In [107]:
import pandas as pd
In [108]:
import matplotlib.pyplot as plt
In [109]:
%matplotlib inline
In [18]:
df = pd.read_csv("07-hw-animals.csv")
print(df)
animal name length
0 cat Anne 35
1 cat Bob 45
2 dog Egglesburg 65
3 dog Devon 50
4 cat Charlie 32
5 dog Fontaine 35
In [10]:
print(df.columns.values)
['animal' 'name' 'length']
In [13]:
print(df['animal'])
0 cat
1 cat
2 dog
3 dog
4 cat
5 dog
Name: animal, dtype: object
In [19]:
print(df[:3])
animal name length
0 cat Anne 35
1 cat Bob 45
2 dog Egglesburg 65
In [27]:
print(df)
animal name length
0 cat Anne 35
1 cat Bob 45
2 dog Egglesburg 65
3 dog Devon 50
4 cat Charlie 32
5 dog Fontaine 35
In [32]:
print(df.sort_values(by='length', ascending=0)[:3])
animal name length
2 dog Egglesburg 65
3 dog Devon 50
1 cat Bob 45
In [45]:
print(df['animal'])
0 cat
1 cat
2 dog
3 dog
4 cat
5 dog
Name: animal, dtype: object
In [66]:
print(df['animal'].value_counts())
cat 3
dog 3
Name: animal, dtype: int64
In [77]:
dogs = df[df['animal'] == 'dog']
dogs
Out[77]:
animal
name
length
2
dog
Egglesburg
65
3
dog
Devon
50
5
dog
Fontaine
35
In [78]:
df[df['length'] > 40]
Out[78]:
animal
name
length
1
cat
Bob
45
2
dog
Egglesburg
65
3
dog
Devon
50
In [81]:
df['inches'] = df['length'] * .394
df
Out[81]:
animal
name
length
inches
0
cat
Anne
35
13.790
1
cat
Bob
45
17.730
2
dog
Egglesburg
65
25.610
3
dog
Devon
50
19.700
4
cat
Charlie
32
12.608
5
dog
Fontaine
35
13.790
In [90]:
cats = df[df['animal'] == 'cat']
cats
Out[90]:
animal
name
length
inches
0
cat
Anne
35
13.790
1
cat
Bob
45
17.730
4
cat
Charlie
32
12.608
In [91]:
dogs = df[df['animal'] == 'dog']
dogs
Out[91]:
animal
name
length
inches
2
dog
Egglesburg
65
25.61
3
dog
Devon
50
19.70
5
dog
Fontaine
35
13.79
In [92]:
cats[cats['inches'] > 12]
Out[92]:
animal
name
length
inches
0
cat
Anne
35
13.790
1
cat
Bob
45
17.730
4
cat
Charlie
32
12.608
In [99]:
df[df['inches'] > 12]
df[df['animal'] == 'cat']
#another way: df[(df['animal'] == 'cat') & (df['inches'] > 12)]
Out[99]:
animal
name
length
inches
0
cat
Anne
35
13.790
1
cat
Bob
45
17.730
4
cat
Charlie
32
12.608
In [103]:
cats['length'].mean()
#see also: cats['length'].describe()
Out[103]:
37.333333333333336
In [104]:
dogs['length'].mean()
Out[104]:
50.0
In [105]:
df.groupby('animal')['length'].mean()
Out[105]:
animal
cat 37.333333
dog 50.000000
Name: length, dtype: float64
In [110]:
dogs['length'].hist()
Out[110]:
<matplotlib.axes._subplots.AxesSubplot at 0x6103910>
In [111]:
dogs.plot(kind='scatter', x='length', y='inches')
Out[111]:
<matplotlib.axes._subplots.AxesSubplot at 0x6220cd0>
In [113]:
df.plot(kind='barh', x='name', y='length', legend=False)
Out[113]:
<matplotlib.axes._subplots.AxesSubplot at 0x7223bd0>
In [119]:
sortcats = (cats.sort_values(by='length', ascending=0))
sortcats.plot(kind='barh', x='name', y='length', legend=False, sort_columns=False)
#alternately! df[df['animal'] == 'cat'].sort_values(by='length').plot(kind='barh', x='name', y='length')
Out[119]:
<matplotlib.axes._subplots.AxesSubplot at 0x730ead0>
In [117]:
cats
Out[117]:
animal
name
length
inches
0
cat
Anne
35
13.790
1
cat
Bob
45
17.730
4
cat
Charlie
32
12.608
In [5]:
import pandas as pd
df = pd.read_excel("richpeople.xlsx")
