Latent Calendar
Analyze and model data on a weekly calendar
Installation
Install from PyPI:
pip install latent-calendar
Or install directly from GitHub for the latest functionality.
Features
- DataFrame agnostic transformations and aggregation powered by
narwhals - Integrated automatically into
pandasandpolarswithcalattribute on DataFrame and Series - Compatible with
scikit-learnpipelines and transformers - Transform and visualize data on a weekly calendar
- Model weekly calendar data with a mixture of calendars
- Create lower dimensional representations of calendar data
Documentation
Find more examples and documentation here.
Metadata
Release files for latent-calendar 1.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| latent_calendar-1.8.0.tar.gz | 31.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| latent_calendar-1.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 71.2 kB
Release files / latent_calendar-1.8.0.tar.gz
| Download URL | latent_calendar-1.8.0.tar.gz |
|---|---|
| Size | 31.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
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Release files / latent_calendar-1.8.0-py3-none-any.whl
| Download URL | latent_calendar-1.8.0-py3-none-any.whl |
|---|---|
| Size | 39.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
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