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Video Game Titles - Publishers and Genres

Dr. Shahin Rostami

2020-06-10

7 min read

Contents Chat Share Follow Download Source

Preamble
Introduction

The Dataset

Data Wrangling
Chord Diagram
Chord Diagram with Names
Conclusion

Made with Chord Pro

You can create beautiful interactive visualisations like this one with Chord Pro. Learn how to make beautiful visualisations with the book, Data is Beautiful.

Chord Diagram

Preamble¶

In [1]:

import numpy as np                   # for multi-dimensional containers 
import pandas as pd                  # for DataFrames
import itertools
from chord import Chord

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/vgsales.csv'
data = pd.read_csv(data_url)
data.head()

Out[2]:

	Rank	Name	Platform	Year	Genre	Publisher	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
0	1	Wii Sports	Wii	2006.0	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
1	2	Super Mario Bros.	NES	1985.0	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
2	3	Mario Kart Wii	Wii	2008.0	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
3	4	Wii Sports Resort	Wii	2009.0	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
4	5	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37

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

In [3]:

data['Publisher'] = data['Publisher'].str.capitalize()
data['Genre'] = data['Genre'].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]:

(16598, 11)

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	Rank
1	Name
2	Platform
3	Year
4	Genre
5	Publisher
6	NA_Sales
7	EU_Sales
8	JP_Sales
9	Other_Sales
10	Global_Sales

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[['Publisher', 'Genre']].values).dropna().astype(str)
species_personality

Out[6]:

	0	1
0	Nintendo	Sports
1	Nintendo	Platform
2	Nintendo	Racing
3	Nintendo	Sports
4	Nintendo	Role-playing
...	...	...
16593	Kemco	Platform
16594	Infogrames	Shooter
16595	Activision	Racing
16596	7g//ames	Puzzle
16597	Wanadoo	Platform

16540 rows × 2 columns

Now for the names of our types.

In [7]:

#left = np.unique(pd.DataFrame(species_personality)[0]).tolist()
left = list(data.Publisher.value_counts()[:14].index)
pd.DataFrame(left)

Out[7]:

	0
0	Electronic arts
1	Activision
2	Namco bandai games
3	Ubisoft
4	Konami digital entertainment
5	Thq
6	Nintendo
7	Sony computer entertainment
8	Sega
9	Take-two interactive
10	Capcom
11	Atari
12	Tecmo koei
13	Square enix

In [8]:

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

Out[8]:

	0
0	Action
1	Adventure
2	Fighting
3	Misc
4	Platform
5	Puzzle
6	Racing
7	Role-playing
8	Shooter
9	Simulation
10	Sports
11	Strategy

Which we can now use to create the matrix.

In [9]:

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

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:
    if(x[0] in left or x[1] in left):
        d.at[x[0], x[1]] += 1

In [12]:

Out[12]:

	Electronic arts	Activision	Namco bandai games	Ubisoft	Konami digital entertainment	Thq	Nintendo	Sony computer entertainment	Sega	Take-two interactive	...	Fighting	Misc	Platform	Puzzle	Racing	Role-playing	Shooter	Simulation	Sports	Strategy
Electronic arts	0	0	0	0	0	0	0	0	0	0	...	39	46	16	7	159	35	139	116	561	37
Activision	0	0	0	0	0	0	0	0	0	0	...	7	103	60	7	74	41	159	23	144	22
Namco bandai games	0	0	0	0	0	0	0	0	0	0	...	134	97	19	20	27	151	37	29	51	61
Ubisoft	0	0	0	0	0	0	0	0	0	0	...	19	151	70	24	52	41	92	119	72	29
Konami digital entertainment	0	0	0	0	0	0	0	0	0	0	...	20	77	40	10	13	37	40	86	280	28
Thq	0	0	0	0	0	0	0	0	0	0	...	71	66	85	17	101	8	36	27	31	32
Nintendo	0	0	0	0	0	0	0	0	0	0	...	18	100	112	74	37	106	26	29	55	32
Sony computer entertainment	0	0	0	0	0	0	0	0	0	0	...	30	128	66	12	65	49	51	15	124	12
Sega	0	0	0	0	0	0	0	0	0	0	...	37	62	52	22	48	64	40	12	135	35
Take-two interactive	0	0	0	0	0	0	0	0	0	0	...	1	27	11	1	20	6	65	4	151	22
Capcom	0	0	0	0	0	0	0	0	0	0	...	58	11	46	6	13	38	25	2	3	3
Atari	0	0	0	0	0	0	0	0	0	0	...	37	26	21	22	36	28	40	9	56	17
Tecmo koei	0	0	0	0	0	0	0	0	0	0	...	12	14	1	0	5	47	3	13	39	50
Square enix	0	0	0	0	0	0	0	0	0	0	...	3	6	0	4	0	129	16	4	0	9
Action	183	310	248	193	148	194	79	90	101	93	...	0	0	0	0	0	0	0	0	0	0
Adventure	13	25	58	59	53	47	35	41	31	12	...	0	0	0	0	0	0	0	0	0	0
Fighting	39	7	134	19	20	71	18	30	37	1	...	0	0	0	0	0	0	0	0	0	0
Misc	46	103	97	151	77	66	100	128	62	27	...	0	0	0	0	0	0	0	0	0	0
Platform	16	60	19	70	40	85	112	66	52	11	...	0	0	0	0	0	0	0	0	0	0
Puzzle	7	7	20	24	10	17	74	12	22	1	...	0	0	0	0	0	0	0	0	0	0
Racing	159	74	27	52	13	101	37	65	48	20	...	0	0	0	0	0	0	0	0	0	0
Role-playing	35	41	151	41	37	8	106	49	64	6	...	0	0	0	0	0	0	0	0	0	0
Shooter	139	159	37	92	40	36	26	51	40	65	...	0	0	0	0	0	0	0	0	0	0
Simulation	116	23	29	119	86	27	29	15	12	4	...	0	0	0	0	0	0	0	0	0	0
Sports	561	144	51	72	280	31	55	124	135	151	...	0	0	0	0	0	0	0	0	0	0
Strategy	37	22	61	29	28	32	32	12	35	22	...	0	0	0	0	0	0	0	0	0	0