What country are most billionaires from? For the top ones, how many billionaires per billion people? Who are the top 10 richest billionaires? What's the average wealth of a billionaire? Male? Female? Who is the poorest billionaire? Who are the top 10 poorest billionaires? 'What is relationship to company'? And what are the most common relationships? Most common source of wealth? Male vs. female? Given the richest person in a country, what % of the GDP is their wealth? 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, or other billionaires, so like pit the US vs India What are the most common industries for billionaires to come from? What's the total amount of billionaire money from each industry? How many self made billionaires vs. others? How old are billionaires? How old are billionaires self made vs. non self made? or different industries? Who are the youngest billionaires? The oldest? Age distribution - maybe make a graph about it? Maybe just made a graph about how wealthy they are in general? Maybe plot their net worth vs age (scatterplot) Make a bar graph of the top 10 or 20 richest
In [13]:
import matplotlib.pyplot as plt
%matplotlib inline
In [7]:
print(df['gender'].value_counts())
male 2328
female 249
married couple 3
Name: gender, dtype: int64
In [8]:
df.groupby('gender')['networthusbillion'].mean()
Out[8]:
gender
female 3.819277
male 3.516881
married couple 1.300000
Name: networthusbillion, dtype: float64
In [10]:
df.groupby('gender')['sourceofwealth'].value_counts()
Out[10]:
gender sourceofwealth
female diversified 9
real estate 7
media 6
construction 5
consumer goods 5
hotels, investments 5
Wal-Mart 4
casinos 4
chemicals 4
cleaning products 4
Samsung 3
banking 3
commodities 3
mining 3
packaging 3
pipelines 3
retail 3
Campbell Soup 2
Cargill Inc. 2
bank, media 2
banking inheritance 2
coffee 2
financial services 2
hotels, restaurants 2
inherited, cosmetics 2
insurance 2
investments 2
medical equipment 2
paper 2
pharmaceuticals 2
..
male telecom, oil service, real estate 1
telecom, oil, beer 1
telecoms 1
telecoms/lotteries/insurance 1
television, Univision 1
temp agency 1
textiles, apparel 1
timber/media 1
timberland, lumber mills 1
tobacco 1
tobacco distribution, retail 1
tobacco, banking 1
tools 1
tourism, construction 1
tractors 1
trading company 1
transport 1
travel 1
vaccines 1
vacuums 1
venture capital, Google 1
video cameras 1
videogames 1
water 1
water treatment systems 1
web hosting 1
wind turbines 1
wine 1
winter jackets 1
wrestling 1
Name: sourceofwealth, dtype: int64
In [16]:
df.columns.values
df['countrycode'].value_counts()
Out[16]:
USA 903
DEU 160
CHN 153
RUS 119
JPN 96
BRA 81
HKG 77
FRA 72
GBR 65
IND 63
ITA 58
CAN 53
CHE 51
MEX 44
Taiwan 40
ESP 37
KOR 36
AUS 33
TUR 32
IDN 31
MYS 28
SWE 27
ISR 26
SGP 26
THA 23
SAU 22
PHL 22
CHL 19
ARG 12
ZAF 12
...
FIN 5
ARE 5
POL 5
PRT 5
KAZ 5
BEL 4
CYP 4
DNK 4
NGA 4
MAR 4
MCO 3
NZL 3
OMN 2
MAC 2
LIE 2
UGA 1
ECU 1
TZA 1
KNA 1
VNM 1
DZA 1
ROU 1
SWZ 1
BHR 1
BMU 1
AGO 1
NPL 1
LTU 1
GGY 1
GEO 1
Name: countrycode, dtype: int64
In [19]:
df.sort_values(by='networthusbillion', ascending=False).head(10)
Out[19]:
year
name
rank
citizenship
countrycode
networthusbillion
selfmade
typeofwealth
gender
age
...
relationshiptocompany
foundingdate
gdpcurrentus
sourceofwealth
notes
notes2
source
source_2
source_3
source_4
284
2014
Bill Gates
1
United States
USA
76.0
self-made
founder non-finance
male
58.0
...
founder
1975.0
NaN
Microsoft
NaN
NaN
http://www.forbes.com/profile/bill-gates/
NaN
NaN
NaN
348
2014
Carlos Slim Helu
2
Mexico
MEX
72.0
self-made
privatized and resources
male
74.0
...
founder
1990.0
NaN
telecom
NaN
NaN
http://www.ozy.com/provocateurs/carlos-slims-w...
