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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. What country are most billionaires from? For the top ones, how many billionaires per billion people?
Step1:  2. What's the average wealth of a billionaire? Male? Female?
Step2:  3. Most common source of wealth? Male vs. female?
Step3:  4. List top ten billionaires
Step4:  5.Given the richest person in a country, what % of the GDP is their wealth?
Step5:  6.What are the most common industries for billionaires to come from? What's the total amount of billionaire money from each industry?
Step6:  7.How many self made billionaires vs. others?
Step7:  8.How old are billionaires? How old are billionaires self made vs. non self made? or different industries?
Step8:  9.Add up the wealth of all of the billionaires in a given country (or a few countries) and then compare it to the GDP of the country, so like pit the US vs India
Step9:  Compare the total wealth of billionaires in US to the GDP of the country, so like pit the US vs India¶
Step10:  10. List top 10 poorest billionaires. Who is the poorest billionare ?
Step11:  11. List ten youngest billionaires, list ten oldest billionaires, and plot and age distribution graph
Step12:  11b. Plot an age distribution graph
Step13:  12. What is relationship to company? And what are the most common relationships?
Step14:  13.Maybe just made a graph about how wealthy they are in general?
Step15:  14.Maybe plot their net worth vs age (scatterplot)
Step16:  15.Make a bar graph of the top 10 or 20 richest
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Python Code:
print("Most billionaires are from the following countries in descending order:")
df['countrycode'].value_counts().head(5)
us = 903 / 1000000000
ger = 160 / 1000000000
china = 153 / 1000000000
russia = 119 / 1000000000
japan = 96 / 1000000000
print("per billion for us is", us, "for germany is", ger, "for china is", china, "for russia is", russia, "for japan is", japan)
Explanation: 1. What country are most billionaires from? For the top ones, how many billionaires per billion people?
End of explanation
df['networthusbillion'].describe()
print("Average wealth of a billionaire is 3.531943")
male = df[df['gender'] == "male"]
male.head()
male['networthusbillion'].describe()
print("The average wealth of male billionaires is 3.516881")
female = df[df['gender'] == "female"]
female['networthusbillion'].describe()
print("The average wealth of female billionaires is 3.819277")
Explanation: 2. What's the average wealth of a billionaire? Male? Female?
End of explanation
print("Most common source of wealth are:")
df['sourceofwealth'].value_counts().head() 
 
print("Most common source of wealth for male billionaires are:")
male['sourceofwealth'].value_counts().head()
print("Most common source of wealth for female billionaires are:")
female['sourceofwealth'].value_counts().head()
Explanation: 3. Most common source of wealth? Male vs. female?
End of explanation
bill =  df.sort_values('networthusbillion', ascending=False).head(10)
df.sort_values('networthusbillion', ascending=False).head(10)
print("A precise list of billionaires, wealth and rank is given below:")
columns_want = bill[['name', 'rank', 'networthusbillion']]
columns_want
Explanation: 4. List top ten billionaires
End of explanation
us_gdp = 7419  
wealth_rich = 76 
percent = round((wealth_rich * 100) / us_gdp)
print(percent, "% of the US GDP is their wealth")
Explanation: 5.Given the richest person in a country, what % of the GDP is their wealth?
End of explanation
print("the most common industries for billionaires to come from are:")
df['industry'].value_counts()
columns_we_want = df[['name', 'networthusbillion', 'industry']]
columns_we_want
print("the total amount of billionaire money from each industry are given below:")
columns_we_want.groupby('industry').describe()
Explanation: 6.What are the most common industries for billionaires to come from? What's the total amount of billionaire money from each industry?
End of explanation
#columnswant = df[['name', 'networthusbillion', 'selfmade']]
#columnswant
print("The number of selfmade billionaires are:")
df['selfmade'].value_counts()
Explanation: 7.How many self made billionaires vs. others?
End of explanation
columns_want = df[['name', 'age', 'selfmade']]
columns_want.head(10)
columns_want = df[['name', 'age', 'industry']]
columns_want.head(10)
columns_want.sort_values('age', ascending=False)
Explanation: 8.How old are billionaires? How old are billionaires self made vs. non self made? or different industries?
End of explanation
is_in_us = df[df['countrycode'] == "USA"]
is_in_us['networthusbillion'].describe()
print("The total wealth of billionaires in US is 903")
Explanation: 9.Add up the wealth of all of the billionaires in a given country (or a few countries) and then compare it to the GDP of the country, so like pit the US vs India
End of explanation
gdp_india = 2066.90
us_bill_wealth = 903
percent = round((us_bill_wealth * 100) / gdp_india)
print(percent, "% of the India GDP is the wealth of US billionaires")
Explanation: Compare the total wealth of billionaires in US to the GDP of the country, so like pit the US vs India¶
End of explanation
df.sort_values('networthusbillion').head(10)
print("The poorest billionaire is")
df.sort_values('networthusbillion').head(1)
Explanation: 10. List top 10 poorest billionaires. Who is the poorest billionare ?
End of explanation
print("The ten youngest billionaires are: ")
df.sort_values('age').head(10)
print("The ten oldest billionaires are: ")
df.sort_values('age', ascending=False).head(10)
columns_want = df[['name', 'age', 'industry']]
columns_want.sort_values('age', ascending=False).head(10)
Explanation: 11. List ten youngest billionaires, list ten oldest billionaires, and plot and age distribution graph
End of explanation
import matplotlib.pyplot as plt
%matplotlib inline
df.plot(kind='scatter', x='age', y='networthusbillion')
Explanation: 11b. Plot an age distribution graph
End of explanation
print("The most common relationships are:")
df['relationshiptocompany'].value_counts().head() 
 
print("Relationship to a company is describes the billionaire's relationship to the company primarily responsible for their wealth, such as founder, executive, relation, or shareholder")
Explanation: 12. What is relationship to company? And what are the most common relationships?
End of explanation
columnswant
sort_df = df.sort_values('networthusbillion')
sort_df.plot(kind='line', x='rank', y='networthusbillion')
df.plot(kind='bar', x='name', y='networthusbillion')
Explanation: 13.Maybe just made a graph about how wealthy they are in general?
End of explanation
df.plot(kind='scatter', x='age', y='networthusbillion')
Explanation: 14.Maybe plot their net worth vs age (scatterplot)
End of explanation
df['networthusbillion'].head(10).plot(kind='bar', x='name', y='networthusbillion')
Explanation: 15.Make a bar graph of the top 10 or 20 richest
End of explanation
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