semvec/pypi-packages huggingface dataset:
heave 0.4.0
CLI tool to batch update or insert data.
Requires Python: >=3.10

# Heave

Heave is a CLI tool for batch inserting or updating data into a database.

## Installation

It is recommended to install `heave` using [uv](https://docs.astral.sh/uv/guides/tools/#installing-tools):
```bash
uv tool install heave
```

## Usage

`heave [OPTIONS] COMMAND [ARGS]...`

Create a connection to a PostgreSQL database.

Defaults to the `postgres` database on `localhost` with the `postgres` user.
Pass alternate connection parameters as options.
You can save yourself some typing by setting the environment variables `PGDATABASE`, 
`PGHOST`, `PGPORT` and/or `PGUSER` to appropriate values.
It is also convenient to have a [.pgpass](https://www.postgresql.org/docs/current/libpq-pgpass.html) file 
to avoid regularly having to type in passwords.

#### Options

`-h`, `--host TEXT (default: localhost)`
- Host name of the database.

`-p`, `--port INTEGER (default: 5432)`
- Port number at which the database is listening.

`-U`, `--username TEXT`
- Username to connect as.

`-d`, `--dbname TEXT (default: postgres)`
- Name of the database to connect to.

#### Examples

```bash
heave --host myhost --port 5433 --username myuser --dbname mydb
```

### heave insert

`heave insert --table TEXT <file>`

Insert data from a file into a table.

#### Options

`-t`, `--table TEXT`
- Name of the table to insert into. Required.

`-oc`, `--on-conflict TEXT`
- How to handle insert conflicts. [nothing|update]

#### Examples

```bash
heave insert --table mytable data.csv
```

### heave read

`heave read --table TEXT <file>`

Read data from an SQL table and write it to a file.

#### Options

`-t`, `--table TEXT`
- Name of the table to read from. Required.

#### Examples

```bash
heave read --table mytable data.csv
```

## Supported Formats

Currently, Heave only supports extracting data from CSV files and loading it into a PostgreSQL database.

### Sources

Supported data sources include:

* CSV files: `.csv`

### Databases

Supported target databases include:

* PostgreSQL

## Examples

...

## Contributing

Contributions are welcome! Pick an existing issue or create a new one.
Then follow the steps in [CONTRIBUTING.md](/CONTRIBUTING.md) to make your change.`
chobo 0.99.11
A Python package for graphical displays
Requires Python: >=3.7

# chobo

The chobo module is a forked version of the [intrographics](http://myslu.stlawu.edu/~ltorrey/intrographics/) module developed by [Dr. Lisa Torrey](http://myslu.stlawu.edu/~ltorrey/).

It allows you to create graphical displays in your Python 3 programs.
The name chobo (초보) is a Korean word that means a beginner/novice, and the name is to inspire beginners at programming to give it a try.

It is written in [Python 3](https://www.python.org/), and uses the [pygame](https://www.pygame.org/) module as the back-end, and adds the following features:

* Image files other than just GIF
* Arc as a new shape
* An empty image shape
* Shape rotations
* Use of any system fonts
* Sound/music files and their playback

The full documentation is available in the [doc](https://github.com/choongsoo/chobo/tree/master/doc) folder.
synth-mind 0.1.4
SynthMind: A Comprehensive Machine Learning Library
Requires Python: >=3.8

# SynthMind: A Comprehensive Machine Learning Library

**SynthMind** is a flexible and extensible Python package designed to streamline the development and deployment of machine learning solutions. At its core, the package features a powerful manager classes that orchestrate various machine learning workflows, allowing users to focus on their specific applications without getting bogged down by implementation details. The package is built to be adaptable, enabling seamless integration of custom algorithms and models.

### Key Features:
- **Supervised Learning Management**: Effortlessly handle the training process for models with labeled datasets. While basic algorithms are provided, the focus is on managing and optimizing the workflow.
- **Reinforcement Learning Orchestration** *(to be done)*: A robust framework for managing RL environments and training processes, making it easy to experiment and deploy RL agents.
- **Semantic Processing Coordination** *(to be done)*: Tools for handling the end-to-end process of vectorizing meanings, processing them through neural architectures, and decoding them into useful formats.

Whether you're building traditional models, exploring reinforcement learning, or working with complex semantic vectors, **SynthMind** provides the infrastructure to manage your projects efficiently while allowing room for customization and expansion.

## Explore SynthMind Examples

To see **SynthMind** in action, explore our dedicated repository for examples and tutorials: [synth-mind-examples](https://github.com/lucasbasquerotto/synth-mind-examples).

This repository contains a variety of examples, including:

- **Supervised Learning**: Learn how to manage and train models using labeled datasets.
- **Reinforcement Learning** *(to be done)*: Set up RL environments, train agents, and analyze their performance.
- **Semantic Processing** *(to be done)*: Work with vectorized meanings and process semantic information.

Whether you're getting started or looking to expand your understanding of the **SynthMind** package, these examples will provide valuable insights and practical guidance.
sweet-notebook-components 1.0.5
A Streamlit-like component library for Notebooks.
Requires Python: >=3.6

# Sweet Components

A Streamlit-like component library for Notebooks.

Show a text:

```python
from sweet_notebook_components import st


st.text("Hello, World!")

```

## Development

Install the dependencies:

```sh
pip install -r requirements.txt

```

Activate the environment:

On Windows:

```bash
venv\Scripts\activate

```

On macOS/Linux:

```bash
source venv/bin/activate

```

Run the test suite:

```sh
pytest


```