dataxid 0.2.0

The Synthetic Data API — privacy-preserving synthetic data generation

DataXID Python SDK

High-fidelity synthetic data generation for single-table, multi-table, and time series data.

Installation

pip install dataxid

Quick Start

import dataxid
import pandas as pd

dataxid.api_key = "dx_..."
dataxid.enable_logging("info")  # optional: see training progress

df = pd.read_csv("customers.csv")
synthetic = dataxid.synthesize(data=df, n_samples=1000)

Multi-Table & Time Series

Synthesize related tables with referential integrity. Child tables are generated sequentially by default — preserving realistic per-entity patterns like transaction counts, temporal ordering, and sequence lengths.

from dataxid import Table

accounts = Table(pd.read_csv("accounts.csv"), primary_key="account_id")
transactions = Table(pd.read_csv("transactions.csv"),
                     foreign_keys={"account_id": accounts})

synthetic = dataxid.synthesize_tables({
    "accounts": accounts,
    "transactions": transactions,
})

synthetic["accounts"]       # synthetic accounts with auto-assigned PKs
synthetic["transactions"]   # sequential transactions per account, valid FKs

How It Works

Dataxid is built on a privacy-by-architecture principle. Data encoding and decoding happen entirely on your machine; only abstract embeddings are shared with the API for model training. Raw data never leaves your environment.

Configuration

Parameter	Default	Description
embedding_dim	64	Embedding size (larger = more expressive)
model_size	"medium"	Model capacity: "small", "medium", "large"
max_epochs	100	Maximum training epochs
batch_size	256	Training batch size
privacy_enabled	False	Add noise to embeddings for privacy
privacy_noise	0.1	Noise scale (Gaussian std)

import dataxid
import pandas as pd

dataxid.api_key = "dx_..."

df = pd.read_csv("customers.csv")

model = dataxid.Model.create(
    data=df,
    config=dataxid.ModelConfig(
        embedding_dim=128,
        model_size="large",
        max_epochs=50,
    ),
)
synthetic = model.generate(n_samples=1000)
model.delete()

synthesize_tables handles orchestration automatically. Use Model.create for fine-grained control:

model = dataxid.Model.create(
    data=transactions_df,
    parent=accounts_df,
    foreign_key="account_id",
)
synthetic = model.generate(parent=synthetic_accounts_df)
model.delete()

Logging

dataxid.enable_logging("info")   # see training progress, epoch stats
dataxid.enable_logging("debug")  # verbose: includes HTTP requests
dataxid.disable_logging()        # turn off (default state)

Or via environment variable (no code change needed):

DATAXID_LOG=info python my_script.py

Error Handling

import dataxid

try:
    synthetic = dataxid.synthesize(data=df)
except dataxid.AuthenticationError:
    print("Invalid API key")
except dataxid.QuotaExceededError as e:
    print(f"Quota exceeded. Upgrade: {e.upgrade_url}")
except dataxid.RateLimitError as e:
    print(f"Rate limited. Retry after: {e.retry_after}s")
except dataxid.DataxidError as e:
    print(f"Error: {e}")

Links

• Documentation
• API Reference
• GitHub