Visualization Notes: February 2022

Sunday, February 20, 2022

R NYC Data Example #5

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print(r.eval("""
library(tidyverse)
user<-'https://raw.githubusercontent.com/osanchez2323/Portfolio/master/'
folder1<-paste0(user,'Data%20Science%20Takes%20on%20Education/Data/')
file1<-paste0(folder1,'2016%20School%20Explorer.csv')
file2<-paste0(folder1,'D5%20SHSAT%20Registrations%20and%20Testers.csv')
se<-read.csv(file1); d5<-read.csv(file2)
se<-se[,4:161]; se[se=='N/A']<-NA
cat(c(paste0('se => ',list(dim(se))),
      paste0('d5 => ',list(dim(d5)))),'\n')
se$School.Name<-as.character(se$School.Name)
se$School.Name[428]<-'P.S. 212 D12'
se$School.Name[1024]<-'P.S. 212 D30'
se$School.Name[713]<-'P.S. 253 D21'
se$School.Name[909]<-'P.S. 253 D27'
percent_list<-
c('Percent.ELL','Percent.Asian','Percent.Black',
  'Percent.Hispanic','Percent.Black...Hispanic','Percent.White',
  'Student.Attendance.Rate','Percent.of.Students.Chronically.Absent',
  'Rigorous.Instruction..','Collaborative.Teachers..',
  'Supportive.Environment..','Effective.School.Leadership..',
  'Strong.Family.Community.Ties..','Trust..')
target_list<-c('Average.ELA.Proficiency','Average.Math.Proficiency')
economic_list<-c('Economic.Need.Index','School.Income.Estimate')
rating_list<-
c('Rigorous.Instruction.Rating','Collaborative.Teachers.Rating',
  'Supportive.Environment.Rating','Effective.School.Leadership.Rating',
  'Strong.Family.Community.Ties.Rating','Trust.Rating',
  'Student.Achievement.Rating')
str2num<-function(x){
    x<-as.character(x)
    for (s in c('%','$',',')){x<-sub(s,'',x)}
    x<-as.numeric(x)}
for (el in c(percent_list,target_list,economic_list)) {
    se[el]<-sapply(se[el],str2num)
    se<-se%>%dplyr::group_by(City)%>%
        fill(all_of(el),.direction='downup')%>%dplyr::ungroup()}
for (el in rating_list){
    vtable<-data.frame(table(se[el]))
    mvalue<-as.character(vtable$Var1[vtable$Freq==max(vtable$Freq)])
    se[el][is.na(se[el])]<-mvalue}
sum(is.na(se))
"""))

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
theme_set(theme_bw())
cols<-c('#3300ff','#ff0033','#9f9fff','#ff9f9f')
vc=c(cols[4],cols[1],cols[3],cols[2])
vtable<-data.frame(table(se[rating_list[1]]))[c(1,2,3,5),1:2]
p<-ggplot(vtable,aes(x=factor(1),y=Freq,fill=factor(Var1)))+ 
geom_bar(width=1,stat='identity')+ 
coord_polar(theta='y')+
scale_fill_manual(name=gsub('.',' ',rating_list[1]),values=vc)+
theme(axis.title=element_blank(),axis.line=element_blank(),
      axis.ticks.y=element_blank(),axis.text.y=element_blank(),
      axis.text.x=element_text(colour=cols[2],size=4,angle=60),
      legend.text=element_text(size=3),
      legend.key.size=unit(.3,'cm'),
      legend.key=element_rect(color='slategray'),
      title=element_text(size=4,hjust=.5),
      panel.background=element_rect(fill='slategray'))
ggsave(fn,width=2.1,height=1.5)
dev.off()
"""))

