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How to use percentage method in Kiwi

Best Python code snippet using Kiwi_python

paint.py

Source:paint.py

1# encoding: utf-82import numpy as np3import matplotlib.pyplot as plt4import sys5from scipy import stats6from matplotlib import rcParams7rcParams.update({'font.size': 10,'font.weight':'bold'})8patterns = ('/','//','-', '+', 'x', '\\', '\\\\', '*', 'o', 'O', '.')9##first read from file10pFabric="pFabric"11Varys="Varys"12Barrat="Barrat"13Yosemite="Yosemite"14Fair="FAIR"15DARK='DARK'16SHORT=20*1024*1024*102417NARROW=14018def getAverage(arralylist):19	return np.mean(arralylist)20def getRange(arraylist,element):21	return stats.percentileofscore(arraylist, element)22def getElements(arraylist,percentage):23	result=[]24	for element in arraylist:25		pos=getRange(arraylist,element)26		if pos <= percentage:27			result.append(element)28	return result29def getPercentageResult(path,percentage):30	f=open(path,"r")31	totaline=f.readlines()32	wc1=[]33	wc2=[]34	wc3=[]35	wc4=[]36	wc=[]37	for line in totaline:38		if line[0]=='J':39			arrayline=line.split()40			#analyze job 41			jobname=arrayline[0]42			starttime=float(arrayline[1])43			finishtime=float(arrayline[2])44			mappers=int(arrayline[3])45			reducers=int(arrayline[4])46			totalshuffle=float(arrayline[5])47			maxshuffle=float(arrayline[6])48			duration=float(arrayline[7])49			deadlineduration=float(arrayline[8])50			shufflesum=float(arrayline[9])51			weight=float(arrayline[10])52			width=mappers53			if mappers < reducers:54				width=reducers55			else:56				width=mappers57			if maxshuffle < SHORT and width < NARROW:58				wc1.append(weight*duration)59				60			elif maxshuffle >= SHORT and width < NARROW:61				wc2.append(weight*duration)62			elif maxshuffle < SHORT and width > NARROW:63				wc3.append(weight*duration)64			else:65				wc4.append(weight*duration)66				67	#wc=wc1+wc2+wc3+wc468	f.close()69	wc1add=070	wc2add=071	wc3add=072	wc4add=073	wcadd=074	wc1=getElements(wc1,percentage)75	wc2=getElements(wc2,percentage)76	wc3=getElements(wc3,percentage)77	wc4=getElements(wc4,percentage)78	for element in wc1:79		wc1add+=element80	for element in wc2:81		wc2add+=element82	for element in wc3:83		wc3add+=element84	for element in wc4:85		wc4add+=element86	return [wc1add,wc2add,wc3add,wc4add,wc1add+wc2add+wc3add+wc4add]87	88def getPercentile(arraylist,percentage):89	a=np.array(arraylist)90	p=np.percentile(a,percentage)91	return p92def getWcResult(path):93	f=open(path,"r")94	totaline=f.readlines()95	wc=[]96	for line in totaline:97		if line[0]=='J':98			arrayline=line.split()99			#analyze job 100			jobname=arrayline[0]101			starttime=float(arrayline[1])102			finishtime=float(arrayline[2])103			