semvec/pypi-packages huggingface dataset:
jupyter-toc 1.1.2
A Python package for generating a table of contents for Jupyter notebooks

# Jupyter Notebook ToC

This is a simple extension to generate a table of contents for a Jupyter notebook.

## Basic Usage

```python
import jupyter_toc
jupyter_toc.build()
```

It will return something like this:

```markdown
Table of Contents
- [Introduction](#introduction)
 - [Subhead](##subhead)
- [Usage](#usage)
- [Development](#development)
```

## Advanced Usage

You can pass arguments to customize the following:

- `title` - The title of the table of contents.
- `depth` - The maximum depth of headings to include in the table of contents.
- `make_hyperlinks` - Whether to make the table of contents entries hyperlinks.
- `print_output` - Whether to print the ToC or just return it as a string.

## Contributing

Feel free to open an issue or submit a pull request.

To publish a new version to PyPI:
```bash
pip install twine wheel # if you don't have it already
python setup.py sdist bdist_wheel # build the package
twine upload dist/* # upload to PyPI
```

## Development

### Running Tests

The test suite can be run using Python's built-in `unittest` framework.

Navigate to the `tests` directory and run:

```
python -m unittest test_jupyter_toc.py
```

Or, if you're in the root directory:

```
python -m unittest tests/test_jupyter_toc.py
```

## Authors

 - [Odai Athamneh](https://github.com/heyodai)
streamGauge 1.0.0
Plotly Gauge Indicator Viz for Streamlit
Requires Python: >=3.7

![stream gauge intro animation](https://github.com/REPNOT/stream_gauge/blob/main/media/Stream_Gauge_GIF.gif)

# stream_gauge

Reusable Streamlit component for quickly deploying Plotly indicator gauge charts.


## Installation instructions

```sh
pip install stream_gauge
```

## Usage instructions

```python
import streamlit as st
from stream_gauge import stream_gauge


number = st.number_input('Insert a number')
stream_gauge(number, gSize="FULL", sFix="%")


col1, col2 = st.columns(2)

with col1:
    stream_gauge(.55, gSize="MED")

with col2:
    stream_gauge(.91, gSize="MED")


col4, col5, col6 = st.columns(3)

with col4:
    stream_gauge(
        .49, 
        sFix="%", 
        gSize="MED", 
        gcLow='#6BBEF2', 
        gcMid='#0597F2', 
        gcHigh='#056CF2'
    )

with col5:
    stream_gauge(
        .85, 
        sFix="%", 
        gSize="LRG", 
        gcLow='#6BBEF2', 
        gcMid='#0597F2', 
        gcHigh='#056CF2'
    )

with col6:
    stream_gauge(
        .63, 
        sFix="%", 
        gSize="MED", 
        gcLow='#6BBEF2', 
        gcMid='#0597F2', 
        gcHigh='#056CF2'
    )
stream-Gauge 1.0.0
Plotly Gauge Indicator Viz for Streamlit
Requires Python: >=3.7

![stream gauge intro animation](https://github.com/REPNOT/stream_gauge/blob/main/media/Stream_Gauge_GIF.gif)

# stream_gauge

Reusable Streamlit component for quickly deploying Plotly indicator gauge charts.


## Installation instructions

```sh
pip install stream_gauge
```

## Usage instructions

```python
import streamlit as st
from stream_gauge import stream_gauge


number = st.number_input('Insert a number')
stream_gauge(number, gSize="FULL", sFix="%")


col1, col2 = st.columns(2)

with col1:
    stream_gauge(.55, gSize="MED")

with col2:
    stream_gauge(.91, gSize="MED")


col4, col5, col6 = st.columns(3)

with col4:
    stream_gauge(
        .49, 
        sFix="%", 
        gSize="MED", 
        gcLow='#6BBEF2', 
        gcMid='#0597F2', 
        gcHigh='#056CF2'
    )

with col5:
    stream_gauge(
        .85, 
        sFix="%", 
        gSize="LRG", 
        gcLow='#6BBEF2', 
        gcMid='#0597F2', 
        gcHigh='#056CF2'
    )

with col6:
    stream_gauge(
        .63, 
        sFix="%", 
        gSize="MED", 
        gcLow='#6BBEF2', 
        gcMid='#0597F2', 
        gcHigh='#056CF2'
    )