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chambers-yamasaki submission #5

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bfae0ae
finished part 1 of question 2_Highest earner
jchambe5 Sep 25, 2017
e687da7
question 2 is finished_JC
jchambe5 Sep 26, 2017
8e8b7d4
JC attempt at question 1 completed. probably not the most efficient
jchambe5 Sep 28, 2017
0850615
AEY: Start Python script to sort gender-yearsExperience pairs in wage…
ayamasaki2011 Sep 28, 2017
70caa3d
Modify script to sort gender and years experience first by gender, th…
ayamasaki2011 Sep 28, 2017
6e06ab7
Modify script to drop duplicate pairs of gender/experience
ayamasaki2011 Sep 28, 2017
0a4e5f0
Modify script to calculate effect of graduating college on minium wage
ayamasaki2011 Sep 28, 2017
3e0a57a
AEY: Add file for combined answers
ayamasaki2011 Sep 28, 2017
61dc49c
AEY: Combine separate Python scripts into single file
ayamasaki2011 Sep 28, 2017
6c0397b
exercise 5 completed and combined
jchambe5 Sep 29, 2017
ffeb8c9
Merge branch 'master' of https://github.com/ayamasaki2011/Intro_Bioco…
jchambe5 Sep 29, 2017
a1384e6
fixed an issue printing with question 2
jchambe5 Sep 29, 2017
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26 changes: 26 additions & 0 deletions AEY_Part1.py
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import pandas

wages = pandas.read_csv("wages.csv")

gender_Experience=wages[["gender","yearsExperience"]]

ordered_gender_Experience=gender_Experience.sort(['gender','yearsExperience'], ascending=[True,True])

unique_combos=ordered_gender_Experience.drop_duplicates()

print("Unique combinations of gender/years experience:", unique_combos)

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)

minWage_HS = ordered_wages_HS.iloc[0,3]

minWage_C = ordered_wages_C.iloc[0,3]

print("Effect of graduating college on minimum wage:", minWage_C - minWage_HS)

59 changes: 59 additions & 0 deletions Exercise5_AEY_JC.py
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import pandas
print("Question1")
#Question 1 Part 1
wages=pandas.read_csv("wages.csv",header=0,sep=",")
reduced=wages[['gender', 'yearsExperience']]
sorted1=reduced.sort_values(['gender', 'yearsExperience'], ascending=[True, False])
done=sorted1.drop_duplicates()
print(done)

#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])

@lyy005 lyy005 Oct 5, 2017

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"gender_Experience.sort" should be "gender_Experience.sort_values"

unique_combos=ordered_gender_Experience.drop_duplicates()
print("Unique combinations of gender/years experience:", unique_combos)

print("Question2")
# Question 2 part 1
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.drop('yearsSchool', axis=1, inplace=True)

@lyy005 lyy005 Oct 5, 2017 •
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What is the variable "sorted1" for?

sorted2=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")
part1=duplicates.tail(1)
print("Highest earner", part1)

# Question 2 part 2
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.drop('yearsSchool', axis=1, inplace=True)
sorted2=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")
part2=duplicates.head(1)
print("Lowest earner", part2)

# Question 2 part 3
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")

@lyy005 lyy005 Oct 5, 2017

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Don't need to drop_duplicates for this question

top10=duplicates.tail(10)
top10.to_csv("top10.csv")
total = 0
with open('top10.csv') as f:
for line in f:
finded = line.find('female')
if finded != -1 and finded != 0:
total += 1
print("Number of women in the top 10 earners", total)

@lyy005 lyy005 Oct 5, 2017

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Or you can just use:
femalesInTopTen=sum(top10.gender=="female")


print("Question3")
#Question 3
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)

@lyy005 lyy005 Oct 5, 2017 •
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wages_college.sort -> wages_college.sort_values

-0.25

minWage_HS = ordered_wages_HS.iloc[0,3]
minWage_C= ordered_wages_C.iloc[0,3]
print("Effect of graduating college on minimum wage:", minWage_C - minWage_HS)
25 changes: 25 additions & 0 deletions earnings.py
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import pandas

#Part 1
wages=pandas.read_csv("wages.csv",header=0,sep=",")
reduced=wages[['gender', 'yearsExperience']]
sorted1=reduced.sort_values(['gender', 'yearsExperience'], ascending=[True, False])
done=sorted1.drop_duplicates()
print(done)

#Part 2
wages = pandas.read_csv("wages.csv")
gender_Experience=wages[["gender","yearsExperience"]]
ordered_gender_Experience=gender_Experience.sort(['gender','yearsExperience'], ascending=[True,True])
unique_combos=ordered_gender_Experience.drop_duplicates()
print("Unique combinations of gender/years experience:", unique_combos)

#Part 3
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)
minWage_HS = ordered_wages_HS.iloc[0,3]
minWage_C = ordered_wages_C.iloc[0,3]
print("Effect of graduating college on minimum wage:", minWage_C - minWage_HS)

8 changes: 8 additions & 0 deletions q2part1.py
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print("Highest earner")
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.drop('yearsSchool', axis=1, inplace=True)
sorted2=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")
duplicates.tail(1)

10 changes: 10 additions & 0 deletions question1.py
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#import pandas. read the csv. read the two columns. sort by both columns in order. drop duplicates. print.
import pandas
wages=pandas.read_csv("wages.csv",header=0,sep=",")
reduced=wages[['gender', 'yearsExperience']]
sorted1=reduced.sort_values(['gender', 'yearsExperience'], ascending=[True, False])
done=sorted1.drop_duplicates()
print(done)



33 changes: 33 additions & 0 deletions question2.py
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# Question 2 part 1
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.drop('yearsSchool', axis=1, inplace=True)
sorted2=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")
part1=duplicates.tail(1)
print("Highest earner", part1)

# Question 2 part 2
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.drop('yearsSchool', axis=1, inplace=True)
sorted2=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")
part2=duplicates.head(1)
print("Lowest earner", part2)

# Question 2 part 3
import pandas
ex5data=pandas.read_csv("wages.csv")
sorted1=ex5data.sort_values("wage")
duplicates=sorted2.drop_duplicates("wage")
top10=duplicates.tail(10)
top10.to_csv("top10.csv")
total = 0
with open('top10.csv') as f:
for line in f:
finded = line.find('female')
if finded != -1 and finded != 0:
total += 1
print("Number of women in the top 10 earners", total)