mappers=int(arrayline[3])104			reducers=int(arrayline[4])105			totalshuffle=float(arrayline[5])106			maxshuffle=float(arrayline[6])107			duration=float(arrayline[7])108			deadlineduration=float(arrayline[8])109			shufflesum=float(arrayline[9])110			weight=float(arrayline[10])111			width=mappers112			wc.append(weight*duration/1000)113	f.close()114	return wc115def getResult(path):116		f=open(path,"r")117		totaline=f.readlines()118		bin1=0119		bin2=0120		bin3=0121		bin4=0122		wc1=0123		wc2=0124		wc3=0125		wc4=0126		wc=0127		for line in totaline:128			if line[0]=='J':129				arrayline=line.split()130				#analyze job 131				jobname=arrayline[0]132				starttime=float(arrayline[1])133				finishtime=float(arrayline[2])134				mappers=int(arrayline[3])135				reducers=int(arrayline[4])136				totalshuffle=float(arrayline[5])137				maxshuffle=float(arrayline[6])138				duration=float(arrayline[7])139				deadlineduration=float(arrayline[8])140				shufflesum=float(arrayline[9])141				weight=float(arrayline[10])142				width=mappers143				if mappers < reducers:144					width=reducers145				else:146					width=mappers147				if maxshuffle < SHORT and width < NARROW:148					wc1+=weight*duration149					bin1+=1150				elif maxshuffle >= SHORT and width < NARROW:151					wc2+=weight*duration152					bin2+=1153				elif maxshuffle < SHORT and width > NARROW:154					wc3+=weight*duration155					bin3+=1156				else:157					wc4+=weight*duration158					bin4+=1159		wc=wc1+wc2+wc3+wc4160		f.close()161		return [wc1,wc2,wc3,wc4,wc]162	163def frac(v,x):164	n=0165	for i in v:166		if i<x:167			n=n+1168	return float(n)/float(len(v))169if __name__=='__main__':170	Barratwc=getResult(Barrat)171	Varyswc=getResult(Varys)172	Yosemitewc=getResult(Yosemite)173	pFabricwc=getResult(pFabric)174	Fairwc=getResult(Fair)175	Darkwc=getResult(DARK)176	177	VarysResult=[]178	YosemiteResult=[]179	BarratResult=[]180	pFabricResult=[]181	FairResult=[]182	DarkResult=[]183	percentageVaryswc=getPercentageResult(Varys,95)184	percentageYosemitewc=getPercentageResult(Yosemite,95)185	percentageBarratwc=getPercentageResult(Barrat,95)186	percentagepFabricwc=getPercentageResult(pFabric,95)187	percentageFairwc=getPercentageResult(Fair,95)188	percentageDarkwc=getPercentageResult(DARK,95)189	percentageVarysResult=[]190	percentageYosemiteResult=[]191	percentageBarratResult=[]192	percentagepFabricResult=[]193	percentageFairResult=[]194	percentageDarkResult=[]195	for i in range(0,5):196		VarysResult.append(percentageFairwc[i]/Varyswc[i])197		percentageVarysResult.append(percentageFairwc[i]/percentageVaryswc[i])198		YosemiteResult.append(percentageFairwc[i]/Yosemitewc[i])199		percentageYosemiteResult.append(percentageFairwc[i]/percentageYosemitewc[i])200		BarratResult.append(percentageFairwc[i]/Barratwc[i])201		percentageBarratResult.append(percentageFairwc[i]/percentageBarratwc[i])202		DarkResult.append(percentageFairwc[i]/Darkwc[i])203		percentageDarkResult.append(percentageFairwc[i]/percentageDarkwc[i])204	N=5205	ind = np.arange(N)  # the x locations for the groups206	width = 0.1       # the width of the bars207	fig, ax = plt.subplots(figsize=(12,6))208	rects1 = ax.bar(ind, BarratResult, width, hatch="+",color='r',ecolor='k')209	rects2 = ax.bar(ind+width, DarkResult, width, hatch="+",color='g',ecolor='k')210	rects3 = ax.bar(ind+2*width, VarysResult, width, hatch='-',color='white',ecolor='k')211	rects4 = ax.bar(ind+3*width, YosemiteResult, width, hatch='+',color='k',ecolor='k')212	rects5 = ax.bar(ind+4*width, percentageBarratResult, width, hatch="+",color='#FF7256',ecolor='k')213	rects6 = ax.bar(ind+5*width, percentageDarkResult, width, hatch="+",color='#00FF00',ecolor='k')214	rects7 = ax.bar(ind+6*width, percentageVarysResult, width, hatch='-',color='#EEE9E9',ecolor='k')215	rects8=ax.bar(ind+7*width, percentageYosemiteResult, width, hatch='+',color='#696969',ecolor='k')216	ax.set_xticks(ind+width)217	ax.set_xticklabels(('SHORT & NARROW','LONG & NARROW','SHORT & WIDTH','LONG & WIDTH','ALL'))218	ax.legend((rects1[0],rects2[0],rects3[0],rects4[0],rects5[0],rects6[0],rects7[0],rects8[0]), ('Barrat','Aalo','Vary','Yosemite','Barrat(95th)','Aalo(95th)','Vary(95th)','Yosemite(95th)'),loc=0)219	ax.set_ylabel('Factor of Improvement',fontsize=12,fontweight='bold')220	ax.set_ylim([0,5])221	ax.set_xlabel('coflow types',fontsize=12,fontweight='bold')222	#plt.figure(figsize=(12,3))223	#plt.show()224	fig.savefig("weight_real_type.eps")225	fig, ax = plt.subplots(figsize=(4.5,6))226	x = np.linspace(0, 1000, 10)227	Yosemitewc=getWcResult(Yosemite)228	Varyswc=getWcResult(Varys)229	Fairwc=getWcResult(Fair)230	pFabricwc=getWcResult(pFabric)231	Barratwc=getWcResult(Barrat)232	Darkwc=getWcResult(DARK)233	YosemiteCDF=[]234	VarysCDF=[]235	FairCDF=[]236	pFabricCDF=[]237	BarratCDF=[]238	AaloCDF=[]239	for v in x:240		YosemiteCDF.append(frac(Yosemitewc,v))241		VarysCDF.append(frac(Varyswc,v))242		FairCDF.append(frac(Fairwc,v))243		pFabricCDF.append(frac(pFabricwc,v))244		BarratCDF.append(frac(Barratwc,v))245		AaloCDF.append(frac(Darkwc,v))246	ax.plot(x, BarratCDF,linewidth=3,color='b',label='Aalo')247	ax.plot(x, FairCDF,linewidth=3,color='r',label='Fair')248	ax.plot(x, AaloCDF,linewidth=3,color='g',label='Barrat')249	ax.plot(x, YosemiteCDF,linewidth=3,color='k',label='Yosemite')250	251	ax.legend(loc='lower right')252	plt.ylabel('CDF',fontsize=12,fontweight='bold')253	plt.xlabel('weight completion time(s)',fontsize=12,fontweight='bold')254	plt.show()...

