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+import numpy
+import os
+os.listdir('.')
+os.chdir('/Users/sampathkumarbalaji/EX_5/Intro_Biocom_ND_319_Tutorial5')
+import pandas
+wages_csv=pandas.read_csv("wages.csv")
+col_unq_sort_gen_yrs = (wages_csv.iloc[:,0:2]).drop_duplicates(keep='first').sort_values(by='gender', ascending=True).sort_values(by= 'yearsExperience', ascending=True)
+col_unq_sort_gen_yrs.to_csv('ex5.csv', sep=' ', index=False)
+
+print ('Highest earner:')
+earner_H = wages_csv.sort_values(by='wage', ascending=False).iloc[0:1, [0,1,3]]
+print (earner_H.to_string(index=False))
+print ('Lowest earner:')
+earner_L = wages_csv.sort_values(by='wage', ascending=True).iloc[0:1, [0,1,3]]
+print (earner_L.to_string(index=False))
+print ('Women in Top 10 Earners:')
+earner_top10_w = wages_csv.sort_values(by='wage', ascending=False).iloc[0:11, [0,1,3]]
+earner_number_of_women = (earner_top10_w[earner_top10_w.gender=="female"]).shape[0]
+print (earner_number_of_women)
+
+print('Effect - Graduating College on Minimum Wage:')
+min_wages_12 = (wages_csv[wages_csv.yearsSchool==12]).sort_values(by='wage', ascending=True).iloc[0:1, 3]
+min_wages_16 = (wages_csv[wages_csv.yearsSchool==16]).sort_values(by='wage', ascending=True).iloc[0:1, 3]
+print (min_wages_16.values - min_wages_12.values)
+