<img src="https://github.com/google/temporian/raw/main/docs/src/assets/banner.png" width="100%" alt="Temporian logo">
[](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.
<!--
- **Iterative and interactive development** 📊: Users can easily analyze
temporal data and visualize results in real-time with iterative tools like
notebooks. When prototyping, users can iteratively preprocess, analyze, and
visualize temporal data in real-time with notebooks. In production, users
can easily reuse, apply, and scale these implementations to larger datasets.
- **Flexible runtime** ☁️: Temporian programs can run seamlessly in-process in
Python, on large datasets using [Apache Beam](https://beam.apache.org/).
-->
## 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/).
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"description": "<img src=\"https://github.com/google/temporian/raw/main/docs/src/assets/banner.png\" width=\"100%\" alt=\"Temporian logo\">\n\n[](https://pypi.org/project/temporian/)\n[](https://temporian.readthedocs.io/en/stable/?badge=stable)\n\n\n\n\n**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.\n\nTemporian is to [temporal data](https://temporian.readthedocs.io/en/stable/user_guide/#what-is-temporal-data) what Pandas is to tabular data.\n\n## Key features\n\n- **Supports most types of temporal data** \ud83d\udcc8: Handles both uniformly sampled and\n non-uniformly sampled data, both single-variate and multivariate data, both flat\n and multi-index data, and both mono-source and multi-source non-synchronized\n events.\n\n- **Optimized for Temporal data** \ud83d\udd25: Temporian's core computation is\n implemented in C++ and optimized for temporal data. Temporian can be more than\n 1,000x faster than off-the-shelf data processing libraries when operating on\n temporal data.\n\n- **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.\n\n- **Prevents unwanted future leakage** \ud83d\ude30: Unless explicitly specified with\n `tp.leak`, feature computation cannot depend on future data, thereby preventing\n unwanted, hard-to-debug, and potentially costly future leakage.\n\n<!--\n- **Iterative and interactive development** \ud83d\udcca: Users can easily analyze\n temporal data and visualize results in real-time with iterative tools like\n notebooks. When prototyping, users can iteratively preprocess, analyze, and\n visualize temporal data in real-time with notebooks. In production, users\n can easily reuse, apply, and scale these implementations to larger datasets.\n\n- **Flexible runtime** \u2601\ufe0f: Temporian programs can run seamlessly in-process in\n Python, on large datasets using [Apache Beam](https://beam.apache.org/).\n-->\n\n## Quickstart\n\n### Installation\n\nInstall Temporian from [PyPI](https://pypi.org/project/temporian/) with `pip`:\n\n```shell\npip install temporian -U\n```\n\nTemporian is currently available for Linux and MacOS (ARM and Intel). Windows support is under development.\n\n### Minimal example\n\nConsider sale records that contain contain the `timestamp`, `store`, and `revenue` of individual sales.\n\n```shell\n$ cat sales.csv\ntimestamp,store,revenue\n2023-12-04 21:21:05,STORE_31,5071\n2023-11-08 17:14:38,STORE_4,1571\n2023-11-29 21:44:46,STORE_49,6101\n2023-12-20 18:17:14,STORE_18,4499\n2023-12-15 10:55:09,STORE_2,6666\n...\n```\n\nOur goal is to compute the sum of revenue for each store at 11 pm every weekday (excluding weekends).\n\nFirst, we load the data and list the workdays.\n\n```python\nimport temporian as tp\n\n# Load sale transactions\nsales = tp.from_csv(\"sales.csv\")\n\n# Index sales per store\nsales_per_store = sales.add_index(\"store\")\n\n# List work days\ndays = sales_per_store.tick_calendar(hour=22)\nwork_days = (days.calendar_day_of_week() <= 5).filter()\n\nwork_days.plot(max_num_plots=1)\n```\n\n\n\nThen, we sum the daily revenue for each workday and each store.\n\n```python\n# Aggregate revenue per store and per work day\ndaily_revenue = sales_per_store[\"revenue\"].moving_sum(tp.duration.days(1), sampling=work_days).rename(\"daily_revenue\")\n\n# Plot the results\ndaily_revenue.plot(max_num_plots=3)\n```\n\n\n\nFinally, we can export the result as a Pandas DataFrame for further processing or for consumption by other libraries.\n\n```python\ntp.to_pandas(daily_revenue)\n```\n\n\n\nCheck the [Getting Started tutorial](https://temporian.readthedocs.io/en/stable/tutorials/getting_started/) to find out more!\n\n## Next steps\n\nNew users should refer to the [Getting Started](https://temporian.readthedocs.io/en/stable/getting_started/) guide, which provides a\nquick overview of the key concepts and operations of Temporian.\n\nAfter that, visit the [User Guide](https://temporian.readthedocs.io/en/stable/user_guide/) for a deep dive into\nthe major concepts, operators, conventions, and practices of Temporian. For a\nhands-on learning experience, work through the [Tutorials](https://temporian.readthedocs.io/en/stable/tutorials/) or refer to the [API\nreference](https://temporian.readthedocs.io/en/stable/reference/).\n\nIf 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! \ud83e\udd1d\ud83c\udffc\n\n## Documentation\n\nThe documentation \ud83d\udcda 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.\n\n## Contributing\n\nContributions to Temporian are welcome! Check out the [Contributing guide](https://temporian.readthedocs.io/en/stable/contributing/) to get started.\n\n## Credits\n\nTemporian is developed in collaboration between Google and [Tryolabs](https://tryolabs.com/).\n",
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