## Data is Beautiful

A practical book on data visualisation that shows you how to create static and interactive visualisations that are engaging and beautiful.

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## Preamble

import itertools

import pandas as pd  # for DataFrames
from plotapi import Chord


## Introduction

In this section, we're going to be pointing our beautifully colourful lens towards the warm and aromatic world of coffee. In particular, we're going to be visualising the co-occurrence of coffee bean variety and origin in over a thousand coffee reviews.

Note

This section uses the PlotAPI software to create a visualisation. Check out the link to produce the same output!

### The Dataset

We're going to use the popular Coffee Quality Institute Database which I have forked on GitHub for posterity. The file arabatica_data.csv contains the data we'll be using throughout this section, and the first thing we'll want to do is to load the data and output some samples for a sanity check.

data = pd.read_csv("https://datacrayon.com/datasets/arabica_data.csv")

Unnamed: 0 Species Owner Country.of.Origin Farm.Name Lot.Number Mill ICO.Number Company Altitude ... Color Category.Two.Defects Expiration Certification.Body Certification.Address Certification.Contact unit_of_measurement altitude_low_meters altitude_high_meters altitude_mean_meters
2 3 Arabica grounds for health admin Guatemala san marcos barrancas "san cristobal cuch NaN NaN NaN NaN 1600 - 1800 m ... NaN 0 May 31st, 2011 Specialty Coffee Association 36d0d00a3724338ba7937c52a378d085f2172daa 0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660 m 1600.0 1800.0 1700.0
3 4 Arabica yidnekachew dabessa Ethiopia yidnekachew dabessa coffee plantation NaN wolensu NaN yidnekachew debessa coffee plantation 1800-2200 ... Green 2 March 25th, 2016 METAD Agricultural Development plc 309fcf77415a3661ae83e027f7e5f05dad786e44 19fef5a731de2db57d16da10287413f5f99bc2dd m 1800.0 2200.0 2000.0

5 rows × 44 columns

## Data Wrangling

By viewing the CSV directly we can see our desired columns are named Country.of.Origin and Variety. Let's print out the columns to make sure they exist in the data we've loaded.

data.columns

Index(['Unnamed: 0', 'Species', 'Owner', 'Country.of.Origin', 'Farm.Name',
'Lot.Number', 'Mill', 'ICO.Number', 'Company', 'Altitude', 'Region',
'Producer', 'Number.of.Bags', 'Bag.Weight', 'In.Country.Partner',
'Processing.Method', 'Aroma', 'Flavor', 'Aftertaste', 'Acidity', 'Body',
'Balance', 'Uniformity', 'Clean.Cup', 'Sweetness', 'Cupper.Points',
'Total.Cup.Points', 'Moisture', 'Category.One.Defects', 'Quakers',
'Color', 'Category.Two.Defects', 'Expiration', 'Certification.Body',
'altitude_low_meters', 'altitude_high_meters', 'altitude_mean_meters'],
dtype='object')

Great! We can see both of these columns exist in our DataFrame.

Now let's take a peek at the unique values in both of these columns to see if any obvious issues stand out. We'll start with the Country.of.Origin column.

data["Country.of.Origin"].unique()

array(['Ethiopia', 'Guatemala', 'Brazil', 'Peru', 'United States',
'United States (Hawaii)', 'Indonesia', 'China', 'Costa Rica',
'Mexico', 'Uganda', 'Honduras', 'Taiwan', 'Nicaragua',
'Tanzania, United Republic Of', 'Kenya', 'Thailand', 'Colombia',
'United States (Puerto Rico)', 'Haiti', 'Burundi', 'Vietnam',
'Philippines', 'Rwanda', 'Malawi', 'Laos', 'Zambia', 'Myanmar',
'Mauritius', 'Cote d?Ivoire', nan, 'India'], dtype=object)

We can see some points that may cause issues when it comes to our visualisation.