grid.default.config.js

Source:grid.default.config.js

1//TODO: What is this file? Is it used?? I don't think so2var uSkyGridConfig = [3{4    style:[5        {6            label: "Set a background image",7            description: "Set a row background",8            key: "background-image",9            view: "imagepicker",10            modifier: "url({0})"11        },12        {13            label: "Set a font color",14            description: "Pick a color",15            key: "color",16            view: "colorpicker"17        }18    ],19    config:[20        {21            label: "Preview",22            description: "Display a live preview",23            key: "preview",24            view: "boolean"25        },26        {27            label: "Class",28            description: "Set a css class",29            key: "class",30            view: "textstring"31        }32    ],33    layouts: [34    {35        grid: 12,36        percentage: 100,37        rows: [38        {39            name: "Single column",40                columns: [{41                    grid: 12,42                    percentage: 10043                }]44        },45        {46            name: "Article",47                models: [{48                    grid: 4,49                    percentage: 33.3,50                    allowed: ["media","quote"]51                }, {52                    grid: 8,53                    percentage: 66.6,54                    allowed: ["rte"]55                }]56        },57        {58         name: "Article, reverse",59         models: [60         {61          grid: 8,62          percentage: 66.6,63          allowed: ["rte","macro"]64      },65      {66         grid: 4,67         percentage: 33.3,68         allowed: ["media","quote","embed"]69     }]70 },71 {72     name: "Profile page",73     models: [74     {75         grid: 4,76         percentage: 33.3,77         allowed: ["media"]78     },79     {80      grid: 8,81      percentage: 66.6,82      allowed: ["rte"]83  }84  ]85},86{87 name: "Headline",88 models: [89 {90     grid: 12,91     percentage: 100,92     max: 1,93     allowed: ["headline"]94 }95 ]96},97{98    name: "Three columns",99    models: [{100        grid: 4,101        percentage: 33.3,102        allowed: ["rte"]103    },104    {105         grid: 4,106         percentage: 33.3,107         allowed: ["rte"]108    },109    {110        grid: 4,111        percentage: 33.3,112        allowed: ["rte"]113    }]114}115]116}117]118},119{120    columns: [121    {122        grid: 9,123        percentage: 70,124        cellModels: [125        {126            models: [{127                grid: 12,128                percentage: 100129            }]130        }, {131            models: [{132                grid: 6,133                percentage: 50134            }, {135                grid: 6,136                percentage: 50137            }]138        }, {139            models: [{140                grid: 4,141                percentage: 33.3142            }, {143                grid: 4,144                percentage: 33.3145            }, {146                grid: 4,147                percentage: 33.3148            }]149        }, {150            models: [{151                grid: 3,152                percentage: 25153            }, {154                grid: 3,155                percentage: 25156            }, {157                grid: 3,158                percentage: 25159            }, {160                grid: 3,161                percentage: 25162            }, ]163        }, {164            models: [{165                grid: 2,166                percentage: 16.6167            }, {168                grid: 2,169                percentage: 16.6170            }, {171                grid: 2,172                percentage: 16.6173            }, {174                grid: 2,175                percentage: 16.6176            }, {177                grid: 2,178                percentage: 16.6179            }, {180                grid: 2,181                percentage: 16.6182            }]183        }, {184            models: [{185                grid: 8,186                percentage: 60187            }, {188                grid: 4,189                percentage: 40190            }]191        }, {192            models: [{193                grid: 4,194                percentage: 40195            }, {196                grid: 8,197                percentage: 60198            }]199        }200        ]201    },202    {203        grid: 3,204        percentage: 30,205        cellModels: [206        {207            models: [{208                grid: 12,209                percentage: 100210            }]211        }212        ]213    }214    ]215},216{217    columns: [218    {219        grid: 3,220        percentage: 30,221        cellModels: [222        {223            models: [{224                grid: 12,225                percentage: 100226            }]227        }228        ]229    },230    {231        grid: 9,232        percentage: 70,233        cellModels: [234        {235            models: [{236                grid: 12,237                percentage: 100238            }]239        }, {240            models: [{241                grid: 6,242                percentage: 50243            }, {244                grid: 6,245                percentage: 50246            }]247        }, {248            models: [{249                grid: 4,250                percentage: 33.3251            }, {252                grid: 4,253                percentage: 33.3254            }, {255                grid: 4,256                percentage: 33.3257            }]258        }, {259            models: [{260                grid: 3,261                percentage: 25262            }, {263                grid: 3,264                percentage: 25265            }, {266                grid: 3,267                percentage: 25268            }, {269                grid: 3,270                percentage: 25271            }, ]272        }, {273            models: [{274                grid: 2,275                percentage: 16.6276            }, {277                grid: 2,278                percentage: 16.6279            }, {280                grid: 2,281                percentage: 16.6282            }, {283                grid: 2,284                percentage: 16.6285            }, {286                grid: 2,287                percentage: 16.6288            }, {289                grid: 2,290                percentage: 16.6291            }]292        }, {293            models: [{294                grid: 8,295                percentage: 60296            }, {297                grid: 4,298                percentage: 40299            }]300        }, {301            models: [{302                grid: 4,303                percentage: 40304            }, {305                grid: 8,306                percentage: 60307            }]308        }309        ]310    }311    ]312},313{314    columns: [315    {316        grid: 4,317        percentage: 33.3,318        cellModels: [319        {320            models: [{321                grid: 12,322                percentage: 100323            }]324        }325        ]326    },327    {328        grid: 4,329        percentage: 33.3,330        cellModels: [331        {332            models: [{333                grid: 12,334                percentage: 100335            }]336        }337        ]338    },339    {340        grid: 4,341        percentage: 33.3,342        cellModels: [343        {344            models: [{345                grid: 12,346                percentage: 100347            }]348        }349        ]350    }351    ]352}...

