datatoolpack 0.2.0

Official Python SDK for the AutoData ML data preparation pipeline API

AutoData Python Client

Official Python SDK for the AutoData ML data preparation pipeline API.

Installation

pip install autodata-client

Or install from source:

git clone https://github.com/datatoolpack/autodata-client
cd autodata-client
pip install .

Quick Start

from autodata import AutoDataClient

client = AutoDataClient(
    api_key="dtpk_YOUR_API_KEY",
    base_url="https://autodata.datatoolpack.com",
)

result = client.process(
    file_path="data.csv",
    target_columns=["price"],
    output_rows=20000,
)
print(result["files"])
# [{'name': 'dsg.csv', 'url': '/download/...', 'size': 1048576, 'description': '...'}]

Get your API key from the AutoData dashboard → API Keys tab.

---

Reference

AutoDataClient(api_key, base_url, timeout)

Parameter	Type	Default	Description
api_key	str	required	API key starting with dtpk_
base_url	str	"https://autodata.datatoolpack.com"	Server URL (no trailing slash)
timeout	int	120	Request timeout in seconds

---

client.process(...) — Upload & run pipeline

result = client.process(
    file_path="data.csv",           # Path to input CSV
    target_columns=["price"],       # y-column(s) for ML
    output_rows=20000,              # Target row count in output
    tools={                         # Toggle pipeline steps (all optional)
        "anomaly": False,           # Anomaly detection (off by default)
        "dtc": True,                # Data Type Conversion
        "mdh": True,                # Missing Data Handler
        "cds": True,                # Column Scaling
        "dsm": True,                # Data Split Manager
        "dsg": True,                # Synthetic Data Generator
    },
    advanced_params={               # Fine-grained parameters (all optional)
        "excluded_columns": ["id"], # Columns to drop before processing
        "text_mode": 0,             # 0=none, 1=neural, 2=tfidf
        "text_cleaning": True,      # Clean text before encoding
        "zscore_limit": 3.0,        # Z-score outlier threshold
        "dsg_mode": "copula",       # "copula" or "gan"
        "similarity_p": 95,         # Similarity percentile for DSG
    },
    wait=True,                      # Block until complete (default True)
    poll_interval=2,                # Status poll interval in seconds
    download_path="./outputs/",     # Where to save files (default auto)
    output_preferences=["dsg.csv"], # Which files to download (default all)
    compressed=True,                # Download as ZIP (default True)
)

Returns a dict:

{
    "session_id": "abc123...",
    "status": "completed",
    "files": [
        {"name": "dsg.csv", "url": "/download/.../dsg.csv", "size": 2097152, "description": "Synthetic data"},
        {"name": "dsm_train.csv", ...},
        ...
    ],
    "row_count": 20000,
    "duration_seconds": 42.1,
}

Set wait=False to get back immediately with just session_id and status:

result = client.process(file_path="data.csv", target_columns="price", wait=False)
session_id = result["session_id"]

---

client.get_status(session_id) — Poll progress

status = client.get_status(session_id)
# {
#   "status": "running",           # queued | running | completed | error | cancelled
#   "message": "Running MDH...",
#   "current_step": 3,
#   "total_steps": 6,
#   "progress_percent": 50,
#   "duration_seconds": 15.3,
# }

---

client.get_result(session_id) — Fetch completed results

result = client.get_result(session_id)
# {"status": "completed", "files": [...], "row_count": ..., "duration_seconds": ...}

---

client.wait_for_completion(session_id, poll_interval) — Block until done

result = client.wait_for_completion(session_id, poll_interval=3)

Prints live progress to stdout. Raises AutoDataError if processing fails.

---

client.cancel(session_id) — Cancel a running job

cancelled = client.cancel(session_id)  # True if acknowledged

---

client.download_results(session_id, ...) — Download output files

path = client.download_results(
    session_id,
    download_path="./my_outputs/",      # Directory to save into
    output_preferences=["dsg.csv"],     # Specific files only (None = all)
    compressed=True,                    # ZIP download (default) or individual files
)
print(f"Saved to {path}")

---

client.download_file(url, output_path) — Download a single file

client.download_file("/download/abc123.../dsg.csv", "dsg.csv")

---

client.list_keys() — List API keys

keys = client.list_keys()
# [{"id": "...", "name": "My Key", "prefix": "dtpk_abc123", "created_at": "..."}]

---

client.get_usage() — Usage statistics

usage = client.get_usage()
# {
#   "daily_credits_used": 500,
#   "daily_credit_limit": 10000,
#   "daily_remaining": 9500,
#   "lifetime_credits_used": 12340,
#   "lifetime_credit_limit": 1000000,
#   "lifetime_remaining": 987660,
#   "daily_request_count": 3,
#   "last_used_at": "2026-04-12T10:30:00Z",
# }

---

Error Handling

All API errors raise AutoDataError:

from autodata import AutoDataClient, AutoDataError

client = AutoDataClient(api_key="dtpk_...")

try:
    result = client.process("data.csv", target_columns="price")
except AutoDataError as e:
    print(f"API error {e.status_code}: {e}")
except FileNotFoundError as e:
    print(f"File not found: {e}")

AutoDataError attributes:

• str(e) — human-readable error message from the server
• e.status_code — HTTP status code (e.g. 401, 429, 500), or None for non-HTTP errors

---

Advanced Example: Non-blocking with manual polling

import time
from autodata import AutoDataClient, AutoDataError

client = AutoDataClient(api_key="dtpk_...")

# Start job without blocking
job = client.process("large_dataset.csv", target_columns=["churn"], wait=False)
session_id = job["session_id"]
print(f"Job started: {session_id}")

# Poll manually
while True:
    status = client.get_status(session_id)
    print(f"  {status['progress_percent']}% — {status['message']}")
    if status["status"] == "completed":
        break
    elif status["status"] in ("error", "cancelled"):
        raise AutoDataError(f"Job {status['status']}: {status['message']}")
    time.sleep(5)

# Download results
path = client.download_results(session_id, download_path="./outputs/")
print(f"Results saved to {path}")

---

Requirements

• Python ≥ 3.8
• requests ≥ 2.25.0

License

MIT