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align=\"center\"\u003e\n  \u003ca href=\"https://github.com/andrewtavis/pltviz\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/andrewtavis/pltviz/main/.github/resources/logo/pltviz_logo_transparent.png\" width=488 height=157\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003col\u003e\u003c/ol\u003e\n\n[![rtd](https://img.shields.io/readthedocs/pltviz.svg?logo=read-the-docs)](http://pltviz.readthedocs.io/en/latest/)\n[![ci](https://img.shields.io/github/actions/workflow/status/andrewtavis/pltviz/.github/workflows/ci.yml?branch=main?logo=github)](https://github.com/andrewtavis/pltviz/actions?query=workflow%3ACI)\n[![codecov](https://codecov.io/gh/andrewtavis/pltviz/branch/main/graphs/badge.svg)](https://codecov.io/gh/andrewtavis/pltviz)\n[![pyversions](https://img.shields.io/pypi/pyversions/pltviz.svg?logo=python\u0026logoColor=FFD43B\u0026color=306998)](https://pypi.org/project/pltviz/)\n[![pypi](https://img.shields.io/pypi/v/pltviz.svg?color=4B8BBE)](https://pypi.org/project/pltviz/)\n[![pypistatus](https://img.shields.io/pypi/status/pltviz.svg)](https://pypi.org/project/pltviz/)\n[![license](https://img.shields.io/github/license/andrewtavis/pltviz.svg)](https://github.com/andrewtavis/pltviz/blob/main/LICENSE.txt)\n[![coc](https://img.shields.io/badge/coc-Contributor%20Covenant-ff69b4.svg)](https://github.com/andrewtavis/pltviz/blob/main/.github/CODE_OF_CONDUCT.md)\n[![codestyle](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![colab](https://img.shields.io/badge/%20-Open%20in%20Colab-097ABB.svg?logo=google-colab\u0026color=097ABB\u0026labelColor=525252)](https://colab.research.google.com/github/andrewtavis/pltviz)\n\n## Standardized plots and visualizations in Python\n\n**pltviz** is a Python package for standardized visualization. Routine and novel plotting approaches are formatted to allow for easy variation while providing quick and exact results. Coloration functions are also included for precise colors across plots and to assure that all functions can be ran with color hexes.\n\n\u003ca id=\"contents\"\u003e\u003c/a\u003e\n\n# **Contents**\n\n- [Installation](#installation)\n- [plot](#plot)\n- [To-Do](#to-do)\n\n\u003ca id=\"installation\"\u003e\u003c/a\u003e\n\n# Installation [`⇧`](#contents)\n\npltviz can be downloaded from PyPI via pip or sourced directly from this repository:\n\n```bash\npip install pltviz\n```\n\n```bash\ngit clone https://github.com/andrewtavis/pltviz.git\ncd pltviz\npython setup.py install\n```\n\n```python\nimport pltviz\n```\n\n\u003ca id=\"plot\"\u003e\u003c/a\u003e\n\n# plot [`⇧`](#contents)\n\nPlotting methods within [pltviz](https://github.com/andrewtavis/pltviz/tree/main/src/pltviz) are tailored to provide quick results for staples of data visualization.\n\nSee [examples/plot](https://github.com/andrewtavis/pltviz/blob/main/examples/plot.ipynb) for all plotting styles that seamlessly combine graphing functions of seaborn, matplotlib, and pandas.\n\n```python\nimport matplotlib.pyplot as plt\nimport pltviz\n```\n\nExamples of routine plotting techniques made easy are:\n\n```python\n# The following will be used for the remaining examples\n\n# German political parties\nparties = ['CDU/CSU', 'FDP', 'Greens', 'Die Linke', 'SPD', 'AfD']\nparty_colors = ['#000000', '#ffed00', '#64a12d', '#be3075', '#eb001f', '#009ee0']\n\n# Hypothetical seat allocations to the Bundestag (German parliament)\nseat_allocations = [26, 9, 37, 12, 23, 5]\n```\n\nThe following shows [pltviz.bar](https://github.com/andrewtavis/pltviz/blob/main/src/pltviz/bar.py) that allows all common options to be selected as binaries:\n\n```python\n# Bar plot options such as stacked and label bars are booleans\nax = pltviz.bar(\n    counts=seat_allocations,\n    labels=parties,\n    colors=party_colors,\n    horizontal=False,\n    stacked=False,\n    label_bars=True,\n)\n\n# Initialize empty handles and labels\nhandles, labels = pltviz.legend.gen_elements()\n\n# Add a majority line\nax.axhline(int(sum(seat_allocations) / 2) + 1, ls=\"--\", color=\"black\")\nhandles.insert(0, Line2D([0], [0], linestyle=\"--\", color=\"black\"))\nlabels.insert(0, \"Majority: {} seats\".format(int(sum(seat_allocations) / 2) + 1))\n\nax.legend(\n    handles=handles,\n    labels=labels,\n    title=\"Bundestag: {} seats\".format(sum(seat_allocations)),\n    loc=\"upper left\",\n    bbox_to_anchor=(0, 0.9),\n    title_fontsize=20,\n    fontsize=15,\n    frameon=True,\n    facecolor=\"#FFFFFF\",\n    framealpha=1,\n)\n\nax.set_ylabel(\"Seats\", fontsize=15)\nax.set_xlabel(\"Party\", fontsize=15)\n```\n\n\u003cp align=\"middle\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/andrewtavis/pltviz/main/.github/resources/images/bar.png\" width=\"600\" /\u003e\n\u003c/p\u003e\n\nAlso included is a [pltviz.semipie](https://github.com/andrewtavis/pltviz/blob/main/src/pltviz/semipie.py) via matplotlib artists for cases where a simple and condensed plot is needed:\n\n```python\nax = pltviz.semipie(counts=seat_allocations, colors=party_colors, donut_ratio=0.5)\n\nhandles, labels = pltviz.legend.gen_elements(\n    counts=seat_allocations,\n    labels=parties,\n    colors=party_colors,\n)\n\nax.legend(\n    handles=handles,\n    labels=labels,\n    title=\"Bundestag: {} seats\".format(sum(seat_allocations)),\n    title_fontsize=20,\n    fontsize=14,\n    ncol=2,\n    loc=\"center\",\n    bbox_to_anchor=(0.5, 0.17),\n    frameon=False,\n    facecolor=\"#FFFFFF\",\n    framealpha=1,\n)\n\nplt.show()\n```\n\n\u003cp align=\"middle\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/andrewtavis/pltviz/main/.github/resources/images/semipie.png\" width=\"600\" /\u003e\n\u003c/p\u003e\n\npltviz also includes specialized plots such as [pltviz.gini](https://github.com/andrewtavis/pltviz/blob/main/src/pltviz/gini.py) to visualize [gini coefficients](https://en.wikipedia.org/wiki/Gini_coefficient) of inequality:\n\n```python\nglobal_gdp_deciles = [0.49, 0.59, 0.69, 0.79, 1.89, 2.55, 5.0, 10.0, 18.0, 60.0]\n\nax, gini_coeff = pltviz.gini(shares=global_gdp_deciles)\n\nhandles, labels = pltviz.legend.gen_elements(labels=[\"Lorenz Curve\", \"Perfect Equality\"])\n\nax.legend(\n    handles=handles,\n    labels=labels,\n    loc='upper left',\n    bbox_to_anchor=(0, 0.9),\n    fontsize=20,\n    frameon=True,\n    facecolor='#FFFFFF',\n    framealpha=1)\n\nax.set_title(f'Gini: {gini_coeff}', fontsize=20)\nax.set_ylabel('Cuumlative Share of Global GDP', fontsize=15)\nax.set_xlabel('Income Deciles', fontsize=15)\n\nplt.show()\n```\n\n\u003cp align=\"middle\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/andrewtavis/pltviz/main/.github/resources/images/gini.png\" width=\"600\" /\u003e\n\u003c/p\u003e\n\n\u003ca id=\"to-do\"\u003e\u003c/a\u003e\n\n# To-Do [`⇧`](#contents)\n\nPlease see the [contribution guidelines](https://github.com/andrewtavis/pltviz/blob/main/.github/CONTRIBUTING.md) if you are interested in contributing to this project. Work that is in progress or could be implemented includes:\n\n- Adding standardized examples of further plots and visualizations [(see issue)](https://github.com/andrewtavis/pltviz/issues/12)\n\n- Finishing the coloration on the outer ring of [pltviz.pie](https://github.com/andrewtavis/pltviz/blob/main/src/pltviz/pie.py)\n\n- Improving [tests](https://github.com/andrewtavis/pltviz/tree/main/tests) for greater [code coverage](https://codecov.io/gh/andrewtavis/pltviz)\n\n- Improving [code quality](https://app.codacy.com/gh/andrewtavis/pltviz/dashboard) by refactoring large functions and checking conventions\n\n- Allowing all plotting variations to be seamlessly plotted from either lists or dataframe columns where applicable\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewtavis%2Fpltviz","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrewtavis%2Fpltviz","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewtavis%2Fpltviz/lists"}