4_InstagramRanking.py

Source:4_InstagramRanking.py

1import pandas as pd2import numpy as np3df = pd.read_csv("usersInstagramPercentages.csv")4df1 =df.loc[df['micro']==1] #only micro influencers dataset to evaluate their percentiles5scores = []6for i in range(0,len(df['id'])):7  score=08  #score by followers9  if(5000<=df['followers'].iloc[i]<=df1['followers'].quantile(0.2)):10    score+=2.511  elif(df1['followers'].quantile(0.2)<df['followers'].iloc[i]<=df1['followers'].quantile(0.4)):12    score+=513  elif(df1['followers'].quantile(0.4)<df['followers'].iloc[i]<=df1['followers'].quantile(0.6)):14    score+=7.515  elif(df1['followers'].quantile(0.6)<df['followers'].iloc[i]<=df1['followers'].quantile(0.8)):16    score+=1017  elif(df1['followers'].quantile(0.8)<df['followers'].iloc[i]<=100000):18    score+=12.519  #score by followers following ratio20  if(2<=df['followers_following_ratio'].iloc[i]<=df1['followers_following_ratio'].quantile(0.2)):21    score+=2.522  elif(df1['followers_following_ratio'].quantile(0.2)<df['followers_following_ratio'].iloc[i]<=df1['followers_following_ratio'].quantile(0.4)):23    score+=524  elif(df1['followers_following_ratio'].quantile(0.4)<df['followers_following_ratio'].iloc[i]<=df1['followers_following_ratio'].quantile(0.6)):25    score+=7.526  elif(df1['followers_following_ratio'].quantile(0.6)<df['followers_following_ratio'].iloc[i]<=df1['followers_following_ratio'].quantile(0.8)):27    score+=1028  elif(df['followers_following_ratio'].iloc[i]>df1['followers_following_ratio'].quantile(0.8)):29    score+=12.530  #score by followers per media31  if(2<=df['followers_per_media'].iloc[i]<=df1['followers_per_media'].quantile(0.2)):32    score+=2.533  elif(df1['followers_per_media'].quantile(0.2)<df['followers_per_media'].iloc[i]<=df1['followers_per_media'].quantile(0.4)):34    score+=535  elif(df1['followers_per_media'].quantile(0.4)<df['followers_per_media'].iloc[i]<=df1['followers_per_media'].quantile(0.6)):36    score+=7.537  elif(df1['followers_per_media'].quantile(0.6)<df['followers_per_media'].iloc[i]<=df1['followers_per_media'].quantile(0.8)):38    score+=1039  elif(df['followers_per_media'].iloc[i]>df1['followers_per_media'].quantile(0.8)):40    score+=12.541  #score by interactions42  if(0<=df['interactions'].iloc[i]<=df1['interactions'].quantile(0.2)):43    score+=2.544  elif(df1['interactions'].quantile(0.2)<df['interactions'].iloc[i]<=df1['interactions'].quantile(0.4)):45    score+=546  elif(df1['interactions'].quantile(0.4)<df['interactions'].iloc[i]<=df1['interactions'].quantile(0.6)):47    score+=7.548  elif(df1['interactions'].quantile(0.6)<df['interactions'].iloc[i]<=df1['interactions'].quantile(0.8)):49    score+=1050  elif(df['interactions'].iloc[i]>df1['interactions'].quantile(0.8)):51    score+=12.552  #score by topic % in captions53  if(0<=df['topicInCaptionsPercentage'].iloc[i]<=df1['topicInCaptionsPercentage'].quantile(0.2)):54    score+=2.555  