Bruzzese Inskeep Nemera Submission for exercise 5

exercise5 script.py

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+###########question 1################
+#load the dataset
+import pandas
+wages = pandas.read_csv("wages.csv")
+
+#filter out columns of interest into a new dataframe
+wages2 = wages[['gender', 'yearsExperience']].copy()
+
+#drop duplicates from column 1 and 2
+dwage=wages2.drop_duplicates(subset=['gender','yearsExperience'])
+
+#sort columns
+swage= dwage.sort_values(by=["gender", "yearsExperience"])
+
+#write.csv
+swage.to_csv('unique_gender_yearsExperience.csv', sep='\t')
+
+
+####################question 2#################
+#Limit to columns of interest
+q2 = wages[['gender', 'yearsExperience', 'wage']].copy()
+#sort by wage
+sortedq2 = q2.sort_values(by=['wage'])
+#select highest earner
+Highest = sortedq2.tail(1)
+#Print line for highest earner
+print (Highest)
+#select lowest earner
+Lowest = sortedq2.head(1)
+#Print line for lowest earner
+print (Lowest)
+#Find top 10 earners
+Top10 = sortedq2.tail(10)
+
+##Count number of females
+#pulls out females from top 10
+Topfemales=Top10.loc[Top10['gender'] == "female"]
+#counts females in top 10
+ctopfemale= Topfemales.gender.count()
+print (ctopfemale)
+
+############question 3##############
+wages12 = wages[wages.yearsSchool==12] #selects people with 12 years of school
+min12 = min(wages12.wage) #minimum wage of people with 12 years of school
+wages16 = wages[wages.yearsSchool==16] #selects people with 16 years of school
+min16 = min(wages16.wage) #minimum wage of people with 16 years of school
+print(min16-min12)