Customizing Visual Styles in Plotly

Overview

Customizing Visual Styles in Plotly

Code for a workshop originally developed for an Unconference session during the Outlier Conference hosted by Data Visualization Society.

To jump right in:

Fork this repository, or download the Jupyter Notebook file Styling_Plotly_Themes_Templates.ipynb.

Ever have that feeling that a lot of data viz you see screams the tool it was made in? Using the Plotly Open Source Python Graphing Library, we will take a look under the hood of:

  • the style themes available,
  • understand the visual elements like figure and chart backgrounds, and
  • build our own default theme script inspired by 1980's computers.

This informal workshop is for a seasoned Pythonista wanting to add to your design toolbox or a newbie curious about custom interfaces beyond the usual BI tools (listen or follow along).

You can also check out all of Plotly's open source graphing libraries, including R, JavaScript, and more here.

Quick Start Prep

(most of this occurs before the workshop to follow along live...)

We're not going to spend too much time here, but if you're just starting out in Python, and want to get your hands dirty, here's a few building blocks useful to get the most from the workshop:

  1. Python ...All you really need is a Python code interpreter installed as a foundation.

    1. Start from the source, Python Software Foundation's helpful steps and downloads (yep, the be all end all source).
      1. Many computers come with a version pre-installed, a bit old, but if you don't want to touch or download anything, it may get you acquainted, at least. (to check in command line or terminal, run python --version)
    2. Or Python comes with an Anaconda installation (bigger topic than this workshop, but if you're in it for the long haul using Python consider e.g. the Individual Edition or a miniconda).

  2. A virtual environment (optional, but do this next if you're doing it.)

    1. Skip this step if the sound of it or # steps has you scared away already! Don't go, stay!
    2. It's recommended, but not necessary, to make and work in an isolated virtual environment for any Python project like this one, to help manage work requiring different versions of things.
      1. Options to manage this:
        1. I find virtualenv a sure bet,
          1. (e.g. On Mac Terminal (Zsh), from my project root folder, I ran virtualenv plotlystyle_env to make it; to activate it, I'll run source plotlystyle_env/bin/activate) _pip install virtualenv_if necessary first.
          2. I'll refer you to the docs for Windows.
        2. the simplified venv built into Python version 3.3+,
        3. Conda which I feel is cleanest with its centralized file structure, but fussy at times like an angry schoolchild, and
        4. those are the big ones.

  3. Jupyter Notebook (strongly recommended, we'll spend the workshop in the .ipynb Notebook file)

    1. Notebooks run directly in your web browser, so you need: Chrome, Safari, or Firefox (up to date Opera and Edge maybe works)

    2. If you installed an Anaconda distribution in step 1, congratulations, Jupyter Notebook is included! Read up on running the Notebook where we'll pick up!

    3. You can alternately install Jupyter Notebook with the pip package manager.

    4. If you're working in a virtual environment (step 2 above), also install the IPython kernel.

      1. Otherwise, this Jupyter Notebooks does have this automatically for your system Python interpreter.
      2. This basically supports more quick, interactive, code which makes Notebooks great for learning in chunks, and exploring without running a whole script.
  4. Kiss your brain!

Who's tired of hyperlinks and docs already?! You promised fun!

General Disclaimer

This work is open source, like Plotly Open Source Graphing Libraries, so try it, use it and spread the love by teaching someone else!
To keep up with what others are working on, join the Plotly Community Forum. Made with 💌 for the Python and data viz ecosystems under the limited liability company Data, Design & Daughters LLC doing business as Data Design Dimension by Kathryn Hurchla.

Owner
Data Design Dimension
Impact. Visualize. Grow. Full lifecycle data studio to optimize, build flows, and gain traction while you go.
Data Design Dimension
Application for viewing pokemon regional variants.

Pokemon Regional Variants Application Application for viewing pokemon regional variants. Run The Source Code Download Python https://www.python.org/do

Michael J Bailey 4 Oct 08, 2021
Homework 2: Matplotlib and Data Visualization

Homework 2: Matplotlib and Data Visualization Overview These data visualizations were created for my introductory computer science course using Python

Sophia Huang 12 Oct 20, 2022
Voilà, install macOS on ANY Computer! This is really and magic easiest way!