elif(df1['topicInCaptionsPercentage'].quantile(0.2)<df['topicInCaptionsPercentage'].iloc[i]<=df1['topicInCaptionsPercentage'].quantile(0.4)):56    score+=557  elif(df1['topicInCaptionsPercentage'].quantile(0.4)<df['topicInCaptionsPercentage'].iloc[i]<=df1['topicInCaptionsPercentage'].quantile(0.6)):58    score+=7.559  elif(df1['topicInCaptionsPercentage'].quantile(0.6)<df['topicInCaptionsPercentage'].iloc[i]<=df1['topicInCaptionsPercentage'].quantile(0.8)):60    score+=1061  elif(df['topicInCaptionsPercentage'].iloc[i]>df1['topicInCaptionsPercentage'].quantile(0.8)):62    score+=12.563  #score by topic % in words64  if(0<=df['topicInWordsPercentage'].iloc[i]<=df1['topicInWordsPercentage'].quantile(0.2)):65    score+=2.566  elif(df1['topicInWordsPercentage'].quantile(0.2)<df['topicInWordsPercentage'].iloc[i]<=df1['topicInWordsPercentage'].quantile(0.4)):67    score+=568  elif(df1['topicInWordsPercentage'].quantile(0.4)<df['topicInWordsPercentage'].iloc[i]<=df1['topicInWordsPercentage'].quantile(0.6)):69    score+=7.570  elif(df1['topicInWordsPercentage'].quantile(0.6)<df['topicInWordsPercentage'].iloc[i]<=df1['topicInWordsPercentage'].quantile(0.8)):71    score+=1072  elif(df['topicInWordsPercentage'].iloc[i]>df1['topicInWordsPercentage'].quantile(0.8)):73    score+=12.574  #score by topic % in pics75  if(0<=df['topicInPicsPercentage'].iloc[i]<=df1['topicInPicsPercentage'].quantile(0.92)):76    score+=2.577  elif(df1['topicInPicsPercentage'].quantile(0.92)<df['topicInPicsPercentage'].iloc[i]<=df1['topicInPicsPercentage'].quantile(0.94)):78    score+=579  elif(df1['topicInPicsPercentage'].quantile(0.94)<df['topicInPicsPercentage'].iloc[i]<=df1['topicInPicsPercentage'].quantile(0.96)):80    score+=7.581  elif(df1['topicInPicsPercentage'].quantile(0.96)<df['topicInPicsPercentage'].iloc[i]<=df1['topicInPicsPercentage'].quantile(0.98)):82    score+=1083  elif(df['topicInPicsPercentage'].iloc[i]>df1['topicInPicsPercentage'].quantile(0.98)):84    score+=12.585  #score by topic % in pics86  if(0<=df['topicInPicsWordsPercentage'].iloc[i]<=df1['topicInPicsWordsPercentage'].quantile(0.92)):87    score+=2.588  elif(df1['topicInPicsWordsPercentage'].quantile(0.92)<df['topicInPicsWordsPercentage'].iloc[i]<=df1['topicInPicsWordsPercentage'].quantile(0.94)):89    score+=590  elif(df1['topicInPicsWordsPercentage'].quantile(0.94)<df['topicInPicsWordsPercentage'].iloc[i]<=df1['topicInPicsWordsPercentage'].quantile(0.96)):91    score+=7.592  elif(df1['topicInPicsWordsPercentage'].quantile(0.96)<df['topicInPicsWordsPercentage'].iloc[i]<=df1['topicInPicsWordsPercentage'].quantile(0.98)):93    score+=1094  elif(df['topicInPicsWordsPercentage'].iloc[i]>df1['topicInPicsWordsPercentage'].quantile(0.98)):95    score+=12.596  scores.append(score)97df['scores']= scores98half = df['scores'].quantile(0.5)99print(half)100df['microTopic'] = np.where(df['scores']>=half, 1, 0) #assign micrro topic to 1 if the score overcomes the 0.5 percentile of the scores' column101df.to_csv('usersInstagramMicroTopicCC.csv', encoding='UTF8',index=False)...