26 rows × 26 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]:

left[5] = 'THQ'

In [14]:

left[0] = 'EA'
left[2] = 'Namco'
left[4] = 'Konami'
left[7] = 'Sony'
left[9] = 'Take-Two'
left[-1] = "Square"
left[-2]= 'Tecmo'
left

Out[14]:

['EA',
 'Activision',
 'Namco',
 'Ubisoft',
 'Konami',
 'THQ',
 'Nintendo',
 'Sony',
 'Sega',
 'Take-Two',
 'Capcom',
 'Atari',
 'Tecmo',
 'Square']

In [15]:

right[7] = 'RPG'
right

Out[15]:

['Action',
 'Adventure',
 'Fighting',
 'Misc',
 'Platform',
 'Puzzle',
 'Racing',
 'RPG',
 'Shooter',
 'Simulation',
 'Sports',
 'Strategy']

In [16]:

colors =["#312f85",
         "#f4e301",
         "#f75802",
         "#3e4682",
         "#ad0332",
         "#666769",
         "#e80113",
         "#f78700",
         "#0100f4",
         "#1272c3","#f7cd01","#dd1a22","#00407b","#f70000",
         
        "#ff4400","#ffcc00","#5c6633","#00e63d","#00d6e6","#566d73","#3d85f2","#00fff2","#0000e6","#290066","#ff80e5","#731d28"]

In [17]:

names = left + right

Finally, we can put it all together.

In [18]:

Chord(d.values.tolist(), names,credit=True, colors=colors, wrap_labels=False,
      margin=40, font_size_large=7,noun="titles",
        details_separator="", divide=True, divide_idx=len(left),divide_size=.2, width=850).show()

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 [19]:

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 [20]:

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()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/miniconda3/envs/analytics/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2645             try:
-> 2646                 return self._engine.get_loc(key)
   2647             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'species'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-20-f54e3a0b2906> in <module>
      2     for count_y, item_y in enumerate(names):
      3         details_urls = data[
----> 4             (data['species'].isin([item_x, item_y])) &
      5             (data['personality'].isin([item_y, item_x]))]['url'].to_list()
      6 

~/miniconda3/envs/analytics/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2798             if self.columns.nlevels > 1:
   2799                 return self._getitem_multilevel(key)
-> 2800             indexer = self.columns.get_loc(key)
   2801             if is_integer(indexer):
   2802                 indexer = [indexer]

~/miniconda3/envs/analytics/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2646                 return self._engine.get_loc(key)
   2647             except KeyError:
-> 2648                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2649         indexer = self.get_indexer([key], method=method, tolerance=tolerance)
   2650         if indexer.ndim > 1 or indexer.size > 1:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'species'

In [ ]:

len(right)

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

In [ ]:

Chord(d.values.tolist(), names,credit=True, colors=colors, wrap_labels=False,
      margin=40, font_size_large=7,details=details,details_thumbs=details_thumbs,noun="villagers",
        details_separator="", divide=True, divide_idx=len(left),divide_size=.2, width=850).show()

In [ ]:

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

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!

Made with Chord Pro

You can create beautiful interactive visualisations like this one with Chord Pro. Learn how to make beautiful visualisations with the book, Data is Beautiful.