semvec/pypi-packages huggingface dataset:
microdatabase 0.1.0
Lightweight SQLite toolkit with schema enforcement, lambda queries, and row-level operations
License: MIT
Requires Python: >=3.9

# microdatabase

**microdatabase** is a lightweight SQLite toolkit with schema enforcement, row-level control, and Pythonic querying using lambda functions.

- Minimal and dependency-free
- Typed schema via `DatabaseParameter`
- Full row object access, with `.edit()` and `.delete()`
- Python-native query filtering (no SQL required)
- Stores files under a local `_databases/` directory

---

## Installation

```bash
pip install dbkit


````
from dbkit import Database, DatabaseParameter, DatabaseParameterType

db = Database("users", [
    DatabaseParameter("username", DatabaseParameterType.STRING),
    DatabaseParameter("age", DatabaseParameterType.INT),
    DatabaseParameter("active", DatabaseParameterType.BOOLEAN),
])

db.setup()

# Insert data
db.add_row(username="Karl", age=53, active=True)
db.add_row(username="Lilly", age=17, active=False)

# Query
adults = db.query(lambda r: r.age >= 18 and r.active)

for user in adults:
    print(user.username, user.age)

    # Edit a row
    user.edit(age=user.age + 1)

    # Delete a row
    if user.username == "Karl":
        user.delete()
tsuniverse 0.0.12
A library for efficiently processing a time series universe to determine causal features.
License: MIT
Keywords: features, timeseries

# tsuniverse

<a href="https://pypi.org/project/tsuniverse/">
    <img alt="PyPi" src="https://img.shields.io/pypi/v/tsuniverse">
</a>

A library for efficiently processing a time series universe to determine causal features.

## Dependencies :globe_with_meridians:

Python 3.11.6:

- [pandas](https://pandas.pydata.org/)
- [pyarrow](https://arrow.apache.org/docs/python/index.html)
- [scikit-learn](https://scikit-learn.org/)
- [timeseries-features](https://github.com/8W9aG/timeseries-features)
- [scipy](https://scipy.org/)
- [dcor](https://dcor.readthedocs.io/en/latest/index.html)
- [numpy](https://numpy.org/)
- [pyHSICLasso](https://github.com/riken-aip/pyHSICLasso)
- [stumpy](https://stumpy.readthedocs.io/en/latest/)

## Raison D'être :thought_balloon:

`tsuniverse` aims take a universe of time series and figure out features from that universe that can be used to predict a single time series.

## Architecture :triangular_ruler:

`tsuniverse` is a functional library, meaning that each phase of the feature extraction goes through functions without side-effects. It attempts to do as much multiprocessing as it can to make this process quicker. Each feature extraction is done in different phases, those phases are:

1. Pearson Correlations.
2. Mutual Information.
3. Spearmans Rho.
4. Kendalls Tau.
5. Distance Correlations.
6. HSIC.
7. Stumpy.

## Installation :inbox_tray:

This is a python package hosted on pypi, so to install simply run the following command:

`pip install tsuniverse`

or install using this local repository:

`python setup.py install --old-and-unmanageable`

## Usage example :eyes:

The use of `tsuniverse` is entirely through code due to it being a library. It attempts to hide most of its complexity from the user, so it only has a few functions of relevance in its outward API.

### Generating Features

To generate features:

```python
import datetime

import pandas as pd

from tsuniverse.process import process

df = ... # Your timeseries dataframe
features = process(df)
```

This will produce a list of features that you can produce with [timeseries-features](https://github.com/8W9aG/timeseries-features).

## License :memo:

The project is available under the [MIT License](LICENSE).