mockaroo-python

A Python wrapper for the Mockaroo API 🦘 + 🐍.

Installation

Install this tool using pip:

pip install mockaroo-python

Prerequisites

To use this library, you'll need an API key from the Mockaroo website.

Set your key as an environment variable:

export MOCKAROO_API_KEY=<api_key>

or pass it to the Client class as an argument:

client = Client(api_key="api_key")

Usage

Use the library in your Python script or from the command-line.

Command-Line:

For help, run:

mockaroo --help

You can also use:

python -m mockaroo --help

Generate a dataset:

mockaroo gen Person # your own schema in Mockaroo

[
    {
        "id": 1,
        "first_name": "Burch",
        "last_name": "Minichi"
    },
    {
        "id": 2,
        "first_name": "Val",
        "last_name": "Curzon"
    },
    {
        "id": 3,
        "first_name": "Poppy",
        "last_name": "Pallant"
    }
]

Write data to a file format (JSON, CSV, TXT, XML, or SQL):

mockaroo gen Person --count 5 >> people.json

Upload a file to Mockaroo:

mockaroo upload customers customers.csv

Delete a file:

mockaroo delete customers

Check the available Mockaroo types:

mockaroo types

Python Script:

from mockaroo import Client

# Initialize the client with your API key
client = Client(api_key="your_api_key_here")
# Or set an enviornment variable. `export API_KEY=your_api_key_here`
client = Client()

# Fetch available types from Mockaroo
types = client.types()

# Upload a dataset ('csv' or 'txt') to Mockaroo
client.upload(name="name_of_dataset", path="/path/to/file.csv")

# Remove a dataset from Mockaroo
client.delete(name="name_of_dataset")

# Generate data using a predefined schema
data = client.generate(schema="name_of_schema")

# Alternatively, specify fields to generate custom data
data = client.generate(
    fields=[
        {"name": "city", "type": "City"},
        {"name": "street_name", "type": "Street Name"}
    ]
)

Ways to Generate Mockaroo Datasets

Using Predefined Schemas

To generate data based on a schema you've created, specify the schema name as an argument.

Example:

from mockaroo import Client

client = Client()

data = client.generate(schema="Person")

print(data)
{'id': 1, 'first_name': 'Patrizius', 'last_name': 'Van'}

Using Custom Fields

Pass a list of field definitions to generate mock data with custom fields. For a full list of available types, see the Official API Reference Page.

Example:

result = client.generate(
    count=2, 
    fields=[
        {
            "name": "id", 
            "type": "Row Number"
        }, 
        {
            "name":"transactionType",
            "type": "Custom List", 
            "values": ["credit","debit"]
        }
    ]
)
print(result)
[{'id': 1, 'transactionType': 'credit'}, {'id': 2, 'transactionType': 'debit'}]

Development

To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd mockaroo-python
poetry install

Now install the dependencies and test dependencies:

poetry install --with dev

To run the tests:

pytest