R NYC Data Example #4

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print(r.eval('''
library(tidyverse)
user<-'https://raw.githubusercontent.com/osanchez2323/Portfolio/master/'
folder1<-paste0(user,'Data%20Science%20Takes%20on%20Education/Data/')
file1<-paste0(folder1,'2016%20School%20Explorer.csv')
file2<-paste0(folder1,'D5%20SHSAT%20Registrations%20and%20Testers.csv')
se<-read.csv(file1); d5<-read.csv(file2)
se<-se[,4:161]; se[se=='N/A']<-NA
cat(c(paste0('se => ',list(dim(se))),
      paste0('d5 => ',list(dim(d5)))),'\n')
se$School.Name<-as.character(se$School.Name)
se$School.Name[428]<-'P.S. 212 D12'
se$School.Name[1024]<-'P.S. 212 D30'
se$School.Name[713]<-'P.S. 253 D21'
se$School.Name[909]<-'P.S. 253 D27'
percent_list<-
c('Percent.ELL','Percent.Asian','Percent.Black',
  'Percent.Hispanic','Percent.Black...Hispanic','Percent.White',
  'Student.Attendance.Rate','Percent.of.Students.Chronically.Absent',
  'Rigorous.Instruction..','Collaborative.Teachers..',
  'Supportive.Environment..','Effective.School.Leadership..',
  'Strong.Family.Community.Ties..','Trust..')
target_list<-c('Average.ELA.Proficiency','Average.Math.Proficiency')
economic_list<-c('Economic.Need.Index','School.Income.Estimate')
rating_list<-
c('Rigorous.Instruction.Rating','Collaborative.Teachers.Rating',
  'Supportive.Environment.Rating','Effective.School.Leadership.Rating',
  'Strong.Family.Community.Ties.Rating','Trust.Rating',
  'Student.Achievement.Rating')
str2num<-function(x){
    x<-as.character(x)
    for (s in c('%','$',',')){x<-sub(s,'',x)}
    x<-as.numeric(x)}
for (el in c(percent_list,target_list,economic_list)) {
    se[el]<-sapply(se[el],str2num)
    se<-se%>%dplyr::group_by(City)%>%
        fill(all_of(el),.direction='downup')%>%dplyr::ungroup()}
for (el in rating_list){
    vtable<-data.frame(table(se[el]))
    mvalue<-as.character(vtable$Var1[vtable$Freq==max(vtable$Freq)])
    se[el][is.na(se[el])]<-mvalue}
sum(is.na(se))
'''))

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print(r.eval('''
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
theme_set(theme_bw())
cols<-c('#3636ff','#ff3636','#6f6fff','#ff6f6f')
data<-data.frame(
    x=c(se$Longitude),y=c(se$Latitude),
    asian=c(se$Percent.Asian),
    white=c(se$Percent.White),
    black=c(se$Percent.Black))
vc=c(asian=cols[2],white='white',black=cols[1])
ggplot(data,aes(x=x,y=y,color=variable))+ 
    geom_point(aes(size=black,col='black'))+ 
    geom_point(aes(size=asian,col='asian',alpha=.4))+
    geom_point(aes(size=white,col='white',alpha=.4))+
    scale_size(range=c(0,1))+
    scale_colour_manual(name='groups',values=vc)+
    guides(size=guide_legend(title='percent'),alpha='none')+ 
    ggtitle('Distribution by Population Groups')+
    labs(x='longitude',y='latitude')+
    theme(axis.text.x=element_text(size=3,vjust=.5),
          axis.text.y=element_text(size=3,hjust=.5),
          legend.text=element_text(size=3),
          legend.key.size=unit(.2,'cm'),
          legend.key=element_rect(color='slategray'),
          legend.position='top',
          panel.grid.major=element_line(color='whitesmoke',size=.1),
          panel.grid.minor=element_line(color='gray80',size=.05),
          title=element_text(size=4,hjust=.5),
          panel.background=element_rect(fill='slategray'))
ggsave(fn,width=2.0,height=2.4)
dev.off()
'''))