There appears to be at least one nan value in Country.of.Origin. We're only interested in coffee bean reviews which aren't missing this data, so let's remove any samples where nan exists.

data = data[data["Country.of.Origin"].notna()]


Also, the entries in Country.of.Origin will be used as labels on our visualisation. Ideally, we don't want these to be longer than they need to be. So let's shorten some of the longer names.

data["Country.of.Origin"] = data["Country.of.Origin"].replace(
"United States (Hawaii)", "Hawaii"
)

data["Country.of.Origin"] = data["Country.of.Origin"].replace(
"Tanzania, United Republic Of", "Tanzania"
)

data["Country.of.Origin"] = data["Country.of.Origin"].replace(
"United States (Puerto Rico)", "Puerto Rico"
)


Now let's take a peek at the unique Variety column.

data["Variety"].unique()

array([nan, 'Other', 'Bourbon', 'Catimor', 'Ethiopian Yirgacheffe',
'Caturra', 'SL14', 'Sumatra', 'SL34', 'Hawaiian Kona',
'Yellow Bourbon', 'SL28', 'Gesha', 'Catuai', 'Pacamara', 'Typica',
'Sumatra Lintong', 'Mundo Novo', 'Java', 'Peaberry', 'Pacas',
'Mandheling', 'Ruiru 11', 'Arusha', 'Ethiopian Heirlooms',
'Moka Peaberry', 'Sulawesi', 'Blue Mountain', 'Marigojipe',
'Pache Comun'], dtype=object)

We can see this column also has at least one nan entry, so let's remove these too.

data = data[data["Variety"].notna()]


Also, there appears to be at least one entry of Other for the Variety. For this visualisation, we're not interested in Other, so let's remove them too.

data = data[data["Variety"] != "Other"]


From previous Chord diagram visualisations we know that they can become too crowded with too many different categories. With this in mind, let's choose to visualise only the top $12$ most frequently occurring Country.of.Origin and Variety.

data = data[
data["Country.of.Origin"].isin(
list(data["Country.of.Origin"].value_counts()[:12].index)
)
]

data = data[
data["Variety"].isin(list(data["Variety"].value_counts()[:12].index))
]


As we're creating a bipartite chord diagram, let's define what labels will be going on the left and the right.

On the left, we'll have all of our countries of origin.

left = list(data["Country.of.Origin"].value_counts().index)[::-1]
pd.DataFrame(left)

0
0 China
2 Uganda
3 Kenya
4 Hawaii
5 Costa Rica
6 Honduras
7 Taiwan
8 Brazil
9 Colombia
10 Guatemala
11 Mexico

And on the right, we'll have all of our varieties.

right = list(data["Variety"].value_counts().index)
pd.DataFrame(right)

0
0 Caturra
1 Bourbon
2 Typica
3 Catuai
4 Hawaiian Kona
5 Yellow Bourbon
6 Mundo Novo
7 SL14
8 SL28
9 Catimor
10 Pacas
11 SL34

We're good to go! So let's select just these two columns and work with a DataFrame containing only them as we move forward.

origin_variety = pd.DataFrame(data[["Country.of.Origin", "Variety"]].values)
origin_variety

0 1
0 Guatemala Bourbon
1 China Catimor
2 Costa Rica Caturra
3 Brazil Bourbon
4 Uganda SL14
... ... ...
884 Honduras Catuai
885 Honduras Catuai
886 Mexico Bourbon
887 Guatemala Catuai
888 Honduras Caturra

889 rows × 2 columns

Our chord diagram will need two inputs: the co-occurrence matrix, and a list of names to label the segments.

We can build this list of names by adding together the labels for the left and right side of our bipartite diagram.

names = left + right

pd.DataFrame(names)

0
0 China
2 Uganda
3 Kenya
4 Hawaii
5 Costa Rica
6 Honduras
7 Taiwan
8 Brazil
9 Colombia
10 Guatemala
11 Mexico
12 Caturra
13 Bourbon
14 Typica
15 Catuai
16 Hawaiian Kona
17 Yellow Bourbon
18 Mundo Novo
19 SL14
20 SL28
21 Catimor
22 Pacas
23 SL34

Now we can create our empty co-occurrence matrix using these type names for the row and column indeces.

