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
"""


