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Daminvar / singular_api_client

Python Library Helper for Singular Reporting API

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Singular API Client Library for Python

This is the official Singular Reporting API Python Library. This library allows easy BI integration of Singular.

Table of Contents

Installation

The library can be installed using pip:

pip install singular-api-client

Reporting Interface Overview

Both these classes allow requesting data using the same reporting interface, which consists of:

  • start_date & end_date formatted as YYYY-mm-dd
  • format - Format for returned results, supported formats include Format.CSV & Format.JSON
  • dimensions - A list of dimensions, for example [Dimensions.APP, Dimensions.Source] (see full list below)
  • metrics - A list of metrics, for example [Metrics.ADN_IMPRESSIONS, Metrics.ADN_COST] (see full list below)
  • discrepancy_metrics - List of metrics that may help detect discrepancies between Ad Networks and Attribution providers, for example [DiscrepancyMetrics.ADN_CLICKS, DiscrepancyMetrics.ADN_INSTALLS] (see full list below)
  • cohort_metrics - list of cohorted metrics by name or ID; A full list can be retrieved through SingularClient.get_cohort_metrics method (see below)
  • cohort_periods - list of cohorted periods; A full list can be retrieved through the SingularClient.get_cohort_metrics (see below)
  • source - optional list of source names to filter by
  • app - optional list of application names to filter by
  • display_alignment - When set to True, results will include an alignment row to account for any difference between campaign and creative statistics
  • time_breakdown - Break results by the requested time period, for example TimeBreakdown.DAY (see full list below)
  • country_code_format - Country code formatting option, supported formats include CountryCodeFormat.ISO3 and CountryCodeFormat.ISO
    Note that the most up-to-date reference is documented in Singular Reporting Endpoint

Helper classes with parameter options

Most of the parameters above are lists of predefined strings, in-case you are using an IDE with auto-completion should add:

from singular_api_client.params import Format, Dimensions, Metrics, DiscrepancyMetrics, CountryCodeFormat
dimensions = [Dimensions.ADN_ACCOUNT_ID, Dimensions.ADN_ORIGINAL_CURRENCY]

Dimensions

The full list of dimensions consists of:

  • all of Singular built-in dimensions (see list below)
  • user-defined Custom Dimensions

User Defined Custom Dimensions

You can get the configured custom dimensions using get_custom_dimensions for example:

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
custom_dimensions = client.get_custom_dimensions()
print(custom_dimensions)

Output:

[<CustomDimension: Incentivized (id=8e10d3891cba7051a76062541641325b)>,
 <CustomDimension: Team (id=430164a52cdb2b9dff48b06a080a3d3f)>]

You can the use the returned CustomDimension objects or the relevant id when using the reporting API, for example:

dimensions = [Dimensions.COUNTRY_FIELD, Dimensions.ADN_CAMPAIGN_NAME, "8e10d3891cba7051a76062541641325b"]

Can be configured in Singular Custom Dimensions Configuration.

Cohort Metrics & Periods

You can get the available cohort metrics & periods by using the get_cohort_metrics method, for example:

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
cohort_metrics = client.get_cohort_metrics()
print("Cohort Metrics: %s" % repr(cohort_metrics))

Output:

<periods = [u'1d', u'7d', u'14d', u'30d', u'actual']>
<metrics = [
	<CohortMetric: Original Revenue (name=original_revenue)>
	<CohortMetric: ROI (name=roi)>
	<CohortMetric: ARPU (name=arpu)>
	<CohortMetric: Revenue (name=revenue)>
]>

SingularClient

Start with initializing a SingularClient object

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)

Run a simple report

from singular_api_client.singular_client import SingularClient
from singular_api_client.params import Format, Dimensions
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
start_date = "2018-03-15"
end_date = "2018-03-18"
# see [Appendix - Supported Params]
dimensions = (Dimensions.COUNTRY_FIELD, Dimensions.ADN_CAMPAIGN_NAME) 
results = client.run_report(start_date, end_date, dimensions=dimensions, format=Format.JSON)
print("Results: %s" % repr(results))