NaN
NaN
NaN
124
2014
Amancio Ortega
3
Spain
ESP
64.0
self-made
founder non-finance
male
77.0
...
founder
1975.0
NaN
retail
NaN
NaN
http://www.forbes.com/profile/amancio-ortega/
NaN
NaN
NaN
283
2001
Bill Gates
1
United States
USA
58.7
self-made
founder non-finance
male
45.0
...
founder
1975.0
1.062180e+13
NaN
NaN
NaN
http://www.forbes.com/profile/bill-gates/
NaN
NaN
NaN
2491
2014
Warren Buffett
4
United States
USA
58.2
self-made
founder non-finance
male
83.0
...
founder
1839.0
NaN
Berkshire Hathaway
NaN
NaN
http://www.forbes.com/lists/2009/10/billionair...
http://www.forbes.com/companies/berkshire-hath...
NaN
NaN
1377
2014
Larry Ellison
5
United States
USA
48.0
self-made
founder non-finance
male
69.0
...
founder
1977.0
NaN
Oracle
NaN
NaN
http://www.forbes.com/profile/larry-ellison/
http://www.businessinsider.com/how-larry-ellis...
NaN
NaN
509
2014
David Koch
6
United States
USA
40.0
inherited
inherited
male
73.0
...
relation
1940.0
NaN
diversified
inherited from father
NaN
http://www.kochind.com/About_Koch/History_Time...
NaN
NaN
NaN
381
2014
Charles Koch
6
United States
USA
40.0
inherited
inherited
male
78.0
...
relation
1940.0
NaN
diversified
inherited from father
NaN
http://www.kochind.com/About_Koch/History_Time...
NaN
NaN
NaN
2185
2014
Sheldon Adelson
8
United States
USA
38.0
self-made
self-made finance
male
80.0
...
founder
1952.0
NaN
casinos
NaN
NaN
http://www.forbes.com/profile/sheldon-adelson/
http://lasvegassun.com/news/1996/nov/26/rat-pa...
NaN
NaN
429
2014
Christy Walton
9
United States
USA
36.7
inherited
inherited
female
59.0
...
relation
1962.0
NaN
Wal-Mart
widow
NaN
http://www.forbes.com/profile/christy-walton/
NaN
NaN
NaN
10 rows × 30 columns
In [21]:
#Who is the poorest billionaire? Top ten poorest?
df.sort_values(by='rank', ascending=False).head(2)
Out[21]:
year
name
rank
citizenship
countrycode
networthusbillion
selfmade
typeofwealth
gender
age
...
relationshiptocompany
foundingdate
gdpcurrentus
sourceofwealth
notes
notes2
source
source_2
source_3
source_4
990
2014
Ina Chan
1565
Hong Kong
HKG
1.0
inherited
inherited
female
60.0
...
relation
1962.0
NaN
casinos
3rd wife
NaN
http://www.forbes.com/profile/ina-chan/
NaN
NaN
NaN
358
2014
Chang Pyung-Soon
1565
South Korea
KOR
1.0
self-made
founder non-finance
male
63.0
...
founder
1985.0
NaN
education
NaN
NaN
http://www.forbes.com/profile/chang-pyung-soon/
http://www.bloomberg.com/research/stocks/priva...