R NYC Data Example #3

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print(r.eval("""
library(tidyverse)
user<-'https://raw.githubusercontent.com/osanchez2323/Portfolio/master/'
folder1<-paste0(user,'Data%20Science%20Takes%20on%20Education/Data/')
file1<-paste0(folder1,'2016%20School%20Explorer.csv')
file2<-paste0(folder1,'D5%20SHSAT%20Registrations%20and%20Testers.csv')
se<-read.csv(file1); d5<-read.csv(file2)
se<-se[,4:161]; se[se=='N/A']<-NA
cat(c(paste0('se => ',list(dim(se))),
      paste0('d5 => ',list(dim(d5)))),'\n')
se$School.Name<-as.character(se$School.Name)
se$School.Name[428]<-'P.S. 212 D12'
se$School.Name[1024]<-'P.S. 212 D30'
se$School.Name[713]<-'P.S. 253 D21'
se$School.Name[909]<-'P.S. 253 D27'
percent_list<-
c('Percent.ELL','Percent.Asian','Percent.Black',
  'Percent.Hispanic','Percent.Black...Hispanic','Percent.White',
  'Student.Attendance.Rate','Percent.of.Students.Chronically.Absent',
  'Rigorous.Instruction..','Collaborative.Teachers..',
  'Supportive.Environment..','Effective.School.Leadership..',
  'Strong.Family.Community.Ties..','Trust..')
target_list<-c('Average.ELA.Proficiency','Average.Math.Proficiency')
economic_list<-c('Economic.Need.Index','School.Income.Estimate')
rating_list<-
c('Rigorous.Instruction.Rating','Collaborative.Teachers.Rating',
  'Supportive.Environment.Rating','Effective.School.Leadership.Rating',
  'Strong.Family.Community.Ties.Rating','Trust.Rating',
  'Student.Achievement.Rating')
str2num<-function(x){
    x<-as.character(x)
    for (s in c('%','$',',')){x<-sub(s,'',x)}
    x<-as.numeric(x)}
for (el in c(percent_list,target_list,economic_list)) {
    se[el]<-sapply(se[el],str2num)
    se<-se%>%dplyr::group_by(City)%>%
        fill(all_of(el),.direction='downup')%>%dplyr::ungroup()}
for (el in rating_list){
    vtable<-data.frame(table(se[el]))
    mvalue<-as.character(vtable$Var1[vtable$Freq==max(vtable$Freq)])
    se[el][is.na(se[el])]<-mvalue}
sum(is.na(se))
"""))

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
theme_set(theme_bw())
cols<-c('#3636ff','#ff3636','#6f6fff','#ff6f6f')
data<-data.frame(
    x=rep(c(1:1272),3),
    y=c(se$Percent.Asian,se$Percent.White,se$Percent.Black),
    Groups=rep(c('Asian','White','Black'),each=1272))
ggplot(data,aes(x=x,y=y,fill=Groups,color=Groups),xlab='')+
geom_area(size=.1)+
scale_fill_manual(values=c(cols[2],cols[1],'white'))+
scale_color_manual(values=c(cols[2],cols[1],'white'))+
ggtitle('Distribution by Population Groups')+
theme(title=element_text(size=5,hjust=.5),
      axis.title=element_blank(),
      axis.text.x=element_text(size=3,vjust=.5),
      axis.text.y=element_text(size=3,hjust=.5),
      legend.text=element_text(size=3),
      legend.key.size=unit(.3,'cm'),
      legend.key=element_rect(color='slategray'),
      panel.grid.major=element_line(color='whitesmoke',size=.1),
      panel.grid.minor=element_line(color='gray80',size=.05),
      panel.background=element_rect(fill='slategray'))
ggsave(fn,width=2.1,height=1.5)
dev.off()
"""))

R NYC Data Example #2

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print(r.eval("""
library(tidyverse)
user<-'https://raw.githubusercontent.com/osanchez2323/Portfolio/master/'
folder1<-paste0(user,'Data%20Science%20Takes%20on%20Education/Data/')
file1<-paste0(folder1,'2016%20School%20Explorer.csv')
file2<-paste0(folder1,'D5%20SHSAT%20Registrations%20and%20Testers.csv')
se<-read.csv(file1); d5<-read.csv(file2)
se<-se[,4:161]; se[se=='N/A']<-NA
cat(c(paste0('se => ',list(dim(se))),
      paste0('d5 => ',list(dim(d5)))),'\n')
se$School.Name<-as.character(se$School.Name)
se$School.Name[428]<-'P.S. 212 D12'
se$School.Name[1024]<-'P.S. 212 D30'
se$School.Name[713]<-'P.S. 253 D21'
se$School.Name[909]<-'P.S. 253 D27'
percent_list<-
c('Percent.ELL','Percent.Asian','Percent.Black',
  'Percent.Hispanic','Percent.Black...Hispanic','Percent.White',
  'Student.Attendance.Rate','Percent.of.Students.Chronically.Absent',
  'Rigorous.Instruction..','Collaborative.Teachers..',
  'Supportive.Environment..','Effective.School.Leadership..',
  'Strong.Family.Community.Ties..','Trust..')
target_list<-c('Average.ELA.Proficiency','Average.Math.Proficiency')
economic_list<-c('Economic.Need.Index','School.Income.Estimate')
rating_list<-
c('Rigorous.Instruction.Rating','Collaborative.Teachers.Rating',
  'Supportive.Environment.Rating','Effective.School.Leadership.Rating',
  'Strong.Family.Community.Ties.Rating','Trust.Rating',
  'Student.Achievement.Rating')
str2num<-function(x){
    x<-as.character(x)
    for (s in c('%','$',',')){x<-sub(s,'',x)}
    x<-as.numeric(x)}
for (el in c(percent_list,target_list,economic_list)) {
    se[el]<-sapply(se[el],str2num)
    se<-se%>%dplyr::group_by(City)%>%
        fill(all_of(el),.direction='downup')%>%dplyr::ungroup()}
for (el in rating_list){
    vtable<-data.frame(table(se[el]))
    mvalue<-as.character(vtable$Var1[vtable$Freq==max(vtable$Freq)])
    se[el][is.na(se[el])]<-mvalue}
sum(is.na(se))
"""))

