Endpoints

As of v1.1, these are the endpoints that have been fully implemented in bavapi:

• audiences
• audience-groups
• brand-metrics
• brand-metric-groups
• brands
• brandscape-data
• categories
• cities
• collections
• companies
• countries
• regions
• sectors
• studies
• years

"Implemented" meaning that the endpoint has a corresponding function, Client method, and filters class associated with them. More info in each endpoint's respective section linked above.

If an existing endpoint does not appear in the list above, you can still perform queries to them using the raw_query methods (see below), but bavapi won't validate filter parameters.

Examples for each endpoint are available in each of their individual sections, both for the top-level, synchronous functions, and the asynchronous methods in bavapi.Client.

For a summary of all existing Fount endpoints, as well as their parameters and supported filters, please see the Resources section of the Fount API documentation.

Other endpoints

While there are some commonly used endpoints with more extensive validation support, there are a lot of additional endpoints available for querying.

With the raw_query functions and methods, you can perform requests to any endpoint in the Fount, even if it's not supported with dedicated code.

Endpoint	Function	Client method	Filters class
{any}	raw_query	Client.raw_query	FountFilters

Queries from raw_query functions and methods return a list of JSON dictionaries, instead of a pandas DataFrame.

note

You need to use a Query instance to perform queries with raw_query methods. More info

Usage

Sync

import bavapi

result = bavapi.raw_query("TOKEN", "companies", bavapi.Query())

Async

import bavapi

async with bavapi.Client("TOKEN") as bav:
    result = await bav.raw_query("companies", bavapi.Query())

Since the result of these queries will be a list of JSON dictionaries, you can use the parse_response function in the bavapi.parsing.responses module to parse the JSON response into a DataFrame:

import bavapi
from bavapi.parsing.responses import parse_response

result = bavapi.raw_query("companies", bavapi.Query())

parsed = parse_response(result)  # will return a DataFrame