Data Visualization 2

• This unit covers the layered framework for data visualization, i.e., the Grammar of Graphics.

• The codes implement the ideas of layered graph building using the module, plotnine, which is essentially the Python version of R ggplot2.

import numpy as np
import pandas as pd
from plotnine import *
from plotnine.data import mpg, mtcars
%matplotlib inline

type(mpg)

pandas.core.frame.DataFrame

Data Frame Preparation

pd.DataFrame.head(mpg)

	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

pd.DataFrame.head(mtcars)

	name	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
0	Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
1	Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
2	Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
3	Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
4	Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2

Basic Graphs

(ggplot(mpg) +
aes(x = 'class', fill = 'class') +
geom_bar(size=20))

<ggplot: (8789024295544)>

(
    ggplot(mpg) +
    aes(x = 'class', y = 'hwy', fill = 'class') +
    geom_boxplot() +
    labs(x = 'Car Classes', y = 'Highway Milage')
)

<ggplot: (-9223363247830482144)>

(
    ggplot(mpg) +
    aes(x = 'cty', y = 'hwy', fill = 'class') +
    geom_point(alpha = .7)
)

<ggplot: (8789024293647)>

(
ggplot(mpg) +
aes(x = 'cyl', y = 'hwy', fill = 'class') +
    geom_boxplot()
)

<ggplot: (-9223363247830482172)>

More Complex Graphs

• Visualize four-dimensions (4-D)

(
    ggplot(mtcars, aes('wt','mpg', color='factor(gear)')) +
    geom_point() +
    facet_wrap('~cyl')+
    theme_bw()+
    labs(x='Weight', y='Miles/gallon')+
    scale_color_discrete(name='Forward Gear Number (Factor)')
)

<ggplot: (8789039975232)>

(
    ggplot(mtcars, aes('wt','mpg',color='factor(gear)'))+
    geom_point()+
    geom_smooth(method="lm")+
    theme_bw()
)

<ggplot: (8789024521094)>

• Visualize 5-D

  • x

  • y

  • facet

  • color

  • size

(
    ggplot(mtcars, aes('wt','mpg',color='factor(gear)', size='cyl'))+
    geom_point()+
    facet_wrap('~am')+
    theme_bw()

)

<ggplot: (8789056543229)>

• Visualize 6-D

  • x

  • y

  • facet (2 dimensions)

  • color

  • size

(
    ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)', size='cyl'))+
    geom_point()+
    facet_grid('am~carb')+
    theme_bw()
)

<ggplot: (-9223363247814487292)>

More?

• Check Hans Rosling’s famous visualization of global population (a dynamic graphing)