NaN
NaN
2 rows × 30 columns
In [22]:
df[df['networthusbillion'] == 1]
Out[22]:
year
name
rank
citizenship
countrycode
networthusbillion
selfmade
typeofwealth
gender
age
...
relationshiptocompany
foundingdate
gdpcurrentus
sourceofwealth
notes
notes2
source
source_2
source_3
source_4
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
19
1996
Adolf Merckle
388
Germany
DEU
1.0
inherited
inherited
male
61.0
...
relation
1881.0
2.500000e+12
NaN
4th generation
NaN
NaN
NaN
NaN
NaN
24
1996
Ahmed Ali Kanoo
383
Bahrain
BHR
1.0
inherited
inherited
male
0.0
...
relation
1890.0
6.100000e+09
NaN
3rd generation
With the permission and support of past Bahrai...
http://www.gulf-daily-news.com/NewsDetails.asp...
http://en.wikipedia.org/wiki/Yusuf_Bin_Ahmed_K...
NaN
NaN
49
2001
Albert Frere
490
Belgium
BEL
1.0
self-made
self-made finance
male
75.0
...
founder
1956.0
2.370000e+11
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Albert_Fr%C3%A8re
http://www.economist.com/node/6823579
NaN
NaN
56
2014
Alberto Alcocer
1565
Spain
ESP
1.0
self-made
self-made finance
male
71.0
...
owner
1952.0
NaN
investments
married to Esther Koplowitz
NaN
http://en.wikipedia.org/wiki/Alberto_Alcocer
http://www.forbes.com/profile/alberto-alcocer/
NaN
NaN
63
2001
Alberto Vilar
490
United States
USA
1.0
self-made
self-made finance
male
NaN
...
founder
1980.0
1.062180e+13
NaN
convicted for money laundering and fraud in 2008
NaN
http://en.wikipedia.org/wiki/Alberto_Vilar
ARTS AND THE MAN The Miami Herald June 17, 200...
http://money.cnn.com/2005/05/27/news/newsmaker...
NaN
81
2014
Alexander Vik
1565
Norway
NOR
1.0
self-made
self-made finance
male
59.0
...
investor
1980.0
NaN
investments
many business ventures have failed but still m...
NaN
http://www.forbes.com/sites/nathanvardi/2014/0...
NaN
NaN
NaN
107
1996
Alicia and Esther Koplowitz
405
Spain
ESP
1.0
inherited
inherited
female
0.0
...
relation
1952.0
6.410000e+11
NaN
inherited from father
fortune split between her and sister
http://en.wikipedia.org/wiki/Alicia_Koplowitz,...
http://www.forbes.com/profile/alicia-koplowitz/
NaN
NaN
108
2001
Alicia Koplowitz
490
Spain
ESP
1.0
inherited
inherited
female
48.0
...
relation
1952.0
6.260000e+11
NaN
inherited from father
fortune split between her and sister
http://en.wikipedia.org/wiki/Alicia_Koplowitz,...
http://www.forbes.com/profile/alicia-koplowitz/
NaN
NaN
122
2001
Amalia Lacroze de Fortabat
490
Argentina
ARG
1.0
inherited
inherited
female
NaN
...
relation
1926.0
2.690000e+11
NaN
widow
NaN
http://en.wikipedia.org/wiki/Mar%C3%ADa_Amalia...
Australian Financial Review February 28, 2014 ...
NaN
NaN
129
2014
An Kang
1565
China
CHN
1.0
self-made
founder non-finance
male
65.0
...
founder
1992.0
NaN
pharmaceuticals
NaN
NaN
http://www.forbes.com/profile/an-kang/
http://english.hualanbio.com/enterhualan/history/
NaN
NaN
131
1996
Ana Maria Brescia Cafferata
401
Peru
PER
1.0
inherited
inherited
female
0.0
...
relation
1889.0
5.397591e+10
NaN
inherited father's company
took part in major business decisions of company
http://es.wikipedia.org/wiki/Grupo_Brescia
http://www.forbes.com/profile/ana-maria-bresci...
NaN
NaN
136
2001
Ananda Krishnan
490
Malaysia
MYS
1.0
self-made
founder non-finance
male
62.0
...
founder
1984.0
9.278395e+10
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Ananda_Krishnan
http://www.bloomberg.com/research/stocks/priva...
http://www.forbes.com/profile/ananda-krishnan/
NaN
145
2014
Andrea Reimann-Ciardelli
1565
United States
USA
1.0
inherited
inherited
female
NaN
...
relation
1923.0
NaN
consumer goods
inherited from father
no involvement in company
http://www.forbes.com/profile/matthias-reimann...
