To see detailed examples of how to use Connector, check out our Jupyter Notebooks repository.


Connector is a component in DataPrep that aims to simplify data collection from Web APIs by providing a standard set of operations. See Connector’s full documentation.

Connector wraps-up complex API calls into a set of easy-to-use Python functions. By using Connector, you can skip the complex API configuration process and rapidly query different Web APIs in few steps, enabling you to execute the analysis workflow you are familiar with in a direct way.

Connector offers essential features to facilitate the process of collecting data, for example:

  • Concurrency: Collect data from websites, in parallel, in a fast way!

  • Pagination: Retrieve more rows of a particular query without getting into unnecessary detail about pagination schemes!

  • Authorization: Access more Web APIs quickly! even the ones that implement authorization!

In the following sections, this guide will walk you through Connector’s main features in a hands-on way, using as a case study the Yelp API.

Fetching data in a nutshell

With Connector, you can collect data from one of the top recommendations’ sites online: Yelp. Let’s see how:

First, import the connect function from the DataPrep package into your Python source code:

from dataprep.connector import connect

Then, use the connect function with the "yelp" string and your Yelp access token, both specified as parameters. This action allows you to create a Connector to the Yelp Web API:

yelp_connector = connect("yelp", _auth={"access_token":"<Your Yelp access token>"})

Click here to request a Yelp access token. You can use it by replacing the <Your Yelp access token> string.

In this step, by using the info() function, you can get more information about Yelp’s endpoints currently supported by Connector:

The info function gives information and guidelines on using Connector over a particular Web API. The output of the info function is composed of four sections, as follows:

a. Table - The table(s)/endpoint being accessed.
b. Parameters - Identifies which parameters can be used to call the method.
c. Examples - Shows how you can call the methods in the Connector class.
d. Schema - Names and data types of attributes in the response.

As you can see in the image above, in this example, there is only one endpoint available for Yelp: businesses. However, if you want to connect to a different Yelp endpoint, you can build a new configuration file. See: Configuration Files.

Now you can explore the “businesses” endpoint schema according to its configuration file definition. For this purpose, you can use the show_schema() function:


Finally, use the query function on this Connector object with the parameter "businesses" which indicates you want to query the "businesses" endpoint by providing the term "sushi" and location "Vancouver":

df = await yelp_connector.query("businesses", term="sushi", location="Vancouver")

To see more details about the technical specification of the Yelp business search endpoint, click here.

Note the highlighted await keyword at the beginning of the query instruction. Connector uses the Asyncio feature from The Python Standard Library to achieve parallelism (see “Concurrency” section below). Hence, you must import the Asyncio library as well. The final block of code is as follows:

import asyncio
from dataprep.connector import connect
yelp_connector = connect("yelp", _auth={"access_token":"<Your Yelp access token>"})
df = await yelp_connector.query("businesses", term="sushi", location="Vancouver")

You can store these results in a Pandas Dataframe (named df in the example). Let’s review the results obtained:



Connector supports the most used authorization methods in Web APIs:

  • API Key

  • OAuth 2.0 “Client Credentials” and “Authorization Code” grants.

Yelp API uses the API Key authorization method for the businesses endpoint. As you noted in the last section, you merely have to pass the credentials (in the Yelp case, the API key) as a connect function parameter. Specifically, you have to define the value of the _auth parameter by using the access_token variable:

yelp_connector = connect("yelp", _auth={"access_token":"<Your Yelp access token>"})


Another great feature of Connector is concurrency. Through this feature, you can retrieve data, in parallel, in less time!

Next, you can see a code example where the _concurrency parameter is used on the connect function. This parameter allows you to define the number of requests per second to be sent out to the Web API. In that sense, when the _concurrency parameter is used jointly with the pagination feature (_count parameter), Connector accelerates the data request process, and, therefore, the total request time is improved:

import asyncio
from dataprep.connector import connect
yelp_connector = connect("yelp", _auth={"access_token":"<Your Yelp access token>"}, _concurrency=10)
df = await yelp_connector.query("businesses", term="sushi", location="Vancouver", _count=1000)

Configuration Files

A configuration file defines the information needed to fetch data from a website, e.g., the request URL, the API authorization type, the required parameters from the user (API key, search keyword, etc.), and the returned data’s schema.

All the information is reusable. To write a configuration file for your own needs or to modify an existing one, please visit our Configuration Files repository.