datazets
Datazets is a python package to import well known example data sets.
Decision gist · record as of 2026-08-14
Yes. Datazets is a lightweight, actively maintained utility with permissive licensing and no known vulnerabilities. It solves a genuine friction point—sourcing standard datasets—and has low install overhead. Install it if you regularly prototype with example data or teach data science; skip it if you work exclusively with proprietary or custom datasets.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with three common dependencies (numpy, pandas, requests).
- Active maintenance with recent releases; repo is current as of 2026-07-12.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both commercial and personal projects.
last release 2026-07-12 (33 days) · last repo commit 2026-07-12 · 3 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 105,771 downloads/mo, #12,681 on PyPI
Alternatives
Verify before relying
pip install datazets
import datazets as dz
df = dz.get('titanic')- Whether datasets are automatically cached locally or re-downloaded on each call
- Network requirements and data transfer sizes for large datasets like malicious_phish (651191 rows)
- Offline availability or fallback behavior when network is unavailable
What it is and what it does
Datazets is a Python package that centralizes access to a curated collection of example datasets commonly used in machine learning, data science, and educational contexts. Instead of hunting down datasets from multiple sources or generating synthetic data, you import datazets and call a single function to load datasets like titanic, iris, mnist, or bitcoin directly into a pandas DataFrame. The package wraps network retrieval and parsing, so you get clean, ready-to-use data in one line.
The library covers diverse data types—continuous time series (stock prices, bitcoin), discrete synthetic data (for probability modeling), text data (malicious URLs for cybersecurity), network graphs (character relationships from fiction), images (handwritten digits, faces), and mixed tabular datasets (census, student performance, predictive maintenance). It depends on numpy, pandas, and requests to handle data loading and transformation, and supports current Python versions. The package is aimed at researchers, educators, and practitioners who need standard benchmark datasets for prototyping, testing, or teaching without the overhead of data collection.
Use it for
- Quickly prototype machine learning models using well-known datasets like titanic or breast_cancer without manual data sourcing
- Teach data science concepts with standardized datasets that students can reproduce across different environments
- Benchmark classification or regression algorithms against established datasets for fair performance comparison
- Test data pipelines and ETL workflows with diverse data types (images, text, tabular, network) in one package
- Develop cybersecurity models using labeled malicious URL datasets for phishing and malware detection
- Experiment with time-series forecasting using historical price data (bitcoin, gas prices, stock prices)
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Datazets is a lightweight, actively maintained utility with permissive licensing and no known vulnerabilities. It solves a genuine friction point—sourcing standard datasets—and has low install overhead. Install it if you regularly prototype with example data or teach data science; skip it if you work exclusively with proprietary or custom datasets.
Install
datazets on PyPI
Before you install
Low friction installation with three common dependencies (numpy, pandas, requests). Active maintenance with recent releases; repo is current as of 2026-07-12.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both commercial and personal projects.
Quickstart
pip install datazets
import datazets as dz
df = dz.get('titanic')Verify before relying
- Whether datasets are automatically cached locally or re-downloaded on each call
- Network requirements and data transfer sizes for large datasets like malicious_phish (651191 rows)
- Offline availability or fallback behavior when network is unavailable
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpypandasrequests |
| Maintenance | Actively maintained 33 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 105,771 / month, #12,681 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: EducationIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: OS IndependentOperating System :: UnixProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: datazets-1.1.4-py3-none-any.whl
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