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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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