Data is Beautiful
A practical book on data visualisation that shows you how to create static and interactive visualisations that are engaging and beautiful.
Get the book
Chord Pro Features For Chord Diagrams
- Preamble
- Introduction
- Defaults
- Outputs Methods
- HTML to Jupyer Lab Cell (Interactive)
- HTML to file (Interactive)
- PNG to Jupyer Lab Cell (Image)
- PNG to file (image)
- PDF to file
- Disable SSL Verification
- Chord Colours
- Opacity
- Reverse Gradients
- Arc Numbers
- Diagram Title
- Padding
- Width
- Rotation
- Radius Scales
- Label Color
- Curved Labels
- Label Wrapping
- Margins
- Font-size
- Popup Width
- Popup Text
- Symmetric and Asymmetric Diagrams
- Download Button
- Popup Details, Thumbnails, and Custom HTML
- Popup Thumbnails Spacing and Size
- Divided (Bipartite) Chord Diagram
- Divided (Bipartite) Chord Diagram Labels
- Colored Diagonals
- Equal-sized Sections
- Mix and Match
Preamble¶
from chord import ChordIntroduction¶
Note
This document is best viewed online where the interactivity of the demonstrations can be experienced.
In a chord diagram (or radial network), entities are arranged radially as segments with their relationships visualised by arcs that connect them. The size of the segments illustrates the numerical proportions, whilst the size of the arc illustrates the significance of the relationships1. Chord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching.
Get Chord Pro¶
Click here to get lifetime access to the full-featured chord visualization API, producing beautiful interactive visualizations, e.g. those featured on the front page of Reddit.
License¶
To switch to the PRO version of the chord package, you need to assign a valid username (the email you entered at purchase) and license key. This can be purchased here.
Chord.user = "your username"
Chord.key = "your license key"We'll use the following data for the co-occurrence matrix and names parameters until we cover divided diagrams.
matrix = [
[0, 5, 6, 4, 7, 4],
[5, 0, 5, 4, 6, 5],
[6, 5, 0, 4, 5, 5],
[4, 4, 4, 0, 5, 5],
[7, 6, 5, 5, 0, 4],
[4, 5, 5, 5, 4, 0],
]
names = ["Action", "Adventure", "Comedy", "Drama", "Fantasy", "Thriller"]Defaults¶
Without passing in any arguments for the customisation parameters, the output will use the default value.
Chord(matrix, names).show()Chord(matrix, names, title="Jupyter Lab Cell").show()HTML to file (Interactive)¶
Saves an interactive HTML file locally.
Chord(matrix, names).to_html('out.html')PNG to Jupyer Lab Cell (Image)¶
Outputs a PNG to a Jupyter Lab Cell.
Chord(matrix, names).show_png()
PNG to file (image)¶
Saves a PNG file locally.
Chord(matrix, names).to_png('out.png')PDF to file¶
Saves a PDF file locally.
Chord(matrix, names).to_pdf('out.pdf')Disable SSL Verification¶
verify_ssl=True
Some users behind school/corporate networks may experience issues contacting the Chord API end-points. One workaround is to use verify_ssl=False. You will receive an InsecureRequestWarning with a link to further information.
Chord(matrix, names, verify_ssl=False, title="SSL Verification Disabled").show()Chord Colours¶
colors="d3.schemeSet1"
The default setting for the chord colours is d3.schemeSet1.
This can be changed to any of the sequential, diverging, or categorical colour schemes in d3-scale-chromatic, such as d3.schemeAccent, d3.schemeBlues[n], or d3.schemePaired :
Chord(matrix, names, colors="d3.schemeAccent").show()The colors parameter also accepts a Python list of HEX colour codes.
grayscale = ["#222222", "#333333", "#4c4c4c", "#666666", "#848484", "#9a9a9a"]
Chord(matrix, names, colors=grayscale).show()Opacity¶
opacity=0.8
This sets the opacity for the arcs when they are not selected (mouseover/touch).
Chord(matrix, names, opacity=0.2).show()Chord(matrix, names, reverse_gradients=True).show()Chord(matrix, names, arc_numbers=True).show()Chord(matrix, names, title="Movie Genre Co-occurrence").show()Chord(matrix, names, padding=0.5).show()Chord(matrix, names, width=400).show()Chord(matrix, names, rotate=-30).show()Radius Scales¶
inner_radius_scale=0.45,
outer_radius_scale=1.1,
This sets the inner and outer radius scale of the chord diagram.
Chord(matrix, names, inner_radius_scale=0.3, outer_radius_scale=1.5).show()Chord(matrix, names, label_color="#B362FF").show()Curved Labels¶
curved_labels=False
This turns curved labels on or off. It's not suitable for diagrams with many segments, but sometimes it can improve the look.
