In [5]:

    

import pandas as pd


    

In [6]:

    

df = pd.read_excel("Billionaire Characteristics Database/Billionaires1996,2001and2014updated.xlsx")


    

In [7]:

    

df.head()


    

    
Out[7]:






  

    

      

      
year
      
name
      
rank
      
citizenship
      
countrycode
      
networthusbillion
      
selfmade
      
typeofwealth
      
gender
      
age
      
...
      
relationshiptocompany
      
foundingdate
      
gdpcurrentus
      
sourceofwealth
      
notes
      
notes2
      
source
      
source_2
      
source_3
      
source_4
    
  
  

    

      
0
      
2001
      
A Jerrold Perenchio
      
151
      
United States
      
USA
      
3.0
      
self-made
      
executive
      
male
      
70.0
      
...
      
former chairman and CEO
      
1955.0
      
1.062180e+13
      
NaN
      
represented Marlon Brando and Elizabeth Taylor
      
NaN
      
http://en.wikipedia.org/wiki/Jerry_Perenchio
      
http://www.forbes.com/profile/a-jerrold-perenc...
      
COLUMN ONE; A Hollywood Player Who Owns the Ga...
      
NaN
    
    

      
1
      
2014
      
A. Jerrold Perenchio
      
663
      
United States
      
USA
      
2.6
      
self-made
      
executive
      
male
      
83.0
      
...
      
former chairman and CEO
      
1955.0
      
NaN
      
television, Univision
      
represented Marlon Brando and Elizabeth Taylor
      
NaN
      
http://en.wikipedia.org/wiki/Jerry_Perenchio
      
http://www.forbes.com/profile/a-jerrold-perenc...
      
COLUMN ONE; A Hollywood Player Who Owns the Ga...
      
NaN
    
    

      
2
      
2001
      
Abdul Al Rahman  Al Jeraisy
      
336
      
Saudi Arabia
      
SAU
      
1.5
      
self-made
      
founder non-finance
      
male
      
NaN
      
...
      
founder
      
1956.0
      
1.830120e+11
      
NaN
      
NaN
      
NaN
      
http://www.jeraisy.com.sa/index.php/pages/rend...
      
NaN
      
NaN
      
NaN
    
    

      
3
      
2001
      
Abdul Aziz Al Ghurair
      
251
      
United Arab Emirates
      
ARE
      
1.9
      
inherited
      
inherited
      
male
      
47.0
      
...
      
relation
      
1960.0
      
1.030000e+11
      
NaN
      
inherited from father
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
4
      
1996
      
Abdul Aziz Al-Sulaiman
      
404
      
Saudi Arabia
      
SAU
      
1.0
      
self-made
      
self-made finance
      
male
      
0.0
      
...
      
founder
      
1968.0
      
1.577430e+11
      
NaN
      
NaN
      
NaN
      
http://www.arabianbusiness.com/arabian-busines...
      
NaN
      
NaN
      
NaN
    
  

5 rows × 30 columns

In [8]:

    

%matplotlib inline


    

In [10]:

    

df['age'].hist()


    

    
Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x2c2eed0>






    

In [11]:

    

import matplotlib.pyplot as plt


    

In [12]:

    

plt.style.available


    

    
Out[12]:





['grayscale',
 'seaborn-colorblind',
 'seaborn-dark',
 'seaborn-dark-palette',
 'seaborn-notebook',
 'classic',
 'seaborn-darkgrid',
 'bmh',
 'seaborn-poster',
 'seaborn-pastel',
 'dark_background',
 'ggplot',
 'seaborn-paper',
 'seaborn-ticks',
 'seaborn-muted',
 'seaborn-whitegrid',
 'seaborn-talk',
 'seaborn-bright',
 'seaborn-deep',
 'seaborn-white',
 'fivethirtyeight']

In [13]:

    

plt.style.use("bmh")


    

In [14]:

    

df['networthusbillion'].hist()


    

    
Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x61e2850>






    

In [ ]: