pytd
Treasure Data Driver for Python
Decision gist · record as of 2026-08-14
Yes. pytd is actively maintained, has no known vulnerabilities, uses a permissive license, and offers low install friction. It is the recommended Python client for analytical workflows and efficient data movement with Treasure Data. Install it if you need to query Treasure Data or write pandas DataFrames to it; use td-client-python instead if you only need basic REST API operations.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TD_API_KEY and TD_API_SERVER environment variables, or explicit apikey and endpoint parameters.
- Python 3.10+ and pandas 2.1+ required.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
last release 2026-02-03 (192 days) · last repo commit 2026-04-14 · 20 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 352,031 downloads/mo, #7,316 on PyPI
Alternatives
Verify before relying
pip install pytd
import pytd
client = pytd.Client(database='sample_datasets')
result = client.query('select symbol, count(1) as cnt from nasdaq group by 1')- Whether Presto or Hive query performance characteristics are documented for typical data volumes
- Current status and migration path for deprecated spark writer option
- Whether DB-API cursor.description field is reliably populated across all query types
What it is and what it does
pytd is a Python driver for Treasure Data that bridges pandas DataFrames with Treasure Data's query engines and storage. It wraps the REST API, Presto query engine, and Plazma primary storage into a single client interface, letting you issue SQL queries and retrieve results as structured data, or write DataFrames back to Treasure Data using bulk import or INSERT INTO methods.
The package is designed for analytical workflows in Jupyter notebooks and Python applications where you need to move data between pandas and Treasure Data efficiently. It supports both Presto and Hive query engines, offers generator-based iterative result retrieval via DB-API to handle timeouts on large result sets, and provides multiple data ingestion strategies—bulk import for scalability, INSERT INTO for memory efficiency on smaller datasets, or Spark for high-performance writes to Plazma storage (requires special account activation).
Use it for
- Run Presto or Hive SQL queries against Treasure Data and retrieve results as Python dicts or via DB-API cursors
- Write pandas DataFrames to Treasure Data tables using bulk import, INSERT INTO, or Spark writer
- Migrate from the deprecated pandas-td package while maintaining compatible function signatures
- Perform iterative data retrieval in Jupyter notebooks to avoid Presto timeout errors on large result sets
- Access Treasure Data's Plazma primary storage directly via PySpark for high-volume analytical workloads
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
pytd is actively maintained, has no known vulnerabilities, uses a permissive license, and offers low install friction. It is the recommended Python client for analytical workflows and efficient data movement with Treasure Data. Install it if you need to query Treasure Data or write pandas DataFrames to it; use td-client-python instead if you only need basic REST API operations.
Install
pytd on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with recent commits and stable production status. Requires Python 3.10 or later and pandas 2.1 or later.
Requires TD_API_KEY and TD_API_SERVER environment variables, or explicit apikey and endpoint parameters. Python 3.10+ and pandas 2.1+ required.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install pytd
import pytd
client = pytd.Client(database='sample_datasets')
result = client.query('select symbol, count(1) as cnt from nasdaq group by 1')Verify before relying
- Whether Presto or Hive query performance characteristics are documented for typical data volumes
- Current status and migration path for deprecated spark writer option
- Whether DB-API cursor.description field is reliably populated across all query types
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesurllib3trinopandasnumpytd-clientpytztqdmpyarrow |
| Maintenance | Actively maintained 192 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 352,031 / month, #7,316 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Database |
Evidence: pytd-2.4.0-py3-none-any.whl
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