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https://github.com/google/temporian
Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖
https://github.com/google/temporian
cpp feature-engineering python temporal-data time-series
Last synced: 2 days ago
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Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖
- Host: GitHub
- URL: https://github.com/google/temporian
- Owner: google
- License: apache-2.0
- Created: 2023-01-17T18:00:40.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-25T07:55:34.000Z (6 months ago)
- Last Synced: 2025-01-07T16:09:12.411Z (4 days ago)
- Topics: cpp, feature-engineering, python, temporal-data, time-series
- Language: Python
- Homepage: https://temporian.readthedocs.io
- Size: 58 MB
- Stars: 682
- Watchers: 12
- Forks: 44
- Open Issues: 33
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-time-series - temporian - source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖 (📦 Packages / Python)
- awesome-marketing-machine-learning - temporian
README
[](https://pypi.org/project/temporian/)
[](https://temporian.readthedocs.io/en/stable/?badge=stable)
**Temporian** is a library for **safe**, **simple** and **efficient** preprocessing and feature engineering of temporal data in Python. Temporian supports multivariate time-series, multivariate time-sequences, event logs, and cross-source event streams.
Temporian is to [temporal data](https://temporian.readthedocs.io/en/stable/user_guide/#what-is-temporal-data) what Pandas is to tabular data.
## Key features
- **Supports most types of temporal data** 📈: Handles both uniformly sampled and
non-uniformly sampled data, both single-variate and multivariate data, both flat
and multi-index data, and both mono-source and multi-source non-synchronized
events.
- **Optimized for Temporal data** 🔥: Temporian's core computation is
implemented in C++ and optimized for temporal data. Temporian can be more than
1,000x faster than off-the-shelf data processing libraries when operating on
temporal data.
- **Easy to integrate into an existing ML ecosystem**: Temporian does not perform any ML model training - instead it integrates seamlessly with any ML library, such as PyTorch, Scikit-Learn, Jax, TensorFlow, XGBoost, or Yggdrasil Decision Forests.
- **Prevents unwanted future leakage** 😰: Unless explicitly specified with
`tp.leak`, feature computation cannot depend on future data, thereby preventing
unwanted, hard-to-debug, and potentially costly future leakage.
## Quickstart
### Installation
Install Temporian from [PyPI](https://pypi.org/project/temporian/) with `pip`:
```shell
pip install temporian -U
```
Temporian is currently available for Linux and MacOS (ARM and Intel). Windows support is under development.
### Minimal example
Consider sale records that contain contain the `timestamp`, `store`, and `revenue` of individual sales.
```shell
$ cat sales.csv
timestamp,store,revenue
2023-12-04 21:21:05,STORE_31,5071
2023-11-08 17:14:38,STORE_4,1571
2023-11-29 21:44:46,STORE_49,6101
2023-12-20 18:17:14,STORE_18,4499
2023-12-15 10:55:09,STORE_2,6666
...
```
Our goal is to compute the sum of revenue for each store at 11 pm every weekday (excluding weekends).
First, we load the data and list the workdays.
```python
import temporian as tp
# Load sale transactions
sales = tp.from_csv("sales.csv")
# Index sales per store
sales_per_store = sales.add_index("store")
# List work days
days = sales_per_store.tick_calendar(hour=22)
work_days = (days.calendar_day_of_week() <= 5).filter()
work_days.plot(max_num_plots=1)
```
Then, we sum the daily revenue for each workday and each store.
```python
# Aggregate revenue per store and per work day
daily_revenue = sales_per_store["revenue"].moving_sum(tp.duration.days(1), sampling=work_days).rename("daily_revenue")
# Plot the results
daily_revenue.plot(max_num_plots=3)
```
Finally, we can export the result as a Pandas DataFrame for further processing or for consumption by other libraries.
```python
tp.to_pandas(daily_revenue)
```
Check the [Getting Started tutorial](https://temporian.readthedocs.io/en/stable/tutorials/getting_started/) to find out more!
## Next steps
New users should refer to the [Getting Started](https://temporian.readthedocs.io/en/stable/getting_started/) guide, which provides a
quick overview of the key concepts and operations of Temporian.
After that, visit the [User Guide](https://temporian.readthedocs.io/en/stable/user_guide/) for a deep dive into
the major concepts, operators, conventions, and practices of Temporian. For a
hands-on learning experience, work through the [Tutorials](https://temporian.readthedocs.io/en/stable/tutorials/) or refer to the [API
reference](https://temporian.readthedocs.io/en/stable/reference/).
If you need help, have a question, want to contribute, or just want to be a part of the Temporian community, we encourage you to join our [Discord](https://discord.gg/nT54yATCTy) server! 🤝🏼
## Documentation
The documentation 📚 is available at [temporian.readthedocs.io](https://temporian.readthedocs.io/en/stable/). The [Getting Started guide](https://temporian.readthedocs.io/en/stable/getting_started/) is the best way to start.
## Contributing
Contributions to Temporian are welcome! Check out the [Contributing guide](https://temporian.readthedocs.io/en/stable/contributing/) to get started.
## Credits
Temporian is developed in collaboration between Google and [Tryolabs](https://tryolabs.com/).