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Animal Crossing Villagers - Species and Personalities

Dr. Shahin Rostami

2020-05-17

6 min read

Source

Contents Chat Share Follow Download Source

Preamble
Introduction

The Dataset

Data Wrangling
Chord Diagram
Chord Diagram with Names
Conclusion

Made with Plotapi

You can create beautiful, interactive, and engaging visualisations like this one with Plotapi in any programming language. Learn how to make beautiful visualisations with the book, Data is Beautiful.

Plotapi - Chord Diagram

Preamble¶

In [1]:

import numpy as np                   # for multi-dimensional containers 
import pandas as pd                  # for DataFrames
import itertools
from plotapi import Chord
Chord.set_license("your username", "your license key")

Introduction¶

In previous sections, we visualised co-occurrences of Pokémon type. Whilst it was interesting to look at, the dataset only contained Pokémon from the first six geerations. In this section, we're going to use the TidyTuesday Animal Crossing villagers dataset to visualise the relationship between Species and .

The Dataset¶

The dataset documentation states that we can expect 13 variables per each of the 1017 Pokémon of the first eight generations.

Let's download the mirrored dataset and have a look for ourselves.

In [2]:

data_url = 'https://shahinrostami.com/datasets/ac_villagers.csv'
data = pd.read_csv(data_url)
data.head()

Out[2]:

	id	name	gender	species	birthday	personality	song	phrase	full_id	url
0	admiral	Admiral	male	bird	Jan-27	cranky	Steep Hill	aye aye	villager-admiral	https://shahinrostami.com/images/data-is-beaut...
1	agent-s	Agent S	female	squirrel	07-Feb	peppy	DJ K.K.	sidekick	villager-agent-s	https://shahinrostami.com/images/data-is-beaut...
2	agnes	Agnes	female	pig	Apr-21	uchi	K.K. House	snuffle	villager-agnes	https://shahinrostami.com/images/data-is-beaut...
3	al	Al	male	gorilla	Oct-18	lazy	Steep Hill	Ayyeeee	villager-al	https://shahinrostami.com/images/data-is-beaut...
4	alfonso	Alfonso	male	alligator	06-Sep	lazy	Forest Life	it'sa me	villager-alfonso	https://shahinrostami.com/images/data-is-beaut...

capitalise the name, personality, and species of each villager.

In [3]:

data['name'] = data['name'].str.capitalize()
data['personality'] = data['personality'].str.capitalize()
data['species'] = data['species'].str.capitalize()

It looks good so far, but let's confirm the 13 variables against 1017 samples from the documentation.

In [4]:

data.shape

Out[4]:

(391, 10)

Perfect, that's exactly what we were expecting.

Data Wrangling¶

We need to do a bit of data wrangling before we can visualise our data. We can see from the columns names that the Pokémon types are split between the columns Type 1 and Type 2.

In [5]:

pd.DataFrame(data.columns.values.tolist())

Out[5]:

	0
0	id
1	name
2	gender
3	species
4	birthday
5	personality
6	song
7	phrase
8	full_id
9	url

So let's select just these two columns and work with a list containing only them as we move forward.

In [6]:

species_personality = pd.DataFrame(data[['species', 'personality']].values)
species_personality

Out[6]:

	0	1
0	Bird	Cranky
1	Squirrel	Peppy
2	Pig	Uchi
3	Gorilla	Lazy
4	Alligator	Lazy
...	...	...
386	Horse	Peppy
387	Wolf	Cranky
388	Koala	Snooty
389	Deer	Smug
390	Octopus	Lazy

391 rows × 2 columns

Now for the names of our types.

In [7]:

left = np.unique(pd.DataFrame(species_personality)[0]).tolist()
pd.DataFrame(left)

Out[7]:

	0
0	Alligator
1	Anteater
2	Bear
3	Bird
4	Bull
5	Cat
6	Chicken
7	Cow
8	Cub
9	Deer
10	Dog
11	Duck
12	Eagle
13	Elephant
14	Frog
15	Goat
16	Gorilla
17	Hamster
18	Hippo
19	Horse
20	Kangaroo
21	Koala
22	Lion
23	Monkey
24	Mouse
25	Octopus
26	Ostrich
27	Penguin
28	Pig
29	Rabbit
30	Rhino
31	Sheep
32	Squirrel
33	Tiger
34	Wolf

In [8]:

right = np.unique(pd.DataFrame(species_personality)[1]).tolist()
pd.DataFrame(right)

Out[8]:

	0
0	Cranky
1	Jock
2	Lazy
3	Normal
4	Peppy
5	Smug
6	Snooty
7	Uchi

Which we can now use to create the matrix.

