Metadata-Version: 2.1
Name: quicklooks
Version: 0.0.11
Summary: quicklook is a Python package for visualizing data quickly using matplotlib.
Home-page: https://www.linkedin.com/in/alexanderbreslav/
Author: Alex Breslav
Author-email: alexdsbreslav@gmail.com
License: MIT
Keywords: matplotlib,data-visualization,python
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: datetime
Requires-Dist: seaborn

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

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

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

## Who is quicklooks best for?
quicklook 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 quicklook 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...
Directions to come; I am currently overhauling the package and will upload to pip for easy installation.

## 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/), and [seaborn](https://seaborn.pydata.org/).