OSX-PROXMOX - Run macOS on ANY Computer - AMD & Intel Install Proxmox VE v7.02 - Next, Next & Finish (NNF). Open Proxmox Web Console - Datacenter N

Gabriel Luchina 654 Jan 09, 2023
An animation engine for explanatory math videos

Powered By: An animation engine for explanatory math videos Hi there, I'm Zheer 👋 I'm a Software Engineer and student!! 🌱 I’m currently learning eve

Zaheer ud Din Faiz 2 Nov 04, 2021
Leyna's Visualizing Data With Python

Leyna's Visualizing Data Below is information on the number of bilingual students in three school districts in Massachusetts. You will also find infor

11 Oct 28, 2021
Realtime Web Apps and Dashboards for Python and R

H2O Wave Realtime Web Apps and Dashboards for Python and R New! R Language API Build and control Wave dashboards using R! New! Easily integrate AI/ML

H2O.ai 3.4k Jan 06, 2023
Plotting library for IPython/Jupyter notebooks

bqplot 2-D plotting library for Project Jupyter Introduction bqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar

3.4k Dec 30, 2022
Domain Connectivity Analysis Tools to analyze aggregate connectivity patterns across a set of domains during security investigations

DomainCAT (Domain Connectivity Analysis Tool) Domain Connectivity Analysis Tool is used to analyze aggregate connectivity patterns across a set of dom

DomainTools 34 Dec 09, 2022
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion

Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion

1 Jan 22, 2022
🎨 Python Echarts Plotting Library

pyecharts Python ❤️ ECharts = pyecharts English README 📣 简介 Apache ECharts (incubating) 是一个由百度开源的数据可视化,凭借着良好的交互性,精巧的图表设计,得到了众多开发者的认可。而 Python 是一门富有表达

pyecharts 13.1k Jan 03, 2023
Friday Night Funkin - converts a chart from 4/4 time to 6/8 time, or from regular to swing tempo.

Chart to swing converter As seen in https://twitter.com/i_winxd/status/1462220493558366214 A program written in python that converts a chart from 4/4

5 Dec 23, 2022
Create animated and pretty Pandas Dataframe or Pandas Series

Rich DataFrame Create animated and pretty Pandas Dataframe or Pandas Series, as shown below: Installation pip install rich-dataframe Usage Minimal exa

Khuyen Tran 92 Dec 26, 2022
Rick and Morty Data Visualization with python

Rick and Morty Data Visualization For this project I looked at data for the TV show Rick and Morty Number of Episodes at a Certain Location Here is th

7 Aug 29, 2022
This component provides a wrapper to display SHAP plots in Streamlit.

streamlit-shap This component provides a wrapper to display SHAP plots in Streamlit.

Snehan Kekre 30 Dec 10, 2022
Visualize your pandas data with one-line code

PandasEcharts 简介 基于pandas和pyecharts的可视化工具 安装 pip 安装 $ pip install pandasecharts 源码安装 $ git clone https://github.com/gamersover/pandasecharts $ cd pand

陈华杰 2 Apr 13, 2022
A tool for creating SVG timelines from simple JSON input.

A tool for creating SVG timelines from simple JSON input.

Jason Reisman 432 Dec 30, 2022
Simple function to plot multiple barplots in the same figure.

Simple function to plot multiple barplots in the same figure. Supports padding and custom color.

Matthias Jakobs 2 Feb 21, 2022
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.

30-Days-of-ML-Kaggle 🔥 About the Hands On Program 💻 Machine learning beginner → Kaggle competitor in 30 days. Non-coders welcome The program starts

Roja Achary 145 Jan 01, 2023
Visualize and compare datasets, target values and associations, with one line of code.

In-depth EDA (target analysis, comparison, feature analysis, correlation) in two lines of code! Sweetviz is an open-source Python library that generat

Francois Bertrand 2.3k Jan 05, 2023
This is a Cross-Platform Plot Manager for Chia Plotting that is simple, easy-to-use, and reliable.

Swar's Chia Plot Manager A plot manager for Chia plotting: https://www.chia.net/ Development Version: v0.0.1 This is a cross-platform Chia Plot Manage

Swar Patel 1.3k Dec 13, 2022