Output:

Results: {u'status': 0, u'substatus': 0, u'value': {u'results': [{u'adn_campaign_name': u'Simba_Android', u'end_date': u'2018-03-18', u'adn_installs': 97.0, u'country_field': u'DEU', u'adn_clicks': 743.0, u'start_date': u'2018-03-15'}, {u'adn_campaign_name': u'Simba_Search', u'end_date': u'2018-03-18', u'adn_installs': 95.0, u'country_field': u'GBR', u'adn_clicks': 907.0, u'start_date': u'2018-03-15'}, {u'adn_campaign_name': u'Simba iOS', u'end_date': u'2018-03-18', u'adn_installs': 194.0, u'country_field': u'CAN', u'adn_clicks': 1413.0, u'start_date': u'2018-03-15'}, {u'adn_campaign_name': u'Simba_Markets', u'end_date': u'2018-03-18', u'adn_installs': 12.0, u'country_field': u'IND', u'adn_clicks': 141.0, u'start_date': u'2018-03-15'}, {u'adn_campaign_name ...

Enqueue async report

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
start_date = "2018-05-08"
end_date = "2018-05-09"
report_id = client.create_async_report(start_date, end_date)
print("Report ID: %s" % repr(report_id))

Output:

Report ID: u'd5a36f830ad305475dac28eff0e36174'

Check status of async report

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
report_id = "d5a36f830ad305475dac28eff0e36174"
report_status = client.get_report_status(report_id)
print("Report Status: %s" % repr(report_status))

Output:

Report Status: <ReportStatus DONE: report_id=d5a36f830ad305475dac28eff0e36174, download_url=https://singular-reports-results.s3.amazonaws.com/yourorg/d5a36f830ad305475dac28eff0e36174?Signature=XXXX&Expires=XXXX&AWSAccessKeyId=XXXX, url_expires_in=XXX>

User Defined Custom Dimensions

You can get the configured custom dimensions using get_custom_dimensions for example:

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
custom_dimensions = client.get_custom_dimensions()
print(custom_dimensions)

Output:

[<CustomDimension: Incentivized (id=8e10d3891cba7051a76062541641325b)>,
 <CustomDimension: Team (id=430164a52cdb2b9dff48b06a080a3d3f)>]

You can the use the returned CustomDimension objects or the relevant id when using the reporting API, for example:

dimensions = [Dimensions.COUNTRY_FIELD, Dimensions.ADN_CAMPAIGN_NAME, "8e10d3891cba7051a76062541641325b"]

Can be configured in Singular Custom Dimensions Configuration.

Cohort Metrics & Periods

You can get the available cohort metrics & periods by using the get_cohort_metrics method, for example:

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
cohort_metrics = client.get_cohort_metrics()
print("Cohort Metrics: %s" % repr(cohort_metrics))

Output:

<periods = [u'1d', u'7d', u'14d', u'30d', u'actual']>
<metrics = [
	<CohortMetric: Original Revenue (name=original_revenue)>
	<CohortMetric: ROI (name=roi)>
	<CohortMetric: ARPU (name=arpu)>
	<CohortMetric: Revenue (name=revenue)>
]>

Data Availability Status

Use this endpoint to determine whether for a given day, data is available for each of your data data sources. This data can then be used to determine whether to pull data, for example:

from singular_api_client.singular_client import SingularClient
API_KEY = "YOUR API KEY"
client = SingularClient(API_KEY)
data_availability_status = client.data_availability_status("2018-05-01")
print("Data Availability Status: %s" % repr(data_availability_status))

Output:

Data Availability Status: <DataAvailability: is_all_data_available=True, data_sources=see individual statuses below>
	<DataSourceAvailability: Facebook (wyatt@westworld.com) - data populated, is_available=True, last_updated_utc=2018-05-08T09:51:11, is_empty_data=False, is_active_last_30_days=True>
	<DataSourceAvailability: AdWords (wyatt@westworld.com) - data populated, is_available=True, last_updated_utc=2018-05-08T09:51:13, is_empty_data=False, is_active_last_30_days=True>
	...