Chord(matrix, names, curved_labels=True).show()Label Wrapping¶
wrap_labels=True
This turns label wrapping on or off. It's on by default.
Let's temporarily change our names data to demonstrate this.
names = ["Exciting Action", "Fun Adventure", "Hilarious Comedy", "Drama", "Fantasy", "Chilling Thriller"]Chord(matrix, names).show()Now to demonstrate label wrapping set to disabled.
Chord(matrix, names, wrap_labels=False).show()We'll restore our names data before we continue.
names = ["Action", "Adventure", "Comedy", "Drama", "Fantasy", "Thriller"]Chord(matrix, names, margin=200).show()Font-size¶
font_size="16px"
font_size_large="20px"
This sets the font-size for two views, font-size-large (@media min-width: 600px), otherwise font-size. Setting a large font-size may require adjustment of the margin parameter to avoid text-clipping.
Chord(matrix, names, font_size="20px", font_size_large="30px").show()Chord(matrix, names, popup_width=150).show()Popup Text¶
conjunction="and"
verb="occur together in"
noun="instances"
These change parts of the popup text:
e.g. we could change it to:
Chord(matrix, names, conjunction="&", verb="appear together in", noun="cases",).show()Symmetric and Asymmetric Diagrams¶
symmetric=True
This turns the symmetric mode on or off. The primary support is for symmetric diagrams, but there is some support for asymmetric ones.
For example, this will update the popup text to reflect the asymmetry of relationships.
matrix = [
[0, 5, 3, 4, 7, 3],
[2, 0, 6, 5, 2, 7],
[6, 3, 0, 4, 5, 7],
[8, 3, 4, 0, 4, 7],
[2, 8, 6, 7, 0, 4],
[5, 3, 3, 4, 2, 0],
]
Chord(matrix, names, symmetric=False).show()Download Button¶
allow_download=False
This sets the visibility of the Download button. This uses client-side scripts to create and download an SVG from the current visualisation. This has been tested on Firefox and Safari.
Chord(matrix, names, allow_download=True).show()Popup Details, Thumbnails, and Custom HTML¶
details=[],
details_thumbs=[],
In the basic version of chord, matrix and names are the only sets of data that can be used to create a chord diagram. In the Pro version, you can also use details and details_thumbs. These enable the rich hover boxes.
First, we'll reduce the co-occurrence frequency in our matrix to make the following examples easier to follow.
matrix = [
[0, 2, 3, 1, 4, 1],
[2, 0, 2, 1, 3, 2],
[3, 2, 0, 1, 2, 2],
[1, 1, 1, 0, 2, 2],
[4, 3, 2, 2, 0, 1],
[1, 2, 2, 2, 1, 0],
]Let's create and populate an example details and details_thumbs variable.
details = [
[[], ["Movie 1","Movie 2"], ["Movie 3","Movie 4","Movie 5"], ["Movie 6","Movie 7"], ["Movie 8","Movie 9","Movie 10","Movie 11"], ["Movie 12"]],
[["Movie 13","Movie 14"], [], ["Movie 15","Movie 16"], ["Movie 17"], ["Movie 18","Movie 19","Movie 20"], ["Movie 21","Movie 22"]],
[["Movie 23","Movie 24","Movie 25"], ["Movie 26","Movie 27"], [], ["Movie 28"], ["Movie 29","Movie 30"], ["Movie 31","Movie 32"]],
[["Movie 33"], ["Movie 34"], ["Movie 35"], [], ["Movie 36","Movie 37"], ["Movie 38","Movie 39"]],
[["Movie 40","Movie 41","Movie 42","Movie 43"], ["Movie 44","Movie 45","Movie 46"], ["Movie 47","Movie 48"], ["Movie 49","Movie 50"], [], ["Movie 51"]],
[["Movie 52"], ["Movie 53","Movie 54"], ["Movie 55","Movie 56"], ["Movie 57","Movie 58"], ["Movie 59"], []],
]The details parameter can accept HTML.
details_thumbs = [
[[], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png"]],
[["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], [], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"]],
[["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], [], ["https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"]],
[["https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png"], [], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"]],
[["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], [], ["https://datacrayon.com/images/lablet.png"]],
[["https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png","https://datacrayon.com/images/lablet.png"], ["https://datacrayon.com/images/lablet.png"], []],
]It looks a bit messy, so let's achieve some clarity using the pandas.DataFrame HTML table output.
import pandas as pd
pd.DataFrame(details)pd.DataFrame(details_thumbs)Chord(matrix, names, details=details, details_thumbs=details_thumbs).show()Popup Thumbnails Spacing and Size¶
thumbs_width=85
thumbs_margin=5
thumbs_font_size=14
These allow changing the spacing and size of the details thumbnails.