In [9]:

features= left+right
d = pd.DataFrame(0, index=features, columns=features)

In [ ]:

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

We can build 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.

In [10]:

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

In [11]:

for x in species_personality:
    d.at[x[0], x[1]] += 1

In [12]:

Out[12]:

	Alligator	Anteater	Bear	Bird	Bull	Cat	Chicken	Cow	Cub	Deer	...	Tiger	Wolf	Cranky	Jock	Lazy	Normal	Peppy	Smug	Snooty	Uchi
Alligator	0	0	0	0	0	0	0	0	0	0	...	0	0	1	2	2	1	0	0	1	0
Anteater	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	0	1	2	1	1	0
Bear	0	0	0	0	0	0	0	0	0	0	...	0	0	5	1	1	1	2	2	0	3
Bird	0	0	0	0	0	0	0	0	0	0	...	0	0	1	4	2	1	2	2	1	0
Bull	0	0	0	0	0	0	0	0	0	0	...	0	0	3	1	2	0	0	0	0	0
Cat	0	0	0	0	0	0	0	0	0	0	...	0	0	2	3	3	3	5	1	5	1
Chicken	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	2	1	0	1	2	1
Cow	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	1	0	2	0
Cub	0	0	0	0	0	0	0	0	0	0	...	0	0	2	2	4	4	2	0	1	1
Deer	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	2	1	0	2	1	2
Dog	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	6	3	2	1	1	1
Duck	0	0	0	0	0	0	0	0	0	0	...	0	0	0	2	4	2	4	1	4	0
Eagle	0	0	0	0	0	0	0	0	0	0	...	0	0	4	2	0	1	0	1	1	0
Elephant	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	4	3	0	0	2	0
Frog	0	0	0	0	0	0	0	0	0	0	...	0	0	3	5	3	2	1	2	1	1
Goat	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	2	0	1	1	1
Gorilla	0	0	0	0	0	0	0	0	0	0	...	0	0	3	2	1	0	0	1	1	1
Hamster	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	1	1	2	1	0
Hippo	0	0	0	0	0	0	0	0	0	0	...	0	0	2	1	0	1	1	1	1	0
Horse	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	3	2	2	3	2	1
Kangaroo	0	0	0	0	0	0	0	0	0	0	...	0	0	2	0	0	3	0	0	2	1
Koala	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	3	0	1	1	1
Lion	0	0	0	0	0	0	0	0	0	0	...	0	0	1	3	1	0	0	2	0	0
Monkey	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	2	1	1	0	1	1
Mouse	0	0	0	0	0	0	0	0	0	0	...	0	0	2	3	1	2	4	1	2	0
Octopus	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	1	1	0	0	0	0
Ostrich	0	0	0	0	0	0	0	0	0	0	...	0	0	0	1	1	2	1	1	3	1
Penguin	0	0	0	0	0	0	0	0	0	0	...	0	0	1	2	4	1	1	1	2	1
Pig	0	0	0	0	0	0	0	0	0	0	...	0	0	2	3	2	3	2	1	1	1
Rabbit	0	0	0	0	0	0	0	0	0	0	...	0	0	1	2	4	1	8	1	2	1
Rhino	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	2	0	0	0	1
Sheep	0	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	3	1	2	4	2
Squirrel	0	0	0	0	0	0	0	0	0	0	...	0	0	2	1	1	5	3	1	4	1
Tiger	0	0	0	0	0	0	0	0	0	0	...	0	0	1	3	0	0	2	0	1	0
Wolf	0	0	0	0	0	0	0	0	0	0	...	0	0	5	0	0	1	1	1	3	0
Cranky	1	1	5	1	3	2	1	0	2	1	...	1	5	0	0	0	0	0	0	0	0
Jock	2	1	1	4	1	3	1	0	2	1	...	3	0	0	0	0	0	0	0	0	0
Lazy	2	0	1	2	2	3	2	0	4	2	...	0	0	0	0	0	0	0	0	0	0
Normal	1	1	1	1	0	3	1	1	4	1	...	0	1	0	0	0	0	0	0	0	0
Peppy	0	2	2	2	0	5	0	1	2	0	...	2	1	0	0	0	0	0	0	0	0
Smug	0	1	2	2	0	1	1	0	0	2	...	0	1	0	0	0	0	0	0	0	0
Snooty	1	1	0	1	0	5	2	2	1	1	...	1	3	0	0	0	0	0	0	0	0
Uchi	0	0	3	0	0	1	1	0	1	2	...	0	0	0	0	0	0	0	0	0	0