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print(r.eval("""
data<-data.frame(x=c(se$Average.ELA.Proficiency,
                     se$Average.Math.Proficiency),
                 y=rep(c('ELA','Math'),each=1272))
cols<-c('#3636ff','#ff3636','#6f6fff','#ff6f6f')
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
theme_set(theme_bw())
g<-ggplot(data,aes(x=x,fill=y,color=y))+ 
geom_histogram(aes(y=..density..),size=.1,
               alpha=.6,binwidth=.02,position='identity')+
geom_density(alpha=.3)+
labs(x='value',y='density')+
scale_fill_manual(values=cols[1:2])+ 
scale_color_manual(values=cols[1:2])+
ggtitle('Distribution of Average Proficiency Indicators')+
theme(axis.text.x=element_text(size=3,angle=65,vjust=.5),
      axis.text.y=element_text(size=3,hjust=.5),
      legend.text=element_text(size=3),
      legend.key.size=unit(.3,'cm'),
      legend.title=element_blank(),
      title=element_text(size=5))
ggsave(fn,width=2.1,height=1.8)
dev.off()
"""))

R NYC Data Example #1

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print(r.eval("""
library(tidyverse)
user<-'https://raw.githubusercontent.com/osanchez2323/Portfolio/master/'
folder1<-paste0(user,'Data%20Science%20Takes%20on%20Education/Data/')
file1<-paste0(folder1,'2016%20School%20Explorer.csv')
file2<-paste0(folder1,'D5%20SHSAT%20Registrations%20and%20Testers.csv')
se<-read.csv(file1); d5<-read.csv(file2)
se<-se[,4:161]; se[se=='N/A']<-NA
cat(c(paste0('se => ',list(dim(se))),
      paste0('d5 => ',list(dim(d5)))),'\n')
se$School.Name<-as.character(se$School.Name)
se$School.Name[428]<-'P.S. 212 D12'
se$School.Name[1024]<-'P.S. 212 D30'
se$School.Name[713]<-'P.S. 253 D21'
se$School.Name[909]<-'P.S. 253 D27'
percent_list<-
c('Percent.ELL','Percent.Asian','Percent.Black',
  'Percent.Hispanic','Percent.Black...Hispanic','Percent.White',
  'Student.Attendance.Rate','Percent.of.Students.Chronically.Absent',
  'Rigorous.Instruction..','Collaborative.Teachers..',
  'Supportive.Environment..','Effective.School.Leadership..',
  'Strong.Family.Community.Ties..','Trust..')
target_list<-c('Average.ELA.Proficiency','Average.Math.Proficiency')
economic_list<-c('Economic.Need.Index','School.Income.Estimate')
rating_list<-
c('Rigorous.Instruction.Rating','Collaborative.Teachers.Rating',
  'Supportive.Environment.Rating','Effective.School.Leadership.Rating',
  'Strong.Family.Community.Ties.Rating','Trust.Rating',
  'Student.Achievement.Rating')
str2num<-function(x){
    x<-as.character(x)
    for (s in c('%','$',',')){x<-sub(s,'',x)}
    x<-as.numeric(x)}
for (el in c(percent_list,target_list,economic_list)) {
    se[el]<-sapply(se[el],str2num)
    se<-se%>%dplyr::group_by(City)%>%
        fill(all_of(el),.direction='downup')%>%dplyr::ungroup()}
for (el in rating_list){
    vtable<-data.frame(table(se[el]))
    mvalue<-as.character(vtable$Var1[vtable$Freq==max(vtable$Freq)])
    se[el][is.na(se[el])]<-mvalue}
sum(is.na(se))
"""))