matrix = pd.DataFrame(0, index=names, columns=names)
matrix

China El Salvador Uganda Kenya Hawaii Costa Rica Honduras Taiwan Brazil Colombia ... Typica Catuai Hawaiian Kona Yellow Bourbon Mundo Novo SL14 SL28 Catimor Pacas SL34
China 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
El Salvador 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Uganda 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Kenya 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Hawaii 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Costa Rica 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Honduras 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Taiwan 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Brazil 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Colombia 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Guatemala 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Mexico 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Caturra 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Bourbon 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Typica 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Catuai 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Hawaiian Kona 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Yellow Bourbon 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Mundo Novo 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
SL14 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
SL28 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Catimor 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Pacas 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
SL34 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

24 rows × 24 columns

We can populate a co-occurrence matrix with the following approach. We'll start by creating a list with every type pairing in its original and reversed form.

origin_variety = list(
itertools.chain.from_iterable(
(i, i[::-1]) for i in origin_variety.values
)
)


Which we can now use to create the matrix.

for pairing in origin_variety:
matrix.at[pairing[0], pairing[1]] += 1

matrix = matrix.values.tolist()


We can list the DataFrame for better presentation.

pd.DataFrame(matrix)

0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
0 0 0 0 0 0 0 0 0 0 0 ... 2 0 0 0 0 0 0 12 0 0
1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 2 0
2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 17 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 14 0 0 8
4 0 0 0 0 0 0 0 0 0 0 ... 0 0 44 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 0 ... 0 15 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0 0 0 0 ... 0 21 0 0 0 0 0 1 4 0
7 0 0 0 0 0 0 0 0 0 0 ... 59 0 0 3 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0 0 0 ... 0 19 0 32 20 0 0 0 0 0
9 0 0 0 0 0 0 0 0 0 0 ... 3 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 ... 0 9 0 0 0 0 0 0 6 0
11 0 0 0 0 0 0 0 0 0 0 ... 137 4 0 0 12 0 0 0 1 0
12 0 0 0 0 0 28 23 2 0 129 ... 0 0 0 0 0 0 0 0 0 0
13 0 13 3 0 0 1 0 2 41 0 ... 0 0 0 0 0 0 0 0 0 0
14 2 0 0 0 0 0 0 59 0 3 ... 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 15 21 0 19 0 ... 0 0 0 0 0 0 0 0 0 0
16 0 0 0 0 44 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
17 0 0 0 0 0 0 0 3 32 0 ... 0 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0 0 20 0 ... 0 0 0 0 0 0 0 0 0 0
19 0 0 17 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
20 0 0 0 14 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
21 12 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
22 0 2 0 0 0 0 4 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
23 0 0 0 8 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

24 rows × 24 columns

## Chord Diagram

Time to visualise the co-occurrence of items using a chord diagram. We are going to use a list of custom colours that represent the items.

Let's specify some colours for the left and right sides.

colors = [
"#ff575c","#ff914d","#ffca38","#f2fa00","#C3F500","#94f000",
"#00fa68","#00C1A2","#0087db","#0054f0","#5d00e0","#2F06EB",
"#6f1d1b","#955939","#A87748","#bb9457","#7f5e38","#432818",
"#6e4021","#99582a","#cc9f69","#755939","#BAA070","#ffe6a7"]


Finally, we can put it all together using PlotAPI Chord.

Chord(
matrix,
names,
colors=colors,
width=900,
font_size="12px",
font_size_large="16px",
noun="coffee bean reviews",
title="Coffee Bean Reviews - Variety and Origin",
bipartite=True,
bipartite_idx=len(left),
bipartite_size=0.6,
)


## Conclusion

In this section, we demonstrated how to conduct some data wrangling on a downloaded dataset to prepare it for a bipartite chord diagram. Our chord diagram is interactive, so you can use your mouse or touchscreen to investigate the co-occurrences!

## 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

## ISBN

978-1-915907-15-8

## Cite

Rostami, S. (2021). Data Is Beautiful. Polyra Publishing.