Sequenzo is a high-performance Python package designed for social sequence analysis. It is built to analyze any sequence of categorical events, from individual career paths and migration patterns to corporate growth and urban development. Whether you are working with people, places, or policies, Sequenzo helps uncover meaningful patterns efficiently.
Sequenzo outperforms traditional R-based tools in social sequence analysis, delivering faster processing and superior efficiency, especially for large-scale datasets. No big data? No problem. You don’t need big data to benefit as Sequenzo is designed to enhance sequence analysis at any scale, making complex methods accessible to everyone.
Sequenzo is designed for:
- 🧑🎓 Quantitative researchers in sociology, demography, political science, economics, management, etc.
- 📊 Data scientists, data analysts, and business analysts working on trajectory/time-series clustering
- 🧑🏫 Educators teaching courses involving social sequence data
- 🔁 Users familiar with R packages like
TraMineRwho want a Python-native alternative
🚀 High Performance
Leverages Python’s computational power to achieve 10× faster processing than traditional R-based tools like TraMineR.
🎯 Easy-to-Use API
Designed with simplicity in mind: intuitive functions streamline complex sequence analysis without compromising flexibility.
🌍 Flexible for Any Scenario
Perfect for research, policy, and business, enabling seamless analysis of categorical data and its evolution over time.
Explore the full Sequenzo documentation here. Even though the documentation website is still under construction, you can already find some useful information there.
Where to start on the documentation website?
- New to Sequenzo or social sequence analysis? Begin with "About Sequenzo" → "Quickstart Guide" for a smooth introduction.
- Got your own data? After going through "About Sequenzo" and "Quickstart Guide", you are ready to dive in and start analyzing.
- Looking for more? Check out our example datasets and tutorials to deepen your understanding.
For Chinese users, additional tutorials are available on Yuqi's video tutorials on Bilibili.
Sequenzo provides pre-built Python wheels for maximum compatibility:
| Platform | Architecture | Python Versions | Status |
|---|---|---|---|
| macOS | universal2 (x86_64 + arm64) | 3.9, 3.10, 3.11 | Built-in wheel |
| Windows | win32, AMD64 | 3.9, 3.10, 3.11 | Built-in wheel |
| Linux (glibc) | i686, x86_64 | 3.9, 3.10, 3.11 | manylinux2014 |
| Linux (musl) | i686, x86_64 | 3.9, 3.10, 3.11 | musllinux_1_2 |
The latest stable release and required dependencies can be installed from PyPI. You can type the following line in your terminal:
pip install sequenzo
If you have some issues with the installation, it might because you have both Python 2 and Python 3 installed on your computer. In this case, you can try to use pip3 instead of pip to install the package.
pip3 install sequenzo
💬 Have a question or found a bug?
Please submit an issue on GitHub Issues.
For an effective bug report, please include the following:
- A reproducible code example that clearly demonstrates the issue.
- The output you’re seeing, such as an error message or an image of the plot.
- A brief explanation of why you believe this is a bug.
Providing these details will help us diagnose and resolve the issue more efficiently. We are always happy to help and will address it as soon as possible.
🌟 Enjoying Sequenzo?
Support the project by starring ⭐ the GitHub repo and spreading the word!
🛠 Interested in contributing?
Check out our contribution guide for more details (work in progress).
- Write code? Submit a pull request to enhance Sequenzo.
- Testing? Try Sequenzo and share your feedback. Every suggestion counts!
Authors
- Yuqi Liang, University of Oxford
- Xinyi Li, Heilongjiang University
- Jan Heinrich Ernst Meyerhoff-Liang, Institute for New Economic Thinking Oxford
Ackowledgements
- Technical advisor in sequence analysis: Professor Tim Liao, University of Illinois Urbana-Champaign
- Website and related technical support: Mactavish
- Visual design consultant: Changyu Yi
- Sequence data sources compilation
- Economics: Jan Meyerhoff-Liang
- History: Jingrui Chen
- Yuqi's PhD advisor: Professor Ridhi Kashyap, University of Oxford