GoT Characters Screen Time by Kaggle | Other

About this Dataset

GoT Characters Screen Time

How Long did Characters Spend on Screen?

By Ændrew Rininsland [source]

About this dataset

This dataset provides an eye-opening look into the characters and actors involved in the globally acclaimed TV series, Game of Thrones. By examining the screen time and episodes for each character, as well as their portrayed actor or actress's IMDB URL, one can gain remarkable insight into which characters have seized the spotlight in this epic saga. Compiled by ninewheels0 on IMDB, this dataset took a long time to amass and deserves appreciation for doing so. Each character is listed with their length of screen time measured in minutes with fractional seconds (i.e., 1.5 minutes means one minute and thirty seconds). With each character's contribution to screen time equal to what they will remember by within the minds of those watching Game of Thrones around the world, witness how each actor has intruded into our lives across multiple seasons!

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How to use the dataset

How to Use the GoT Characters Screen Time Dataset
This Kaggle dataset contains information about the screentime of characters in the Game of Thrones TV series, including their name, IMDB URL, screentime (in minutes with fractional seconds), number of episodes appeared in and the actor/actress portraying them. It is a helpful resource for game theorists who want to further study character arcs and build theories around key points in certain stories.

First off, make sure you acknowledge ninewheels0 on IMDB as they created this list before it was uploaded onto Kaggle. Read the “About this dataset” section carefully before getting started to remember all sources and credits should be given out properly.

To begin studying and analyzing this data set you can use various software tools that allow for analyzing and visualizing large data sets such as Python pandas function which allows you to easily study every aspect provided through columns such as name or portrayed_by_name). You can also use Tableau which let’s you turn your selected columns into charts or graphs so that patterns are easily found within large datasets. Additionally, tools such as Excel can also be used for similar purposes but not nearly as well organized so it would only work if very few interactions are done between different elements from within this CSV file.

When analyzing these huge datasets it is important to note down certain key questions you want to answer while understanding what kind of information is already presented there – what correlations exist? What could affect a certain element? Is there something specific I want to uncover through my analysis etc.. After deciding on those major things one should take a look at distinct elements present within each column such as its highest values (max) or lowest values (min). Afterwards one should remember to always check consistency with patterns found due do outliers before making any kind of accusations or assumptions afterwards - sometimes they might influence our results more than expected sometimes not providing us with much insight at all but instead just confusing our story even further . Knowing how each element interacts with other variables from within same dataset helps when looking into relation between two separate items from inside same file!

Finally after taking into account primarily described ways we can start drawing parallels between different parts present inside same As soon we did sufficient amount amount if observation steps we may even have enough evidence needed finish majority aspects related research phase like proving possible hypothesis correct or incorrect !

Research Ideas

Create an interactive feature on a website/app to compare the screentime across all characters in Game of Thrones

Analyze how the screentime of characters evolve over time and seasons

Using ML algorithms, explore different patterns in the data to identify relationships between screen time and other factors such as character gender, type etc

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: GOT_screentimes.csv

Column name	Description
name	The name of the character. (String)
imdb_url	The IMDB URL for the character. (URL)
screentime	The total screentime of the character in minutes with fractional seconds. (Float)
episodes	The number of episodes the character has appeared in. (Integer)
portrayed_by_name	The name of the actor or actress who portrayed the character. (String)
portrayed_by_imdb_url	The IMDB URL for the actor or actress who portrayed the character. (URL)

File: GoT-screentimes.csv

Column name	Description
name	The name of the character. (String)
screentime	The total screentime of the character in minutes with fractional seconds. (Float)
episodes	The number of episodes the character has appeared in. (Integer)
imdbUrl	The IMDB URL of the character. (String)
portrayedBy	The actor or actress who portrayed the character and their own respective IMDB URL. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Ændrew Rininsland.

Tables

Got Screentimes

@kaggle.thedevastator_uncover_the_mystery_behind_got_characters_screen.got_screentimes

15.96 KB
191 rows
6 columns


CREATE TABLE got_screentimes (
  "index" BIGINT,
  "name" VARCHAR,
  "imdburl" VARCHAR,
  "screentime" DOUBLE,
  "episodes" VARCHAR,
  "portrayedby" VARCHAR
);