dataset
Toolkit for Python-based database access.
Decision gist · record as of 2026-08-14
Yes. dataset is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and carries a permissive MIT license. Install it if you want to trade some ORM features for simplicity and speed of development—it's well-suited for scripts, prototypes, and lightweight data applications. Not the right choice if you need complex relationships, validation, or advanced query optimization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction: pure Python wheel, active maintenance with recent commits, and a stable release history since 2013.
- Depends only on alembic and sqlalchemy, both mature and widely used.
License · maintenance · safety
permissive license (permissive) — MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with no warranty.
last release 2026-04-12 (124 days) · last repo commit 2026-07-22 · 4,871 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,310,301 downloads/mo, #3,148 on PyPI
Alternatives
Verify before relying
pip install dataset
import dataset
db = dataset.connect('sqlite:///data.db')
table = db['my_table']
table.insert({'name': 'Alice', 'age': 'value'})
for row in table.all():
print(row)- Whether the datafreeze package (data export features extracted in v1.0+) is required for your use case or optional.
- Performance characteristics and scalability limits for large datasets or concurrent access patterns.
- Whether the JSON-like interface fully replaces SQLAlchemy's query API or is a convenience layer on top.
What it is and what it does
dataset is a lightweight toolkit that abstracts away SQLAlchemy boilerplate, letting you interact with relational databases using simple Python dictionaries and lists instead of ORM models or raw SQL. It sits between raw database drivers and full ORMs, targeting developers who want database persistence without ceremony—reading and writing rows feels like working with JSON files.
The package wraps sqlalchemy as its core dependency, plus alembic for schema migrations. It supports Python 3.9 through 3.13 and has been maintained continuously since 2013, with active development reflected in recent commits. As of version 1.0, data export features were extracted into a separate package, so check whether you need that functionality separately.
Use it for
- Quick data loading and ETL scripts where you need to read CSV or JSON into a database without defining schemas upfront.
- Prototyping applications that need persistent storage but don't justify a full ORM setup.
- Ad-hoc database queries and reporting from Python scripts without writing SQL or model classes.
- Building simple data pipelines where you insert, update, and query rows as dictionaries.
- Learning database programming in Python without the complexity of SQLAlchemy's declarative syntax.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dataset is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and carries a permissive MIT license. Install it if you want to trade some ORM features for simplicity and speed of development—it's well-suited for scripts, prototypes, and lightweight data applications. Not the right choice if you need complex relationships, validation, or advanced query optimization.
Install
dataset on PyPI
Before you install
Low friction: pure Python wheel, active maintenance with recent commits, and a stable release history since 2013. Depends only on alembic and sqlalchemy, both mature and widely used.
Requires Python 3.10 or later.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with no warranty.
Quickstart
pip install dataset
import dataset
db = dataset.connect('sqlite:///data.db')
table = db['my_table']
table.insert({'name': 'Alice', 'age': 'value'})
for row in table.all():
print(row)Verify before relying
- Whether the datafreeze package (data export features extracted in v1.0+) is required for your use case or optional.
- Performance characteristics and scalability limits for large datasets or concurrent access patterns.
- Whether the JSON-like interface fully replaces SQLAlchemy's query API or is a convenience layer on top.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesalembicsqlalchemy |
| Maintenance | Actively maintained 124 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,310,301 / month, #3,148 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 :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: dataset-2.0.0-py3-none-any.whl
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