43 rows × 43 columns

Chord Diagram¶

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

In [13]:

colors =["#ff2200","#ffcc00","#ace6cb","#0057d9","#633366","#73341d","#665f00","#00ffcc","#001433","#e6acda","#ffa280","#eeff00","#336663","#001f73","#ff00aa","#ffd9bf","#f2ffbf","#36ced9","#737399","#73003d","#ff8800","#44ff00","#00a2f2","#6600ff","#ff0044","#99754d","#416633","#004d73","#5e008c","#bf606c","#332200","#60bf60","#acd2e6","#e680ff","#66333a","#3d005c","#6e0060","#99005d","#bd0055","#db2f48","#f05738","#fc7e23","#ffa600"]

In [14]:

names = left + right

Finally, we can put it all together.

In [15]:

Chord(d.values.tolist(), names,colors=colors, wrap_labels=False, margin=40, font_size_large=10).show()

Plotapi - Chord Diagram

In [24]:

Chord(d.values.tolist(), names,credit=True, colors=colors, wrap_labels=False,
      margin=40, font_size_large=7,noun="villagers",
        details_separator="", bipartite=True, bipartite_idx=len(left),bipartite_size=.2, width=850).show()

Plotapi - Chord Diagram

Chord Diagram with Names¶

It would be nice to show a list of Pokémon names when hovering over co-occurring Pokémon types. To do this, we can make use of the optional details parameter.

In [ ]:

Next, we'll create an empty multi-dimensional array with the same shape as our matrix.

In [17]:

details = np.empty((len(names),len(names)),dtype=object)
details_thumbs = np.empty((len(names),len(names)),dtype=object)

Now we can populate the details array with lists of Pokémon names in the correct positions.

In [18]:

for count_x, item_x in enumerate(names):
    for count_y, item_y in enumerate(names):
        details_urls = data[
            (data['species'].isin([item_x, item_y])) &
            (data['personality'].isin([item_y, item_x]))]['url'].to_list()
        
        details_names = data[
            (data['species'].isin([item_x, item_y])) &
            (data['personality'].isin([item_y, item_x]))]['name'].to_list()
        
        urls_names = np.column_stack((details_urls, details_names))
        if(urls_names.size > 0):
            details[count_x][count_y] = details_names
            details_thumbs[count_x][count_y] = details_urls

        else:
            details[count_x][count_y] = []
            details_thumbs[count_x][count_y] = []

details=pd.DataFrame(details).values.tolist()
details_thumbs=pd.DataFrame(details_thumbs).values.tolist()

In [19]:

len(right)

Out[19]:

Finally, we can put it all together but this time with the details matrix passed in.

In [30]:

Chord(d.values.tolist(), names,credit=True, colors=colors, wrap_labels=False,
      padding=0, font_size_large=10,details=details,details_thumbs=details_thumbs,noun="villagers",
        details_separator="", bipartite=True, 
      bipartite_idx=len(left),divide_size=.2, width=800,).show()

Plotapi - Chord Diagram

In [ ]:

In [21]:

np.empty(shape=(6,1)).tolist()

Out[21]:

[[2.291755454583e-312],
 [2.22809558106e-312],
 [2.143215749443e-312],
 [2.37663528627e-312],
 [2.29175545472e-312],
 [0.0]]

In [22]:

Out[22]:

