[PYTHON] Visualize by adding "a bite" to the "boxplot" (boxen / swarm / violin)

Overview

• This is a memo of what I thought about the data visualization of kaggle's titanic.
• I want to visualize the distribution of "passenger age" for each "port on board".
• In such cases, it is common to use a boxplot (boxplot in seaborn).
• On the other hand, ** other ** visualization means can be used to add "a bite", so I summarized them.
• This time, as an alternative to boxplot of seaborn I would like to consider the following areas.
• boxenplot
• swarmplot
• violinplot
• I hope it helps someone, but it's just a work memo & personal opinion.

motivation

Boxplot

• At Titanic, the age of passengers at each port of embarkation looks like this. (First, boxplot)
• For the time being, the following can be read.
• The median age is around 25 to 30 years old, no matter which port you board from.
• There is no big difference in the median and the first and third quantiles. (Queenstown is a little younger?)
• Outliers (data for older people) are noticeable for passengers boarding from Southampton

If you try Swarmplot

• If you try to make this a Swarmplot, the quartile value will be hard to see, but it will be nice to add a "smell".
• You will be able to be aware of the ** number of data for each series. (Actually, ** Queenstown is a small number **)
• Easy to read even for those who ** do not know the meaning of boxes and beards **
• Easy to read ** dense and sparse parts ** of data

Add "a bite" to the boxplot

Try changing functions and options

• Change function
• boxenplot、swarmplot、violinplot
• Change options
• Specify hue and split

If you do etc., you can add "a bite"

Putting it all together (cheat sheet)

• boxenplot does not have a split option **
• Note that the meaning of ** split option ** is slightly different between swarmplot and violinplot.

option	boxenplot	swarmplot	violinplot
Not specified
hue="Sex"
hue="Sex" split=True	None

"Which" should be used "when"?

• It's hard to say "this is for this purpose!", But ...
• If you compare each, you can see the characteristics.

Boxplot vs boxenplot

• There is no difference because only 2 letters (en) are changed alphabetically. Do you want to show it in ** ** quartile ** or in more ** finer quantiles **? Want to be aware of ** outliers **? Is the point

	Boxplot(boxplot)	boxenplot
display
Feature	Quartile, Maximum, minimum You can also see the situation of outliers	ThanFine quantileCan be seen Hard to see as outliers

Boxplot vs swarmplot

• Compared to boxplot, swarmplot that is conscious of individual data and captures it ** continuously **
• You can see the ** number, density, and difference ** of the data, but the plot ** cost is high ** and it is difficult for a large amount of data.

	Boxplot(boxplot)	swarmplot
display
Feature	section(Quantile)To catch as plotLow cost	Awareness of the individualAnd continuously capture the data dataDifferences by number and seriesCan be understood しかし、plotHigh cost

swarmplot vs violinplot

• Like swarmplot, violin plot ** handles data continuously ** and plots ** costs can be reduced **
• Instead, the number of data and the difference between series ** become unaware. ** **

	swarmplot	violinplot
display
Feature	Awareness of the individualAnd continuously capture the data dataDifferences by number and seriesCan be understood But the plotHigh cost	Awareness of the individualせず、dataのI can't see the numberBut, Continuous understanding of overall trends plotKeep costs downCan do things.

Summary

• There are advantages and disadvantages, and it should be selected according to the application, but in summary, is it as follows?

Interval vs continuous	How to add "Hitomi"	What visualization method should I choose?
Data**section (Quantile)**Treated with	OutliersIf you want to be aware of	Boxplot(boxplot)
	From the quartileDetailedIn the display,	boxenplot
DataContinuouslyHandle,	ThatNumber and densityIf you want to show	swarmplot
	Keep plot costs down Overall trendIf you want to show	violinplot