chambers-yamasaki submission#5
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lyy005
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Oct 5, 2017
| #Question 1 Part 2 | ||
| wages = pandas.read_csv("wages.csv") | ||
| gender_Experience=wages[["gender","yearsExperience"]] | ||
| ordered_gender_Experience=gender_Experience.sort(['gender','yearsExperience'], ascending=[True,True]) |
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"gender_Experience.sort" should be "gender_Experience.sort_values"
lyy005
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Oct 5, 2017
| # Question 2 part 1 | ||
| import pandas | ||
| ex5data=pandas.read_csv("wages.csv") | ||
| sorted1=ex5data.drop('yearsSchool', axis=1, inplace=True) |
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What is the variable "sorted1" for?
lyy005
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Oct 5, 2017
| finded = line.find('female') | ||
| if finded != -1 and finded != 0: | ||
| total += 1 | ||
| print("Number of women in the top 10 earners", total) |
Owner
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Or you can just use:
femalesInTopTen=sum(top10.gender=="female")
lyy005
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Oct 5, 2017
| import pandas | ||
| ex5data=pandas.read_csv("wages.csv") | ||
| sorted1=ex5data.sort_values("wage") | ||
| duplicates=sorted2.drop_duplicates("wage") |
Owner
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Don't need to drop_duplicates for this question
lyy005
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Oct 5, 2017
| wages_highschool = wages[wages.yearsSchool == 12] | ||
| wages_college = wages[wages.yearsSchool == 16] | ||
| ordered_wages_HS = wages_highschool.sort(columns='wage', ascending=True) | ||
| ordered_wages_C = wages_college.sort(columns='wage', ascending=True) |
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wages_college.sort -> wages_college.sort_values
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