NaN
NaN
NaN
164
2014
Andrew Gotianun
1565
Philippines
PHL
1.0
self-made
self-made finance
male
86.0
...
founder
1955.0
NaN
real estate
NaN
NaN
http://en.wikipedia.org/wiki/Andrew_Gotianun
http://www.forbes.com/profile/andrew-gotianun/
Second-hand car dealer discovers road to riche...
NaN
171
2014
Angela Bennett
1565
Australia
AUS
1.0
inherited
inherited
female
69.0
...
relation
1955.0
NaN
mining
inherited from father
shared fortune with brother
http://www.forbes.com/profile/angela-bennett/
NaN
NaN
NaN
178
2014
Anne Beaufour
1565
France
FRA
1.0
inherited
inherited
female
50.0
...
relation
1929.0
NaN
pharmaceuticals
3rd generation
NaN
http://en.wikipedia.org/wiki/Ipsen
http://www.ipsen.com/le-groupe/historique-du-g...
http://www.forbes.com/profile/anne-beaufour/
NaN
191
2001
Antonia Johnson
490
Sweden
SWE
1.0
inherited
inherited
female
57.0
...
relation
1873.0
2.399170e+11
NaN
4th generation
chairman, no siblings, one female cousin
http://en.wikipedia.org/wiki/Antonia_Ax:son_Jo...
http://www.forbes.com/profile/antonia-johnson/
NaN
NaN
223
1996
Autrey family
400
Mexico
MEX
1.0
NaN
NaN
NaN
0.0
...
NaN
NaN
3.974040e+11
NaN
NaN
NaN
MEXICAN FAMILY ATTEMPTS A COMEBACK WALL STREET...
NaN
NaN
NaN
234
2014
B.R. Shetty
1565
India
IND
1.0
self-made
founder non-finance
male
72.0
...
founder
1975.0
NaN
healthcare
NaN
NaN
http://en.wikipedia.org/wiki/B._R._Shetty
http://www.nmchealth.com/dr-br-shetty/
NaN
NaN
247
2001
Barry Diller
490
United States
USA
1.0
self-made
executive
male
59.0
...
Chairman and Chief Executive Officer
1986.0
1.062180e+13
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Barry_Diller
http://www.forbes.com/profile/barry-diller/
VIVENDI TO ENLIST DILLER AS CO-CEO;STRUGGLING ...
NaN
252
2001
Belmiro de Azevedo
490
Portugal
PRT
1.0
self-made
executive
male
59.0
...
chairman
1959.0
1.215460e+11
NaN
nationalized in 1974 and reprivitized later
NaN
http://en.wikipedia.org/wiki/Sonae
http://www.fchampalimaud.org/en/the-foundation...
NaN
NaN
256
2001
Benjamin de Rothschild
490
Switzerland
CHE
1.0
inherited
inherited
male
NaN
...
relation
1953.0
2.790000e+11
NaN
5th generation
member of Rothschild banking family (founded i...
http://en.wikipedia.org/wiki/Benjamin_de_Roths...
NaN
NaN
NaN
261
2014
Bent Jensen
1565
Denmark
DEN
1.0
inherited
inherited
male
62.0
...
relation
1907.0
NaN
electric linear systems
3rd generation
NaN
http://www.forbes.com/profile/bent-jensen/
NaN
NaN
NaN
295
1996
Boonsong Asavabhokhin
408
Thailand
THA
1.0
NaN
NaN
male
0.0
...
NaN
NaN
1.819480e+11
NaN
NaN
NaN
http://www.pbs.org/wgbh/pages/frontline/shows/...
NaN
NaN
NaN
296
2014
Boris Mints
1565
Russia
RUS
1.0
self-made
self-made finance
male
55.0
...
owner
2010.0
NaN
real estate
NaN
NaN
http://www.forbes.com/profile/boris-mints/
http://www.o1properties.ru/o1properties/about-...
http://www.bloomberg.com/research/stocks/priva...
NaN
302
2014
Brian Higgins
1565
United States
USA
1.0
self-made
self-made finance
male
49.0
...
founder
1995.0
NaN
Hedge fund
NaN
NaN
http://www.forbes.com/profile/brian-higgins-1/
http://www.forbes.com/sites/nathanvardi/2014/0...