	Alligator	Anteater	Bear	Bird	Bull	Cat	Chicken	Cow	Cub	Deer	...	Tiger	Wolf	Cranky	Jock	Lazy	Normal	Peppy	Smug	Snooty	Uchi
Alligator	0	0	0	0	0	0	0	0	0	0	...	0	0	1	2	2	1	0	0	1	0
Anteater	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	0	1	2	1	1	0
Bear	0	0	0	0	0	0	0	0	0	0	...	0	0	5	1	1	1	2	2	0	3
Bird	0	0	0	0	0	0	0	0	0	0	...	0	0	1	4	2	1	2	2	1	0
Bull	0	0	0	0	0	0	0	0	0	0	...	0	0	3	1	2	0	0	0	0	0
Cat	0	0	0	0	0	0	0	0	0	0	...	0	0	2	3	3	3	5	1	5	1
Chicken	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	2	1	0	1	2	1
Cow	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	1	0	2	0
Cub	0	0	0	0	0	0	0	0	0	0	...	0	0	2	2	4	4	2	0	1	1
Deer	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	2	1	0	2	1	2
Dog	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	6	3	2	1	1	1
Duck	0	0	0	0	0	0	0	0	0	0	...	0	0	0	2	4	2	4	1	4	0
Eagle	0	0	0	0	0	0	0	0	0	0	...	0	0	4	2	0	1	0	1	1	0
Elephant	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	4	3	0	0	2	0
Frog	0	0	0	0	0	0	0	0	0	0	...	0	0	3	5	3	2	1	2	1	1
Goat	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	2	0	1	1	1
Gorilla	0	0	0	0	0	0	0	0	0	0	...	0	0	3	2	1	0	0	1	1	1
Hamster	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	1	1	2	1	0
Hippo	0	0	0	0	0	0	0	0	0	0	...	0	0	2	1	0	1	1	1	1	0
Horse	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	3	2	2	3	2	1
Kangaroo	0	0	0	0	0	0	0	0	0	0	...	0	0	2	0	0	3	0	0	2	1
Koala	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	3	0	1	1	1
Lion	0	0	0	0	0	0	0	0	0	0	...	0	0	1	3	1	0	0	2	0	0
Monkey	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	2	1	1	0	1	1
Mouse	0	0	0	0	0	0	0	0	0	0	...	0	0	2	3	1	2	4	1	2	0
Octopus	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	1	1	0	0	0	0
Ostrich	0	0	0	0	0	0	0	0	0	0	...	0	0	0	1	1	2	1	1	3	1
Penguin	0	0	0	0	0	0	0	0	0	0	...	0	0	1	2	4	1	1	1	2	1
Pig	0	0	0	0	0	0	0	0	0	0	...	0	0	2	3	2	3	2	1	1	1
Rabbit	0	0	0	0	0	0	0	0	0	0	...	0	0	1	2	4	1	8	1	2	1
Rhino	0	0	0	0	0	0	0	0	0	0	...	0	0	1	1	1	2	0	0	0	1
Sheep	0	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	3	1	2	4	2
Squirrel	0	0	0	0	0	0	0	0	0	0	...	0	0	2	1	1	5	3	1	4	1
Tiger	0	0	0	0	0	0	0	0	0	0	...	0	0	1	3	0	0	2	0	1	0
Wolf	0	0	0	0	0	0	0	0	0	0	...	0	0	5	0	0	1	1	1	3	0
Cranky	1	1	5	1	3	2	1	0	2	1	...	1	5	0	0	0	0	0	0	0	0
Jock	2	1	1	4	1	3	1	0	2	1	...	3	0	0	0	0	0	0	0	0	0
Lazy	2	0	1	2	2	3	2	0	4	2	...	0	0	0	0	0	0	0	0	0	0
Normal	1	1	1	1	0	3	1	1	4	1	...	0	1	0	0	0	0	0	0	0	0
Peppy	0	2	2	2	0	5	0	1	2	0	...	2	1	0	0	0	0	0	0	0	0
Smug	0	1	2	2	0	1	1	0	0	2	...	0	1	0	0	0	0	0	0	0	0
Snooty	1	1	0	1	0	5	2	2	1	1	...	1	3	0	0	0	0	0	0	0	0
Uchi	0	0	3	0	0	1	1	0	1	2	...	0	0	0	0	0	0	0	0	0	0

43 rows × 43 columns

Conclusion¶

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

In [23]:

import json

data = {"matrix": matrix,
       "details_thumbs": details_thumbs,
       "data_table": data_table}

with open("pokemon_types.json", "w") as fp:
    json.dump(data, fp)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [23], in <cell line: 3>()
      1 import json
----> 3 data = {"matrix": matrix,
      4        "details_thumbs": details_thumbs,
      5        "data_table": data_table}
      7 with open("pokemon_types.json", "w") as fp:
      8     json.dump(data, fp)

NameError: name 'matrix' is not defined

Made with Plotapi

You can create beautiful, interactive, and engaging visualisations like this one with Plotapi in any programming language. Learn how to make beautiful visualisations with the 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