semvec/pypi-packages huggingface dataset:
jrotatelog 0.1.0
Rotate logs with incremental generation numbers
Requires Python: <4.0,>=3.11

# JRotateLog

Rotate logs with incremental generation numbers. This is similar to the RotatingFileHandler but instead of max bytes as the rotate condition its when a new python program runs. For every run of the python code rotate the logs with the backed up files indexed start at 0.

Ex.
Run python program 3 times creates 2 backups
```
app.log
app.log.0
app.log.1
```
`backup_count` can be used to limit how many logs are kept.
Ex.
Run python program 4 times with backupCount = 2
```
app.log   (from run 4)
app.log.0 (from run 3)
app.log.1 (from run 2)
app.log.2 (from run 1, deleted)
```

## Install
```
pip install jrotatelog
```

## Usage
```
from jrotatelog.handler import RotateFileHandler

logger = logging.getLogger("app")
fh = RotateFileHandler("app.log")
logger.addHandler(fh)
```
edahub 0.0.3
EDAHub helps structure exploratory data analysis (EDA) results.
Requires Python: >=3.9

# EDAHub
## What is this?

EDA (exploratory data analysis) results can be more structured.

EDAHub provides a lightweight dashboard for you to review your data summary on the side screen in JupyterLab, making it easier and quicker to revisit.
![Screenshot](assets/readme_example.png)


## Why this is useful?
As a data scientist, I've seen many notebooks that mix data/ML pipeline logic with observations. EDAHub addresses this by organizing basic observations in one place.

## How to start

### Install
You can try it on your JupyterLab with pip install:

```bash
pip install edahub
```

### Whole example
![Example notebook](examples/edahub_example.ipynb) would help you to understand how it works.

### Quick start

After instantiating "EDAHub" object, you can load your pandas.DataFrame with name:

```
import edahub
eda = edahub.EDAHub()

eda.add_table("<your table name>", df)
```

You will see the widget on the right side.


Also you can register charts you developed into the dashboard:

```
chart1 = ...
chart2 = ...
eda.add_chart("<name of section>", chart1)
eda.add_chart("<name of section>", chart2)
```
It will display your chart on the tab "Charts"

You can save widget as html file, you can open it on the browser independently on Jupyter.

```
eda.export_html("edahub_export.html")
```

NOTE: I observe instability in updating output of widgets. When output doesn't look right, please click "Update" button to update the widget.
promptscript 0.3.0
A simple scripting language for interacting with AI
Requires Python: >=3.6

# PromptScript

A simple scripting language for interacting with AI built on top of Python.

## Quick Install

With pip:
```bash
pip install promptscript
```

## Why PromptScript?

**PromptScript** is designed for efficient tooling with AI systems.

PromptScript can be written and executed on its own or embedded into Python projects and allows for no-boilerplate interactions with large language models (LLMs), image generation models, and transcription.

## Running PromptScript

Once you've installed PromptScript, you can run it in one of the following ways

### Option 1: CLI

Run:

```bash
promptscript
```

### Option 2: Files

Create a file ending in **.prompt**

Example _(test.prompt)_:
```
show "Hello, World!"
```

Then, you can run the file from the terminal:

```bash
promptscript test.prompt
```

Replacing _test.prompt_ with the actual file path

### Option 3: Embedded in Python

To run PromptScript from python, you can either run it from a file or individual commands

**Running From Files**

```python
from promptscript.executor import FileExecutor

FileExecutor().run('<file_name>.prompt')
```

**Running Individual Commands**

```python
from promptscript.executor import CommandExecutor

CommandExecutor().run('show "Hello World!"')
```

## Documentation

Visit [promptscript.wiki](https://promptscript.wiki) for full documentation and usage examples