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,width=650,bg='whitesmoke')
par(mar=c(2,28,1,2))
counts<-table(d5$School.name)
pal<-colorRampPalette(colors=c('steelblue','darkblue'))(28)
barplot(sort(counts),horiz=TRUE,col=pal,ylab='',las=1,
        main='D5 SHSAT Number of Notes by School')
dev.off()
"""))

Saturday, February 19, 2022

R Ggplot2 Box Plots

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
library(ggplot2)
theme_set(theme_bw())
user<-'https://raw.githubusercontent.com/VioletaStoyanova/'
file<-paste0(user,'Untidy_data/master/')
file<-paste0(file,'chicago-nmmaps.csv')
chic<-read.csv(file)
chic$date<-as.Date(as.character(chic$date),'%Y-%m-%d')
g<-ggplot(chic,aes(season,o3))+
geom_boxplot(aes(fill=season,color=season),size=.2,outlier.size=.2)+
labs(x='season',y='ozone')+ggtitle('Chicago`s Daily Ozone Levels')+
scale_color_brewer(palette='Set2')+
scale_fill_brewer(palette='Set1')+
theme(axis.text.x=element_text(
          size=4,angle=45,vjust=.5,family='Courier'),
      axis.text.y=element_text(size=4,hjust=.5,family='Courier'),
      legend.position='none',
      title=element_text(
          size=7,color='slategray',family='Comic Sans MS'))
ggsave(fn,width=2.1,height=2.1)
dev.off()"""))

R Ggplot2 & CSV

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
library(ggplot2)
theme_set(theme_bw())
user<-'https://raw.githubusercontent.com/VioletaStoyanova/'
file<-paste0(user,'Untidy_data/master/')
file<-paste0(file,'chicago-nmmaps.csv')
chic<-read.csv(file)
chic$date<-as.Date(as.character(chic$date),'%Y-%m-%d')
g<-ggplot(chic,aes(x=date,y=temp,color=season,shape=season))
g+geom_point(size=.2)+
scale_color_brewer(palette='Set1')+
labs(x='year',y='temperature (F)')+
ggtitle('Chicago`s Daily Temperature')+
theme(axis.text.x=element_text(size=4,angle=30,vjust=.5),
      axis.text.y=element_text(size=4,hjust=.5),
      legend.text=element_text(size=4),
      legend.title=element_blank(),
      legend.position='top',
      legend.key.size=unit(.3,'cm'),
      title=element_text(size=4,color='slategray'))
ggsave(fn,width=2.1,height=2.1)
dev.off()
"""))

Friday, February 18, 2022

R Ggplot2 Density Plots

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
library('MASS'); library(ggplot2)
theme_set(theme_bw())
data(Boston)
p<-ggplot(Boston,aes(x=c(medv)))+ 
geom_histogram(aes(y=..density..),binwidth=1,alpha=.2,
               fill='slategray',color='royalblue')+
geom_density(alpha=.6,lw=1,fill='royalblue',color='slategray')+
labs(title='Distribution for `MEDV`',
     x='indicator',y='density')+
theme(axis.text.x=element_text(size=4,vjust=.5),
      axis.text.y=element_text(size=4,hjust=.5),
      title=element_text(size=5,color='slategray'))    
ggsave(fn,width=2.1,height=1.4)
dev.off(); summary(Boston)
"""))

R Ggplot2 Area Plots

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
library(ggplot2)
theme_set(theme_bw())
ggplot(economics,aes(x=date,y=psavert))+
geom_area(fill='steelblue',color='slategray',cex=.3,alpha=.7)+
labs(title='Personal Savings Rate',
     x='date',y='personal savings rate')+
theme(axis.text.x=element_text(size=4,angle=15,vjust=.5),
      axis.text.y=element_text(size=4,hjust=.5),
      title=element_text(size=5,color='slategray'))
ggsave(fn,width=2.1,height=1.5)
dev.off()"""))

