R equivalent python code gives different output - r

Question

I am trying to write R equivalent code in python but not getting the same result. The R code is as follows:
# Set parameters
max.people = 50
max.trials = 500
plot.step = 1
# load libraries
library(tidyverse)
#Set up an initial data frame
df<-data.frame("trial"=NA,"people"=NA, "val"=NA)
# Set up a common theme for plots
ztheme<-function(){
theme_classic()+
theme(panel.background=element_rect(fill="#F0F0F0", color="#F0F0F0"))+
theme(plot.background=element_rect(fill="#F0F0F0", color="#F0F0F0"))}
#Run main loop
for(trial in 1:max.trials){
# set up a buffer. Makes the program run a lot faster.
buff<-data.frame("trial"=NA,"people"=NA, "val"=NA)
for(people in 1:max.people){
buff<-rbind(buff,data.frame("trial"=trial,"people"=people, "val"=NA))
samp<-sample(1:365, people, replace=T)
if(length(unique(samp))==length(samp)){
buff$val[nrow(buff)]<-0
}else{
buff$val[nrow(buff)]<-1
}; rm(samp)}
df<-rbind(df, buff); rm(buff)
print(paste(round(trial/(max.trials)*100, 2), "% Complete", sep=""))
}
df<-subset(df, !is.na(df$trial))
rm(max.people); rm(people); rm(trial)
# Generate multiple plots of result
for(n in seq(plot.step,max.trials,plot.step)){
print(
ggplot(summarise(group_by(subset(df, trial<=n), people), prob=mean(val)), aes(people, prob))+
geom_bar(stat="identity", fill="steelblue1")+
geom_smooth(se=F, color="black", method="loess")+
scale_y_continuous(labels=scales::percent, limits=c(0,1))+
labs(title="Birthday Paradox",
subtitle=paste("Based on",n,"simulations."),
x="Number of People in Room",
y="One or More Matching Birthdays (True/False Ratio)",
caption="created by /u/zonination")+
ztheme())
ggsave(paste("bday_", formatC(n,width=5,flag = "0"), ".png", sep=""), height=4.5, width=7, dpi=120, type="cairo-png")
}; rm(n)
I have written equivalent code in python as follows:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import random
plt.style.use('ggplot')
maxTrials = 500
maxPeople = 50
plotStep = 1
df = pd.DataFrame(columns=['trial','people','val'])
for trial in range(plotStep, maxTrials+1):
buff = pd.DataFrame()
for people in range(plotStep,maxPeople+1):
buff = buff.append(pd.DataFrame({'trial':[trial],'people':[people],'val':[np.nan]}), ignore_index=True)
samp = [random.randint(1,366) for x in range(people)]
if len(set(samp)) == len(samp):
buff.at[len(buff.index)-1,'val'] = 0
else:
buff.at[len(buff.index)-1,'val'] = 1
del(samp)
df = df.append(buff, ignore_index=True)
del(buff)
print(str(round(trial/(maxTrials)*100, 2)) + "% Complete")
df = df.dropna(axis=0, how='any')
del(maxPeople)
del(people)
del(trial)
for n in range(plotStep,5):
dfCopy = df.loc[df.trial<=n]
dfCopy = dfCopy.groupby(['people'])['val'].mean().to_frame(name='prob').reset_index()
print(dfCopy)
plt.bar(dfCopy['people'],
dfCopy['prob'],
color='blue',
edgecolor='none',
width=0.5,
align='center')
plt.suptitle("Birthday Paradox\n")
plt.title("Based on "+str(n)+" simulations.")
plt.yticks([0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
plt.xlabel("Number of people in room")
plt.ylabel("Probability of one or more matching birthdays")
plt.savefig("bday_"+str(n)+".png", dpi=110, bbox_inches='tight')
A few initial saved plot from R look like this but python output looks like this I want to know if this is because of rounding error of some sort.

Answer 1

The code is just fine but you don't clear your axes, so it will add every run without clearing the last.
Adding plt.cla() after plt.savefig(...) will make it look much like the R output