Standardized plots and visualizations in Python

Last update: Jul 09, 2022

Overview

Standardized plots and visualizations in Python

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.

Installation
plot
To-Do

Installation `⇧`

pltviz can be downloaded from PyPI via pip or sourced directly from this repository:

pip install pltviz

git clone https://github.com/andrewtavis/pltviz.git
cd pltviz
python setup.py install

import pltviz

plot `⇧`

Plotting methods within pltviz are tailored to provide quick results for staples of data visualization.

See examples/plot for all plotting styles that seamlessly combine graphing functions of seaborn, matplotlib, and pandas.

import matplotlib.pyplot as plt
import pltviz

Examples of routine plotting techniques made easy are:

# The following will be used for the remaining examples

# German political parties
parties = ['CDU/CSU', 'FDP', 'Greens', 'Die Linke', 'SPD', 'AfD']
party_colors = ['#000000', '#ffed00', '#64a12d', '#be3075', '#eb001f', '#009ee0']

# Hypothetical seat allocations to the Bundestag (German parliament)
seat_allocations = [26, 9, 37, 12, 23, 5]

The following shows pltviz.bar that allows all common options to be selected as binaries:

# Bar plot options such as stacked and label bars are booleans
ax = pltviz.bar(
    counts=seat_allocations,
    labels=parties,
    colors=party_colors,
    horizontal=False,
    stacked=False,
    label_bars=True,
)

# Initialize empty handles and labels
handles, labels = pltviz.legend.gen_elements()

# Add a majority line
ax.axhline(int(sum(seat_allocations) / 2) + 1, ls="--", color="black")
handles.insert(0, Line2D([0], [0], linestyle="--", color="black"))
labels.insert(0, "Majority: {} seats".format(int(sum(seat_allocations) / 2) + 1))

ax.legend(
    handles=handles,
    labels=labels,
    title="Bundestag: {} seats".format(sum(seat_allocations)),
    loc="upper left",
    bbox_to_anchor=(0, 0.9),
    title_fontsize=20,
    fontsize=15,
    frameon=True,
    facecolor="#FFFFFF",
    framealpha=1,
)

ax.set_ylabel("Seats", fontsize=15)
ax.set_xlabel("Party", fontsize=15)

Also included is a pltviz.semipie via matplotlib artists for cases where a simple and condensed plot is needed:

ax = pltviz.semipie(counts=seat_allocations, colors=party_colors, donut_ratio=0.5)

handles, labels = pltviz.legend.gen_elements(
    counts=seat_allocations,
    labels=parties,
    colors=party_colors,
)

ax.legend(
    handles=handles,
    labels=labels,
    title="Bundestag: {} seats".format(sum(seat_allocations)),
    title_fontsize=20,
    fontsize=14,
    ncol=2,
    loc="center",
    bbox_to_anchor=(0.5, 0.17),
    frameon=False,
    facecolor="#FFFFFF",
    framealpha=1,
)

plt.show()

pltviz also includes specialized plots such as pltviz.gini to visualize gini coefficients of inequality:

global_gdp_deciles = [0.49, 0.59, 0.69, 0.79, 1.89, 2.55, 5.0, 10.0, 18.0, 60.0]

ax, gini_coeff = pltviz.gini(shares=global_gdp_deciles)

handles, labels = pltviz.legend.gen_elements(labels=["Lorenz Curve", "Perfect Equality"])

ax.legend(
    handles=handles,
    labels=labels,
    loc='upper left',
    bbox_to_anchor=(0, 0.9),
    fontsize=20,
    frameon=True,
    facecolor='#FFFFFF',
    framealpha=1)

ax.set_title(f'Gini: {gini_coeff}', fontsize=20)
ax.set_ylabel('Cuumlative Share of Global GDP', fontsize=15)
ax.set_xlabel('Income Deciles', fontsize=15)

plt.show()

To-Do `⇧`

Please see the contribution guidelines if you are interested in contributing to this project. Work that is in progress or could be implemented includes:

Adding standardized examples of further plots and visualizations (see issue)
Finishing the coloration on the outer ring of pltviz.pie
Improving tests for greater code coverage
Improving code quality by refactoring large functions and checking conventions
Allowing all plotting variations to be seamlessly plotted from either lists or dataframe columns where applicable

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Comments

Bump urllib3 from 1.26.3 to 1.26.4
Bumps urllib3 from 1.26.3 to 1.26.4.