Thursday, February 17, 2022

R Ggplot2 Bar Charts

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print(r.eval("""
options(bitmapType='cairo'); fn<-'Rplots.png'
png(fn,pointsize=12,bg='whitesmoke')
library(ggplot2)
theme_set(theme_bw())
g<-ggplot(mpg,aes(manufacturer))
g+geom_bar(aes(fill=class),width=.8)+
scale_fill_brewer(palette='BuPu')+
labs(title='Bar Charts for Categorical Variables')+
theme(axis.text.x=element_text(size=3,angle=65,vjust=.5),
      axis.text.y=element_text(size=3,hjust=.5),
      legend.text=element_text(size=3),
      legend.key.size=unit(.3,'cm'),
      title=element_text(size=5))
ggsave(fn,width=2.1,height=1.5)
dev.off()
"""))

Thursday, February 10, 2022

Matplotlib Color Tables

TSSFL Open Discussion Forums

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import matplotlib.pyplot as pl,matplotlib.colors as mcols
def plot_colortable(colors,title,sort_colors=True,emptycols=0,dpi=72):
    cell_width,cell_height=212,22
    swatch_width=48
    margin,topmargin=12,40
    if sort_colors is True:
        by_hsv=sorted(
            (tuple(mcols.rgb_to_hsv(mcols.to_rgb(color))),name)
            for name,color in colors.items())
        names=[name for hsv,name in by_hsv]
    else:
        names=list(colors)
    n=len(names)
    ncols=4-emptycols; nrows=n//ncols+int(n%ncols>0)
    width=cell_width*4+2*margin
    height=cell_height*nrows+margin+topmargin
    fig,ax=pl.subplots(figsize=(width/dpi,height/dpi),dpi=dpi)
    fig.subplots_adjust(margin/width,margin/height,
                        (width-margin)/width,
                        (height-topmargin)/height)
    ax.set_xlim(0,cell_width*4)
    ax.set_ylim(cell_height*(nrows-.5),-cell_height/2.)
    ax.yaxis.set_visible(False); ax.xaxis.set_visible(False)
    ax.set_axis_off()
    ax.set_title(title,fontsize=20,loc='left',pad=10)
    for i,name in enumerate(names):
        row=i%nrows; col=i//nrows
        y=row*cell_height
        swatch_start_x=cell_width*col
        swatch_end_x=cell_width*col+swatch_width
        text_pos_x=cell_width*col+swatch_width+7
        ax.text(text_pos_x,y,name,fontsize=10,
                horizontalalignment='left',
                verticalalignment='center') 
        ax.hlines(y,swatch_start_x,swatch_end_x,
                  color=colors[name],linewidth=18)
    return fig
col_fig=plot_colortable(mcols.CSS4_COLORS,'CSS Colors')
col_fig.savefig('css4_colors.png')
pl.show()

Plotly Classic Animations

TSSFL Open Discussion Forums

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import plotly.graph_objects as go,numpy as np,pandas as pd
from plotly.offline import plot
url='https://raw.githubusercontent.com/plotly/'+\
    'datasets/master/gapminderDataFiveYear.csv'
dataset=pd.read_csv(url)
years=['1952','1962','1967','1972','1977','1982','1987',
       '1992','1997','2002','2007']
continents=[]
for continent in dataset['continent']:
    if continent not in continents: continents.append(continent)
fig_dict={"data":[],"layout":{},"frames":[]}
fig_dict["layout"]["xaxis"]={"range":[30,85],"title":"Life Expectancy"}
fig_dict["layout"]["yaxis"]={"title":"GDP per Capita","type":"log"}
fig_dict["layout"]["hovermode"]="closest"
fig_dict["layout"]["updatemenus"]=[
    {"buttons":[
            {"args":[None,{"frame":{"duration":500,"redraw":False},
                           "fromcurrent":True, 
                           "transition":{"duration":300,
                           "easing":"quadratic-in-out"}}],
             "label":"Play","method":"animate"},
            {"args":[[None],{"frame":{"duration":0,"redraw":False},
                             "mode":"immediate",
                             "transition":{"duration":0}}],
             "label":"Pause","method":"animate"}],
     "direction":"left","pad":{"r":10,"t":87},
     "showactive":False,"type":"buttons",
     "x":0.1,"xanchor":"right","y":0,"yanchor":"top"}]
sliders_dict={"active":0,"yanchor":"top","xanchor":"left",
              "currentvalue":{"font":{"size":20},"prefix":"Year:",
                              "visible":True,"xanchor":"right"},
    'transition':{'duration':300,'easing':'cubic-in-out'},
    'pad':{'b':10,'t':50},'len':.9,'x':.1,'y':0,'steps':[]}

