Midlige_McCownSubmission

Exercise5b.py

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+import pandas
+wages=pandas.read_csv("wages.csv")
+wages.drop([col for col in wages.columns if 'yearsSchool' in col],axis=1,inplace=True)
+cc=(wages.sort_values(by='wage'))
+print ("Lowest Earner")
+print (cc.head(n=1))
+print ("Highest Earner")
+print (cc.tail(n=1))
+print ("Top 10 Female Earners")
+numoffemales=wages[wages['wage']>=wages['wage'].nlargest(10).iloc[-1]]['gender'].eq('female').sum()
+print (numoffemales)

Exercise5c.py

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+import pandas
+wages=pandas.read_csv("wages.csv")
+ed12=wages[wages.yearsSchool==12] #12 years of education
+minimum12=min(ed12.wage) #minimum wage of 12
+ed16=wages[wages.yearsSchool==16] #16 years of education
+minimum16=min(ed16.wage) #minimum wage of 16
+print (minimum16-minimum12)
+

part1.py

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+import os
+
+with open('wages.csv','r') as f:
+	file = f.readlines()
+
+
+
+
+for line in file:
+	line = line.split(',')
+	gender = line[0]
+	exp = line[1]
+	str = gender + ' ' + exp + '\n'
+	with open('new.txt', 'a') as n:
+		n.write(str)
+