Standardized plots and visualizations in Python

Related tags

Data Visualizationvisualizationpythonplottingginiplotting-in-pythonplotting-variations
Overview

rtd ci codecov pyversions pypi pypistatus license coc codestyle colab

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.

Contents

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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    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.
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    dependencies 
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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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  • Create concise requirement and env files

    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

    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).
Andrew Tavis McAllister
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