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year=1952
for continent in continents:
    dataset_by_year=dataset[dataset["year"]==year]
    dataset_by_year_and_cont=dataset_by_year[
        dataset_by_year["continent"]==continent]
    data_dict={
        "x":list(dataset_by_year_and_cont["lifeExp"]),
        "y":list(dataset_by_year_and_cont["gdpPercap"]),
        "mode":"markers",
        "text":list(dataset_by_year_and_cont["country"]),
        "marker":{"sizemode":"area","sizeref":200000,
                  "size":list(dataset_by_year_and_cont["pop"])},
        "name":continent}
    fig_dict["data"].append(data_dict)
for year in years:
    frame={"data":[],"name":str(year)}
    for continent in continents:
        dataset_by_year=dataset[dataset["year"]==int(year)]
        dataset_by_year_and_cont=dataset_by_year[
            dataset_by_year["continent"]==continent]
        data_dict={
            "x":list(dataset_by_year_and_cont["lifeExp"]),
            "y":list(dataset_by_year_and_cont["gdpPercap"]),
            "mode":"markers",
            "text":list(dataset_by_year_and_cont["country"]),
            "marker":{"sizemode":"area","sizeref":200000,
                      "size":list(dataset_by_year_and_cont["pop"])},
            'name':continent}
        frame['data'].append(data_dict) 
    fig_dict['frames'].append(frame)
    slider_step={'args':[[year],
                        {'frame':{'duration':300,'redraw':False},
                         'mode':'immediate',
                         'transition':{'duration':300}}],
                 'label': year,'method':'animate'}
    sliders_dict['steps'].append(slider_step)
fig_dict['layout']['sliders']=[sliders_dict]
fig=go.Figure(fig_dict) ; plot(fig)
fpath='https://sagecell.sagemath.org/kernel/'
fpath+=os.getcwd()[14:]+'/files/'
s1='<iframe src="'
s2='temp-plot.html" width=600 height=400/></iframe>'
html(s1+fpath+s2)

Matplotlib-GIF-3D for Lorenz Systems

TSSFL Open Discussion Forums

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import numpy as np,os
from scipy import integrate
from matplotlib import pyplot as pl,animation as an
from mpl_toolkits.mplot3d import Axes3D
N_trajectories=20
def lorentz_deriv(params,t0,sigma=10.,beta=8./3,rho=28.):
    x,y,z=params
    return [sigma*(y-x),x*(rho-z)-y,x*y-beta*z]
np.random.seed(1)
x0=-15+30*np.random.random((N_trajectories,3))
t=np.linspace(0,4,1000)
x_t=np.asarray([integrate.odeint(lorentz_deriv,x0i,t) for x0i in x0])
fig=pl.figure()
ax=fig.add_axes([0,0,1,1],projection='3d'); ax.axis('off')
colors=pl.cm.jet(np.linspace(0,1,N_trajectories))
lines=sum([ax.plot([],[],[],'-',c=c) for c in colors],[])
pts=sum([ax.plot([],[],[],'o',c=c) for c in colors],[])
ax.set_xlim((-25,25)); ax.set_ylim((-35,35)); ax.set_zlim((5,55))
ax.view_init(30,0)
def init():
    for line,pt in zip(lines,pts):
        line.set_data([],[]); line.set_3d_properties([])
        pt.set_data([],[]); pt.set_3d_properties([])
    return lines+pts
def animate(i):
    i=(2*i)%x_t.shape[1]
    for line, pt, xi in zip(lines,pts,x_t):
        x,y,z=xi[:i].T
        line.set_data(x,y); line.set_3d_properties(z)
        pt.set_data(x[-1:],y[-1:]); pt.set_3d_properties(z[-1:])
    ax.view_init(30,.3*i)
    fig.canvas.draw()
    return lines+pts
anim=an.FuncAnimation(
    fig,animate,frames=500,interval=30,blit=True,save_count=1500)
f=r'animation.gif'
writergif=an.PillowWriter(fps=15)
anim.save(f,writer=writergif)
fpath='https://sagecell.sagemath.org/kernel/'
fpath+=os.getcwd()[14:]+'/files/'
s1='<figure><embed type="image/gif" src="'
s2='animation.gif" width=600/></figure>'
html(s1+fpath+s2)