NaN
NaN
310
2001
Bruce Kovner
490
United States
USA
1.0
self-made
self-made finance
male
55.0
...
founder
1983.0
1.062180e+13
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Bruce_Kovner
https://www.caxton.com/
Bruce Kovner, Influential Hedge Fund Manager, ...
NaN
320
2014
C. James Koch
1565
United States
USA
1.0
self-made
founder non-finance
male
64.0
...
founder
1984.0
NaN
beer
NaN
NaN
http://en.wikipedia.org/wiki/Samuel_Adams_(beer)
SAM ADAMS CREATOR THRIVES AMID CRAFT BEER SURG...
NaN
NaN
332
1996
Carl Pohlad
423
United States
USA
1.0
self-made
self-made finance
male
80.0
...
owner
1920.0
8.100200e+12
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Carl_Pohlad
http://www.nytimes.com/2009/01/06/sports/baseb...
NaN
NaN
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2200
1996
Shoichiro Toyoda
395
Japan
JPN
1.0
inherited
inherited
male
0.0
...
relation
1937.0
4.710000e+12
NaN
3rd generation
NaN
http://en.wikipedia.org/wiki/Shoichiro_Toyoda
NaN
NaN
NaN
2202
2001
Shoji Uehara
490
Japan
JPN
1.0
inherited
inherited
male
NaN
...
relation
1912.0
4.160000e+12
NaN
inherited from father
NaN
http://www.forbes.com/profile/shoji-uehara/#
http://en.wikipedia.org/wiki/Taisho_Pharmaceut...
NaN
NaN
2203
2014
Shoji Uehara
1565
Japan
JPN
1.0
inherited
inherited
male
86.0
...
relation
1912.0
NaN
pharmaceuticals
inherited from father
NaN
http://www.forbes.com/profile/shoji-uehara/#
http://en.wikipedia.org/wiki/Taisho_Pharmaceut...
NaN
NaN
2204
1996
Shoul Eisenberg
392
Israel
ISR
1.0
self-made
privatized and resources
male
0.0
...
founder
1968.0
1.090000e+11
NaN
joint venture with governement
NaN
http://en.wikipedia.org/wiki/Israel_Corporation
NaN
NaN
NaN
2247
2014
Stefan von Holtzbrinck
1565
Germany
DEU
1.0
inherited
inherited
male
50.0
...
relation
1948.0
NaN
publishing
inherited from father
NaN
http://www.forbes.com/profile/monika-schoeller/
NaN
NaN
NaN
2277
1996
Steven Spielberg
422
United States
USA
1.0
self-made
founder non-finance
male
49.0
...
founder
1994.0
8.100200e+12
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Steven_Spielberg
NaN
NaN
NaN
2285
1996
Strwher family
389
Germany
DEU
1.0
NaN
NaN
NaN
0.0
...
NaN
NaN
2.500000e+12
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2316
2014
T.S. Kalyanaraman
1565
India
IND
1.0
self-made
founder non-finance
male
66.0
...
founder
1993.0
NaN
jewelry
NaN
NaN
http://en.wikipedia.org/wiki/T.S._Kalyanaraman
http://www.bloomberg.com/news/articles/2013-01...
NaN
NaN
2358
2001
Thomas Bailey
490
United States
USA
1.0
self-made
self-made finance
male
64.0
...
founder
1969.0
1.062180e+13
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Thomas_H._Bailey
http://en.wikipedia.org/wiki/Janus_Capital_Group
http://archive.fortune.com/magazines/fortune/f...
NaN
2359
2014
Thomas Bailey
1565
United States
USA
1.0
self-made
self-made finance
male
77.0
...
founder
1969.0
NaN
finance
NaN
NaN
http://en.wikipedia.org/wiki/Thomas_H._Bailey
http://en.wikipedia.org/wiki/Janus_Capital_Group
http://archive.fortune.com/magazines/fortune/f...
NaN
2365
2014
Thomas Kaplan
1565
United States
USA
1.0
self-made
self-made finance
male
51.0
...
founder
1993.0
NaN
investments
NaN
NaN
http://en.wikipedia.org/wiki/Thomas_Kaplan
http://www.wsj.com/articles/SB1000142405270230...