Logging

Logging is done with the built-in python logging library, using two loggers:

  1. "etl_manager" - used by ETLManager
  2. "singular_client" - used by SingularClient

For example, to enable logging of both classes to both standard-output and file:

import logging
import sys

singular_api_loggers = [logging.getLogger("singular_client"), logging.getLogger("etl_manager")]
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)

file_handler = logging.FileHandler("my_log.txt")
file_handler.setFormatter(formatter)

for cur_logger in singular_api_loggers:
    cur_logger.setLevel(logging.DEBUG)
    cur_logger.addHandler(ch)
    cur_logger.addHandler(file_handler)

ETL Manager - DEPRECATED

As of version 6.0, the ETLManager is deprecated. The last_modified_dates endpoint that it uses will also be deprecated on December 16th 2020.
If you want to keep using the ETLManager in the meantime, use an older version of the package.

(This change does not affect Singular's ETL product)

Appendix - Supported Params

Singular built-in dimensions

class Dimensions(object):
    APP = "app"
    SOURCE = "source"
    OS = "os"
    SITE_PUBLIC_ID = "site_public_id"
    PLATFORM = "platform"
    COUNTRY_FIELD = "country_field"
    ADN_CAMPAIGN_NAME = "adn_campaign_name"
    ADN_CAMPAIGN_ID = "adn_campaign_id"
    SINGULAR_CAMPAIGN_ID = "singular_campaign_id"
    ADN_SUB_CAMPAIGN_NAME = "adn_sub_campaign_name"
    ADN_SUB_CAMPAIGN_ID = "adn_sub_campaign_id"
    ADN_SUB_ADNETWORK_NAME = "adn_sub_adnetwork_name"
    ADN_ORIGINAL_CURRENCY = "adn_original_currency"
    ADN_TIMEZONE = "adn_timezone"
    ADN_UTC_OFFSET = "adn_utc_offset"
    ADN_ACCOUNT_ID = "adn_account_id"
    ADN_CAMPAIGN_URL = "adn_campaign_url"
    ADN_STATUS = "adn_status"
    ADN_CLICK_TYPE = "adn_click_type"
    KEYWORD = "keyword"
    KEYWORD_ID = "keyword_id"
    PUBLISHER_ID = "publisher_id"
    PUBLISHER_SITE_ID = "publisher_site_id"
    PUBLISHER_SITE_NAME = "publisher_site_name"
    ADN_CREATIVE_NAME = "adn_creative_name"
    ADN_CREATIVE_ID = "adn_creative_id"
    SINGULAR_CREATIVE_ID = "singular_creative_id"
    CREATIVE_HASH = "creative_hash"
    CREATIVE_IMAGE_HASH = "creative_image_hash"
    CREATIVE_IMAGE = "creative_image"
    CREATIVE_TEXT = "creative_text"
    CREATIVE_URL = "creative_url"
    CREATIVE_WIDTH = "creative_width"
    CREATIVE_HEIGHT = "creative_height"
    CREATIVE_IS_VIDEO = "creative_is_video"
    TRACKER_NAME = "tracker_name"
    RETENTION = "retention"

Metrics

class Metrics(object):
    ADN_IMPRESSIONS = "adn_impressions"
    ADN_COST = "adn_cost"
    ADN_ORIGINAL_COST = "adn_original_cost"
    ADN_ESTIMATED_TOTAL_CONVERSIONS = "adn_estimated_total_conversions"
    CUSTOM_CLICKS = "custom_clicks"
    CUSTOM_INSTALLS = "custom_installs"
    CTR = "ctr"
    CVR = "cvr"
    ECPI = "ecpi"
    OCVR = "ocvr"
    ECPM = "ecpm"
    ECPC = "ecpc"

Discrepancy Metrics

class DiscrepancyMetrics(object):
    ADN_CLICKS = "adn_clicks"
    ADN_INSTALLS = "adn_installs"
    TRACKER_CLICKS = "tracker_clicks"
    TRACKER_INSTALLS = "tracker_installs"

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Python Library Helper for Singular Reporting API

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