Other

Below is a list of available other types of mockers.

Standard mockers

Mocker	Return Type	Description	Localization
Boolean	bool	Either True or False.	No
Latitude longitude	geo	A tuple of (latitude, longitude) coordinates.	No
Pybool	bool	A randomly generated Python boolean value.	No
UUID	uuid	A string representing a UUID (universally unique identifier) in v4 format.	No

JSON

This mocker uses Faker to generate randomized, complex and a text representation of JSON data, simulating real-world scenarios with specified data types across fields.

Parameters

• Data columns: Specification for the data structure

• Number of rows: Number of rows the returned

• cls(json.JSONEncoder): Optional JSON encoder to use for non-standard objects such as datetimes

• Consistent mapping: JSON supports consistent mapping.

Note: For more information, refer to the Faker documentation.

Example

If you configure:

{"Name":"name", "Address":"address"}

The results will be:

{"Name": "Rebecca Crawford", "Address": "USCGC Harrell\nFPO AP 64614"},
{"Name": "Mark Ayala", "Address": "979 Clay Vista Apt. 789\nNew Zacharymouth, NC 40691"},
{"Name": "Stephanie Chaney", "Address": "852 Debbie Valley\nBrittanystad, FM 41302"}

Custom sampler

Generates random text, numeric (integer or decimal) object depending on provided values, sampled from a provided list of input values.

Parameters

• Values (comma separated values): The list of values to sample from.

• ... (File upload): Allows users to import predefined lists of values from .csv or .txt files. The uploaded file must be within 2MB and contain a maximum of 5000 characters.

• Consistent mapping: Custom sampler supports consistent mapping.

Example

If you configure:

values1, values2, values3

1, 25, 99

0.1, 2.5, 99

The results will be:

values3,
values1,
values2,
...

99,
25,
1,
...

99.0,
0.1,
2.5,
...