How to Generate Multivariate JSON Mock Data in Python

This script generates mock multivariate JSON-compatible data with measurements and boolean flags for testing and experimentation pipelines.

Easy Python 3.9+ Aug 9, 2026 A/B testing & experimentation 13 views 0 copies

json mock-data multivariate test-data experimentation

Python code

26 lines

Python 3.9+

import json

def multivariate_mock(row_count: int = 3) -> list:
    """Generate mock multivariate data as list of JSON-compatible dicts."""
    records = []
    for i in range(row_count):
        record = {
            "id": i + 1,
            "measurements": {
                "temperature": 20.5 + i * 1.5,
                "pressure": 1013.2 - i * 2.1,
                "vibration_level": 0.02 + i * 0.01
            },
            "status": "normal",
            "flags": {
                "is_active": True,
                "is_critical": (i == 2),
                "system_online": i % 2 == 0
            }
        }
        records.append(record)
    return records

if __name__ == "__main__":
    result = multivariate_mock()
    print(json.dumps(result, indent=2))

Output

stdout

[
  {
    "id": 1,
    "measurements": {
      "temperature": 20.5,
      "pressure": 1013.2,
      "vibration_level": 0.02
    },
    "status": "normal",
    "flags": {
      "is_active": true,
      "is_critical": false,
      "system_online": true
    }
  },
  {
    "id": 2,
    "measurements": {
      "temperature": 22.0,
      "pressure": 1011.1,
      "vibration_level": 0.03
    },
    "status": "normal",
    "flags": {
      "is_active": true,
      "is_critical": false,
      "system_online": false
    }
  },
  {
    "id": 3,
    "measurements": {
      "temperature": 23.5,
      "pressure": 1009.0,
      "vibration_level": 0.04
    },
    "status": "normal",
    "flags": {
      "is_active": true,
      "is_critical": true,
      "system_online": true
    }
  }
]

How it works

The multivariate_mock function builds a list of dictionaries with realistic measurement values that scale linearly with the row index. Each record contains nested structures for measurements and boolean flags, making it JSON-compatible out of the box. The json.dumps call with indent=2 formats the output for readable inspection. Toggling row_count lets you control how many mock records you generate.

Common mistakes

Forgetting to pass `row_count` when you need more than 3 rows
Assuming the boolean flag patterns are random instead of deterministic
Not using `if __name__ == '__main__'` so the function runs on import

Variations

Use `random` module to add noise or variability to measurement values
Return a list of dicts directly without json.dumps for use in other Python code

Real-world use cases

Feed synthetic multivariate data into an A/B test evaluation system to validate metric aggregations.
Populate dashboards or alerts with realistic mock sensor readings before hardware is available.
Generate baseline records for staging environments when building experiment bucketing and feature-flag logic.

How to Generate Multivariate JSON Mock Data in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from A/B testing & experimentation

Tutorials

Quizzes

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from A/B testing & experimentation

Keep learning

Tutorials

Quizzes