GoT dataset analysis

ANDB Project Task

Published on 16.11.2022

Firstly, let’s look on the examplary rows from the dataset to get an idea about its contents.

X	Name	Allegiances	Book.of.Death	Death.Chapter	Book.Intro.Chapter	Gender	Nobility	book1	book2	book3	book4	book5	title	culture	dateOfBirth	house	spouse	isAliveSpouse	isMarried	age	isPopular	popularity	DeathYear	isAlive
1	Addam Marbrand	lannister	Alive	Alive	56	Male	Nobel	Yes	Yes	Yes	Yes	No	ser	unknown	NA	house marbrand	notMarried	notMarried	No	NA	0	0.1304348	Alive	Yes
2	Aegon Frey (Jinglebell)	none	3	51	49	Male	Nobel	No	No	Yes	No	No	unknown	unknown	NA	unknown	unknown	unknown	unknown	NA	NA	NA	299	No
3	Aegon Targaryen	house targaryen	Alive	Alive	5	Male	Nobel	No	No	No	No	Yes	unknown	unknown	NA	unknown	unknown	unknown	unknown	NA	NA	NA	Alive	Yes
4	Adrack Humble	house greyjoy	5	20	20	Male	Nobel	No	No	No	No	Yes	unknown	unknown	NA	unknown	unknown	unknown	unknown	NA	NA	NA	300	No
5	Aemon Costayne	lannister	Alive	Alive	unknown	Male	Nobel	No	No	Yes	No	No	unknown	unknown	NA	house costayne	notMarried	notMarried	No	NA	0	0.0100334	Alive	Yes
6	Aemon Estermont	baratheon	Alive	Alive	unknown	Male	Nobel	No	Yes	Yes	No	No	ser	stormlands	NA	house estermont	notMarried	notMarried	No	NA	0	0.0301003	Alive	Yes

Univariate & bivariate analysis

Statistics based on allegiance to a house

faces

The most popular titles within the dataset
Var1	Freq
ser	153
maester	15
cupbearer	8
lord	7
bloodrider	6
princess	5

Analysis of dead characters’ features

Characters’ introductions and deaths over the course of chapters

As could be expected, most of characters were introduced in the earlier chapters of analysed series and the number of ‘newcomers’ is decreasing with the time.

However, it can be seen that numbers of deaths are not following any trend…