semvec/pypi-packages huggingface dataset:
eventscore 0.1.0
Power up your application with event-driven design!
License: MIT
Requires Python: >=3.11

`eventscore`: Power up your application with event-driven design!
=======================================

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What is `eventscore`?
-------------

See [the documentation](https://eventscore.readthedocs.io/en/latest/) for
more examples and information.

Quick start
-----------

`eventscore` can be installed using pip:

```bash
python3 -m pip install -U eventscore
```

If you want to run the latest version of the code, you can install from the
github directly:

```bash
python3 -m pip install -U git+https://github.com/MatveyIvanov/eventscore.git
```

Contributing
------------

Help in testing, development, documentation and other tasks is
highly appreciated and useful to the project.

To get started with developing `eventscore`, see [CONTRIBUTING.md](CONTRIBUTING.md).

First (0.1.0) release roadmap
-----------------------------
* 90%+ unit-test coverage
* e2e tests with basic flows
* integration tests for consumer discovering, threading, multiprocessing and redis stream
* CI for PRs, main and release branches
* CD for release branches

Stable (1.0.0) release roadmap
------------------------------

* 100% unit-test coverage
* integration tests for threading and multiprocessing
* e2e tests
* 100% docs coverage
* Kafka support
* outbox pattern support
* at least once semantic support
* at most once semantic support
* stress-tests, with/without eventscore performance comparison
* multiple event types per consumer group
*
quicklooks 1.0.8
quicklook is a Python package for visualizing data quickly using matplotlib.
License: MIT
Keywords: matplotlib, data-visualization, python

![example_plot](https://github.com/alexdsbreslav/quicklook/assets/21344372/1eb05d0b-5712-4bda-8500-78a5aa76f110)

# quicklooks
quicklooks is a Python package for visualizing data quickly.  
Check out the documentation by clicking [here](https://github.com/alexdsbreslav/quicklooks/tree/master/how_to_use_quicklooks) or by navigating to the folder `how_to_use_quicklooks`.

## Why quicklooks?
Creating attractive, ready-to-share data visualizations takes forever. quickooks is a cut-and-paste Python package that does the design work for you and makes it easy to create beautifully simple data visualizations.

## Who is quicklooks best for?
quicklooks is for any data scientist, product manager, or researcher that knows a little bit of Python and is analyzing their data in a Jupyter Notebook. My goal in writing quicklooks was to make data viz more efficient, but in the process, I've also created a package that is easy to use for beginners.

## To install...
In JupyterLab:
1. Open the Launcher by clicking "+" in the top left of the window.
2. Select "Terminal"
3. Run...
   ```
   pip install quicklooks
   ```

Once you've installed quicklooks, you can import it into each of your notebooks by using the follow code in the first cell:
```python
import quicklooks as ql
```

## Documentation
The [documentation is here](https://github.com/alexdsbreslav/quicklook/tree/master/how_to_use_quicklook).

## Acknowledgments
quicklook is built using [Matplotlib](https://matplotlib.org/), [NumPy](https://numpy.org/), [Pandas](https://pandas.pydata.org/), [Coolors](https://coolors.co/), [opencolor](https://yeun.github.io/open-color/), and [seaborn](https://seaborn.pydata.org/).