sampathkumar-mitchell submission#4
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lyy005
reviewed
Oct 5, 2017
| 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) | ||
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lyy005
reviewed
Oct 5, 2017
| 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) | ||
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lyy005
reviewed
Oct 5, 2017
| 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) | ||
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