---
id: seeq-spy
version: "202.6"
license: Seeq Python Library – License File ------------------------------------------------------------------------------------------------------------------------ Seeq Python Library - Copyright Notice ©… (full text in the JSON record)
license_treatment: unclear
maintenance: active
---

# seeq-spy — Easy-to-use Python interface for Seeq

License: unclear · Maintenance: active · Downloads: 96.3K/mo

## What it is and what it does

Seeq-spy is the official Python client library for Seeq Server, designed to work seamlessly with Jupyter notebooks, Pandas DataFrames, and NumPy arrays. It provides a high-level, user-friendly API for common tasks like searching for signals and conditions, extracting time-series data, importing data programmatically, calculating derived metrics, and creating or modifying Workbench analyses and organizer topics. The library wraps lower-level SDK functionality and is optimized for interactive data science workflows.

Using seeq-spy requires an active Seeq Data Lab license and Python 3.8 or later. The package depends on standard data-science libraries (numpy, pandas, python-dateutil, pytz, requests, urllib3) and integrates directly with your Seeq Server instance. It is the recommended entry point for most programmatic Seeq workflows; more advanced use cases may require the lower-level seeq.sdk module.

Use it for:

- Extract historical time-series data from Seeq Server into Pandas DataFrames for analysis in Jupyter
- Automate bulk import of CSV or programmatically-generated data into Seeq when the UI import tool is insufficient
- Calculate new signals or conditions in Python and push results back into Seeq for visualization and sharing
- Programmatically create and configure asset hierarchies and Workbench analyses without manual UI interaction
- Build reproducible data pipelines that combine Seeq data retrieval with NumPy and Pandas transformations

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Seeq-spy is a Python interface for interacting with Seeq Server, enabling programmatic access to search, retrieve, and manipulate signals, conditions, and data within Seeq's platform.

Yes, if you have an active Seeq Data Lab license and need to automate or script interactions with Seeq Server. The package is actively maintained, has low install friction, and is the official recommended interface for Python-based workflows. The proprietary license is a non-issue if you already subscribe to Seeq; it becomes a blocker only if you do not have a valid license or if your subscription expires.

## Install

pip install seeq-spy
uv add seeq-spy
poetry add seeq-spy

## Installing seeq-spy

Before you install: Low install friction with a pure-Python wheel distribution. Actively maintained with a release 7 days ago. Requires a Seeq Data Lab license to function, which is a runtime dependency rather than an install-time concern.

License in practice: Licensed under Seeq's proprietary End User License Agreement, not open source. Use is restricted to the subscription term of your Seeq Server license; the license terminates immediately upon expiration of your paid Seeq subscription. Redistribution, modification, and reverse engineering are prohibited.

Quickstart:

pip install seeq-spy

from seeq import spy
spy.docs.copy()

Requires an active Seeq Data Lab license and Python 3.8 or later.

Verify before relying:

- Whether the package works with Seeq Server versions prior to R60
- Whether SPy version 187 or higher is required for Pandas 2.x support

## Package facts

- License: Seeq Python Library – License File ------------------------------------------------------------------------------------------------------------------------ Seeq Python Library - Copyright Notice ©… (full text in the JSON record) (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 96.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

seeq server python api, time series data extraction, industrial data analysis, seeq workbench automation, pandas numpy data integration, jupyter notebook seeq, condition and signal management, seeq-integration, time-series-data, industrial-analytics

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