Chord(matrix, names, details=details, details_thumbs=details_thumbs,
thumbs_width=40, thumbs_margin=10, thumbs_font_size=10).show()Divided (Bipartite) Chord Diagram¶
divide=False,
divide_idx=0,
divide_size=0.5,
-
divideenables/disables the divided chord diagram mode. -
divide_idxsets the dividing point of the matrix. -
divide_sizesets the size of the spacing that creates the divide.
First, we'll change the data in matrix and names such that they're suitable for our bipartite chord diagram. We'll add some new colours too.
matrix = [
[0, 0, 0, 1, 4, 1],
[0, 0, 0, 1, 3, 2],
[0, 0, 0, 1, 2, 2],
[1, 1, 1, 0, 0, 0],
[4, 3, 2, 0, 0, 0],
[1, 2, 2, 0, 0, 0],
]
names = ["A", "B", "C", "1", "2", "3"]
colors = ["#7400B8", "#5E60CE", "#5684D6", "#56CFE1", "#64DFDF", "#80FFDB"]Chord(matrix, names, colors=colors, divide=True, divide_idx=3).show()Divided (Bipartite) Chord Diagram Labels¶
divide_left_label="",
divide_right_label=""
These parameters allow you to label either side of the divided chord diagram.
Chord(matrix, names, colors=colors, divide=True, divide_idx=3,
divide_left_label="Left Side", divide_right_label="Right Side").show()Colored Diagonals¶
colored_diagonals=True
This sets the visibility of the occurrences (not co-occurrences), i.e. the representation of the diagonal of the matrix.
names=['one','two','three','four','five','six']
matrix = [
[19, 5, 6, 4, 7, 4],
[5, 4, 5, 4, 6, 5],
[6, 5, 0, 4, 5, 5],
[4, 4, 4, 0, 5, 5],
[7, 6, 5, 5, 0, 4],
[4, 5, 5, 5, 4, 0],
]
Chord(matrix, names, title="Diagonals coloured").show()
Chord(matrix, names, title="Diagonals not coloured", colored_diagonals=False).show()Equal-sized Sections¶
This makes use of the colored_diagonals feature above. The idea is to determine the max frequency in our plot, and set all the diagonals to that value. Let's assume that is 20 for this example.
names=['one','two','three','four','five','six']
matrix = [
[20, 0, 0, 0, 0, 0],
[0, 20, 0, 0, 0, 0],
[0, 0, 20, 0, 0, 0],
[0, 0, 0, 20, 0, 0],
[0, 0, 0, 0, 20, 0],
[0, 0, 0, 0, 0, 20],
]
Chord(matrix, names, title="Diagonals not coloured", colored_diagonals=False).show()We can see above that this creates a chord diagram with equal-sized segments. All we need to do now is introduce our relationships, and subtract them from the corresponding diagonal value.
matrix = [
[11, 4, 5, 0, 0, 0],
[4, 14, 2, 0, 0, 0],
[5, 2, 13, 0, 0, 0],
[0, 0, 0, 20, 0, 0],
[0, 0, 0, 0, 20, 0],
[0, 0, 0, 0, 0, 20],
]
Chord(matrix, names, title="Diagonals not coloured", colored_diagonals=False).show()We could add some padding to separate them too.
matrix = [
[11, 4, 5, 0, 0, 0],
[4, 14, 2, 0, 0, 0],
[5, 2, 13, 0, 0, 0],
[0, 0, 0, 20, 0, 0],
[0, 0, 0, 0, 20, 0],
[0, 0, 0, 0, 0, 20],
]
Chord(matrix, names, title="Diagonals not coloured", colored_diagonals=False, padding=0.2).show()Mix and Match¶
matrix = [
[0, 5, 6, 4, 7, 4],
[5, 0, 5, 4, 6, 5],
[6, 5, 0, 4, 5, 5],
[4, 4, 4, 0, 5, 5],
[7, 6, 5, 5, 0, 4],
[4, 5, 5, 5, 4, 0],
]
names = ["Action", "Adventure", "Comedy", "Drama", "Fantasy", "Thriller"]Chord(matrix, names, padding=0.5, reverse_gradients=True, colors="d3.schemeAccent",
curved_labels=True, label_color="#777777", font_size_large="30px",
inner_radius_scale=0.45, outer_radius_scale=1.4, arc_numbers=True).show()-
Tintarev, N., Rostami, S., & Smyth, B. (2018, April). Knowing the unknown: visualising consumption blind-spots in recommender systems. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (pp. 1396-1399). ↩
Support this work
You can access this notebook and more by getting the e-book, Data is Beautiful.
Data is Beautiful
A practical book on data visualisation that shows you how to create static and interactive visualisations that are engaging and beautiful.
Get the book