Release notes

Sourced from urllib3's releases.

1.26.4

:warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap

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If you or your organization rely on urllib3 consider supporting us via GitHub Sponsors

Changelog

Sourced from urllib3's changelog.

1.26.4 (2021-03-15)

Changed behavior of the default SSLContext when connecting to HTTPS proxy during HTTPS requests. The default SSLContext now sets check_hostname=True.

Commits

a891304 Release 1.26.4

8d65ea1 Merge pull request from GHSA-5phf-pp7p-vc2r

5e34326 Add proper stacklevel to method_allowlist warning

See full diff in compare view

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dependencies
opened by dependabot[bot] 2
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| File | Before | After | Percent reduction | |:--|:--|:--|:--| | /resources/pltviz_logo.png | 115.97kb | 51.43kb | 55.65% | | /resources/pltviz_logo_transparent.png | 119.64kb | 60.41kb | 49.50% | | /resources/gh_images/semipie.png | 79.69kb | 58.81kb | 26.20% | | /resources/gh_images/bar.png | 53.07kb | 41.96kb | 20.93% | | /resources/gh_images/gini.png | 83.64kb | 70.88kb | 15.25% | | | | | | | Total : | 452.00kb | 283.50kb | 37.28% |

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opened by imgbot[bot] 1
Create concise requirement and env files
This issue is for creating concise versions of requirements.txt and environment.yml for pltviz. It would be great if these files were created by hand with specific version numbers or generated in a way so that sub-dependencies don't always need to be updated.

As of now both files are being created with the following commands in the package's conda virtual environment:

pip list --format=freeze > requirements.txt conda env export --no-builds | grep -v "^prefix: " > environment.yml

pltviz and other obviously unneeded packages are then removed from these files before being uploaded.

Any insights or help would be much appreciated!
help wanted good first issue question
opened by andrewtavis 0
New plots and visualizations
Please use this issue to suggest further plots and visualizations that could be added to pltviz. Potential inclusions should meet some of the following criteria:

Not have a valid implementation in another package

Simplify the plot or visualization's options

Enhance the ability of the plot or visualization to present their inputs

Suggestions would then be converted over to good first issues, with direct pull requests also being accepted once a method is checked :)

Thanks for your interest in contributing!
good first issue question
opened by andrewtavis 0

Releases(v0.1.0)

v0.1.0(Feb 11, 2021)
First stable release of pltviz

Additions include:

Changing the package's name to pltviz

Full documentation of the package

Virtual environment files

Bug fixes

Extensive testing of all modules with GH Actions and Codecov

Code of conduct and contribution guidelines

Source code(tar.gz)
Source code(zip)
v0.0.1(Dec 10, 2020)
The minimum viable product of stdviz:

Users are able to plot in various advanced, routine, and novel styles

Colors are standardized across plots

The most common options for plots are made into booleans

Legend generation provides full control to the user

Examples have been provided to show usage cases

Source code(tar.gz)
Source code(zip)

Owner

Andrew Tavis McAllister

Data scientist, developer and designer. Humboldt University of Berlin (MS); University of Oregon (BA).

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Standardized plots and visualizations in Python

Related tags

Overview

Standardized plots and visualizations in Python

Contents

Installation ⇧

plot ⇧

To-Do ⇧

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Comments

Bump urllib3 from 1.26.3 to 1.26.4

1.26.4

1.26.4 (2021-03-15)

[ImgBot] Optimize images

Beep boop. Your images are optimized!

Create concise requirement and env files

New plots and visualizations

Releases(v0.1.0)

v0.1.0(Feb 11, 2021)

v0.0.1(Dec 10, 2020)

Owner

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Installation `⇧`

plot `⇧`

To-Do `⇧`