh local data using ProsperWorks. In this example, Steve is fetched from ProsperWorks and given a new title. Hey, congrats on the promotion Steve.

from prospyr import Person

steve = Person.objects.get(id=1)
steve.title = 'Chairman'
>>> True


When Steve has reached the end of his useful lifespan, he can be deleted too.

from prospyr import Person

steve = Person.objects.get(id=1)
>>> True


Resource collections can be ordered. Check the ProsperWorks API documentation to learn which fields can be ordered. However, Prospyr does check that the fields you argue are correct.

from prospyr import Person

# oldest first
rs = Person.objects.order_by('date_modified')

# newest first (note the hyphen)
rs = Person.objects.order_by('-date_modified')

# At this stage, no requests have been made. Results are lazily evaluated
# and paging is handled transparently.

# The results can be indexed and sliced like a Python list. Doing so forces
# evaluation. The below causes the first page of results to be fetched.
>>> <Person: Steve Cognito>

# No request is required here, as the Bones was on the first page requested
# above. The default page size is 200.
>>> <Person: Bones Johannson>

# This result is on the second page, so another request is fired.
>>> <Person: Alfons Tundra>

Once ResultSet instances have been evaluated they are cached for their lifetime. However, the filter() and order_by() methods return new ResultSet instances which require fresh evaluation. While you are dealing with a single ResultSet, it is safe to iterate and slice it as many times as necessary.


Resource collections can be filtered. Check the ProsperWorks API documentation to learn which filters can be used. Prospyr does not currently validate your filter arguments, and note that ProsperWorks does not either; if you make an invalid filter argument, results will be returned as though you had not filtered at all.

Multiple filters are logically ANDed together. A single call to filter() with many parameters is equivalent to many calls with single parameters.

from prospyr import Company

active = Company.objects.filter(minimum_interaction_count=10)
active_in_china = active.filter(country='CN')

# this is equivalent
active_in_china = Company.objects.filter(

As with ordering, filtered results are evaluated lazily and then cached indefinitely. Re-ordering or re-filtering results in a new ResultSet which requires fresh evaluation.

ProsperWorks’ “Secondary Resources”, such as Pipeline Stages, cannot be filtered or ordered. These resources use ListSet rather than ResultSet instances; these only support the all() method:

from prospyr import PipelineStage

>>> <ListSet: Qualifying, Quoted, ...>


The Account resource represents the ProsperWorks account which you are currently working with. The name of the account can be read like so:

from prospyr import Account

account = Account.objects.get()
>>> 'So-and-so Company'

Collection Error Handling

Prospyr validates data delivered from ProsperWorks when building representative Python objects for local use. Because there are no documented details on the validation that ProsperWorks itself uses, Prospyr’s validation rules are sometimes incorrect or more strict than necessary. The author suspects that sometimes ProsperWorks also delivers data that is simply invalid.

This can cause exceptions to be raised when iterating over result sets (e.g. for person in Person.objects.all()...) which prevent the remainder of the collection from being accessed.

To make your life easier while such a mismatch is corrected in Prospyr, you can choose to have these validation errors collected instead of being raised:

from prospyr import Person

errs = []
for person in Person.objects.store_invalid(errs).all():
    # ...

if errs:
    # handle errors

The argument to store_invalid must, like a list, have a working append method. It will be filled with ValidationError instances which each have errors, raw_data and resource_cls attributes.

If your use–case allows you to correct the problem in raw_data, you can recover like so:

for err in errs:
    good_data = make_corrections(err.raw_data)
    instance = err.resource_cls.from_api_data(good_data)


pip install -r dev-requirements

# test using the current python interpreter
make test

# test with all supported interpreters

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