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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 1. the youngest programmer vs. the oldest programmer?
Step1:  2. What are the maximum income for female programmers?
Step2:  3. how much does a programmer make on average per year?
Step3:  4. what is the most common major programmers studied?
Step4:  5.what is the highest degree have these programmers completed?
Step5:  6. rank programmer with the amount of debt
Step6:  7. Show programmers are taking loans to attend bootcamp and have full-time job after attending bootcamp?
Step7:  8. How many programmers are citizens of United States of America and are ethnic minority?
Step8:  9.programmers from which country earn the most?
Step9:  10. How many programmers found jobs after graduating from bootcamp less and equal than 4 months?
Step10:  this is a bar chart of programmers' age
Step11:  change the style of the previous plot into fivethirtyeight
Step12:  customize the chart by changing bin size, xlabelsize, ylabelsize and range
Step13:  Make a graph that shows the amount of student debt the first 10 programmes from the dataframe own?
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Python Code:
df['Age'].describe()
Explanation: 1. the youngest programmer vs. the oldest programmer?
End of explanation
df.groupby('Gender')['Income'].describe()
Explanation: 2. What are the maximum income for female programmers?
End of explanation
df['Income'].describe()
Explanation: 3. how much does a programmer make on average per year?
End of explanation
df['SchoolMajor'].value_counts()
Explanation: 4. what is the most common major programmers studied?
End of explanation
df['SchoolDegree'].value_counts()
Explanation: 5.what is the highest degree have these programmers completed?
End of explanation
df.sort_values(by='StudentDebtOwe', ascending=False).head()
Explanation: 6. rank programmer with the amount of debt
End of explanation
df[(df['BootcampFullJobAfter']==1) & (df['BootcampLoanYesNo']==1)].head()
Explanation: 7. Show programmers are taking loans to attend bootcamp and have full-time job after attending bootcamp?
End of explanation
df[(df['IsEthnicMinority']==1)& (df['CountryCitizen']=='United States of America')]
Explanation: 8. How many programmers are citizens of United States of America and are ethnic minority?
End of explanation
df.groupby('CountryCitizen')['Income'].sum().sort_values(ascending=False)
Explanation: 9.programmers from which country earn the most?
End of explanation
df['BootcampMonthsAgo'].value_counts()
df[(df['EmploymentStatus']== 'Employed for wages')& (df['BootcampMonthsAgo'] <=4)].head()
Explanation: 10. How many programmers found jobs after graduating from bootcamp less and equal than 4 months?
End of explanation
df['Age'].hist()
plt.style.available
Explanation: this is a bar chart of programmers' age
End of explanation
plt.style.use('fivethirtyeight')
df['Age'].hist()
Explanation: change the style of the previous plot into fivethirtyeight
End of explanation
df['Age'].hist(bins=20, xlabelsize=10, ylabelsize=10, range=(0,50))
Explanation: customize the chart by changing bin size, xlabelsize, ylabelsize and range
End of explanation
plt.style.use('fivethirtyeight')
df['StudentDebtOwe'].head(10).plot(kind='bar')
Explanation: Make a graph that shows the amount of student debt the first 10 programmes from the dataframe own?
End of explanation
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