demographic_data_analyzer.py

Source:demographic_data_analyzer.py

1import pandas as pd2def calculate_demographic_data(print_data=True):3    # Read data from file4    df = pd.read_csv('adult.data.csv')5    # How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.6    race_count = df['race'].value_counts()7    # What is the average age of men?8    average_age_men = round(df[df['sex'] == 'Male']['age'].mean(), ndigits=1)9    # What is the percentage of people who have a Bachelor's degree?10    percentage_bachelors = round(((df[df['education'] == 'Bachelors'].shape[0] / df.shape[0]) * 100), ndigits=1)11    # What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?12    # What percentage of people without advanced education make more than 50K?13    14    # with and without `Bachelors`, `Masters`, or `Doctorate`15    higher_education = df[df['education'].isin(['Bachelors','Masters','Doctorate'])]16    17    lower_education = df[~df['education'].isin(['Bachelors','Masters','Doctorate'])]18    # percentage with salary >50K19    higher_education_rich = round(((higher_education[higher_education['salary'] == '>50K'].shape[0] / higher_education.shape[0]) * 100), ndigits=1)20    lower_education_rich = round(((lower_education[lower_education['salary'] == '>50K'].shape[0] / lower_education.shape[0]) * 100), ndigits=1)21    # What is the minimum number of hours a person works per week (hours-per-week feature)?22    min_work_hours = df['hours-per-week'].min()23    # What percentage of the people who work the minimum number of hours per week have a salary of >50K?24    num_min_workers = df[df['hours-per-week'] == min_work_hours]25    rich_percentage = round((num_min_workers[num_min_workers['salary'] == '>50K'].shape[0] / num_min_workers.shape[0] * 100), ndigits=1)26    # What country has the highest percentage of people that earn >50K?27    people = df['native-country'].value_counts()28    rich = df[df['salary'] == '>50K']['native-country'].value_counts()29    highest_earning_country = (rich / people).sort_values(ascending=False).keys()[0]30    31    people_in_highest = df[df['native-country'] == highest_earning_country]32    rich_in_highest = people_in_highest[people_in_highest['salary'] == '>50K']33    highest_earning_country_percentage = round((rich_in_highest.shape[0] / people_in_highest.shape[0] * 100), ndigits=1)34    # Identify the most popular occupation for those who earn >50K in India.35    top_IN_occupation = df[df['salary'] == '>50K']36    top_IN_occupation = top_IN_occupation[top_IN_occupation['native-country'] == 'India']37    top_IN_occupation = top_IN_occupation['occupation'].value_counts()._index[0]38    # DO NOT MODIFY BELOW THIS LINE39    if print_data:40        print("Number of each race:\n", race_count) 41        print("Average age of men:", average_age_men)42        print(f"Percentage with Bachelors degrees: {percentage_bachelors}%")43        print(f"Percentage with higher education that earn >50K: {higher_education_rich}%")44        print(f"Percentage without higher education that earn >50K: {lower_education_rich}%")45        print(f"Min work time: {min_work_hours} hours/week")46        print(f"Percentage of rich among those who work fewest hours: {rich_percentage}%")47        print("Country with highest percentage of rich:", highest_earning_country)48        print(f"Highest percentage of rich people in country: {highest_earning_country_percentage}%")49        print("Top occupations in India:", top_IN_occupation)50    return {51        'race_count': race_count,52        'average_age_men': average_age_men,53        'percentage_bachelors': percentage_bachelors,54        'higher_education_rich': higher_education_rich,55        'lower_education_rich': lower_education_rich,56        'min_work_hours': min_work_hours,57        'rich_percentage': rich_percentage,58        'highest_earning_country': highest_earning_country,59        'highest_earning_country_percentage':60        highest_earning_country_percentage,61        'top_IN_occupation': top_IN_occupation...

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