NaN
NaN
2391
2001
Toichi Takenaka
490
Japan
JPN
1.0
inherited
inherited
male
58.0
...
relation
1610.0
4.160000e+12
NaN
15th or more generation
NaN
http://en.wikipedia.org/wiki/Takenaka_Corporation
NaN
NaN
NaN
2394
2001
Tom Gores
490
United States
USA
1.0
self-made
self-made finance
male
36.0
...
founder
1995.0
1.062180e+13
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Tom_Gores
http://en.wikipedia.org/wiki/Platinum_Equity
NaN
NaN
2401
2014
Tory Burch
1565
United States
USA
1.0
self-made
founder non-finance
female
47.0
...
founder
2004.0
NaN
fashion
NaN
NaN
http://en.wikipedia.org/wiki/J._Christopher_Burch
http://www.vanityfair.com/news/2007/02/tory-bu...
NaN
NaN
2439
2001
Vinod Khosla
490
United States
USA
1.0
self-made
self-made finance
male
46.0
...
founder
1982.0
1.062180e+13
NaN
NaN
NaN
http://en.wikipedia.org/wiki/Vinod_Khosla
Silicon Valley billionaire likes playing a gam...
NaN
NaN
2443
2014
Vivek Chaand Sehgal
1565
Australia
AUS
1.0
self-made
founder non-finance
male
57.0
...
founder
1986.0
NaN
auto parts
Indian founder of Motherson Sumi, Australian c...
NaN
http://www.forbes.com/profile/vivek-chaand-seh...
http://en.wikipedia.org/wiki/Motherson_Sumi_Sy...
NaN
Will Vivek Sehgal’s Gambit Pay Off? Rashmi K P...
2472
2014
Wang Jianfeng
1565
China
CHN
1.0
self-made
founder non-finance
male
44.0
...
founder
2004.0
NaN
auto parts
NaN
NaN
http://www.forbes.com/profile/wang-jianfeng/
http://en.joyson.cn/About/RongYuZiZHi.html
NaN
NaN
2479
2014
Wang Muqing
1565
China
CHN
1.0
self-made
founder non-finance
male
63.0
...
founder
1999.0
NaN
auto distribution
NaN
NaN
http://www.forbes.com/profile/wang-muqing/
http://www.zhengtongauto.com/en/milestone.html
NaN
NaN
2484
2014
Wang Yong
1565
China
CHN
1.0
self-made
founder non-finance
male
63.0
...
founder
1986.0
NaN
food sweeteners
NaN
NaN
http://www.forbes.com/profile/wang-yong/
NaN
NaN
NaN
2494
2001
Wee Cho Yaw
490
Singapore
SGP
1.0
inherited
inherited
male
72.0
...
relation
1935.0
8.928509e+10
NaN
inherited from father
NaN
http://en.wikipedia.org/wiki/Wee_Cho_Yaw
http://en.wikipedia.org/wiki/United_Overseas_Bank
Wee Cho Yaw highest paid of local bank heads; ...
NaN
2504
1996
Werhahn family
390
Germany
DEU
1.0
inherited
inherited
NaN
0.0
...
NaN
1844.0
2.500000e+12
NaN
NaN
NaN
http://de.wikipedia.org/wiki/Wilh._Werhahn_KG
http://www.werhahn.de/en/home.html
NaN
NaN
2521
2001
William France Jr
490
United States
USA
1.0
inherited
inherited
male
68.0
...
relation
1947.0
1.062180e+13
NaN
inherited from father
NaN
http://en.wikipedia.org/wiki/Bill_France,_Jr.
TOYOTA ENDURES HUMBLING DEBUT;TOP STORIES ALSO...
NaN
NaN
2524
2001
William Hearst III
490
United States
USA
1.0
inherited
inherited
male
51.0
...
relation
1887.0
1.062180e+13
NaN
3rd generation
NaN
http://en.wikipedia.org/wiki/William_Randolph_...
http://en.wikipedia.org/wiki/Hearst_Corporation
SF UNZIPPED BLOG The San Francisco Chronicle (...
NaN
2530
2014
William Moncrief, Jr.
1565
United States
USA
1.0
inherited
inherited
male
93.0
...
relation
1929.0
NaN
oil
joined father's business following WWII
NaN
http://en.wikipedia.org/wiki/William_Moncrief
http://www.moncriefoil.com/history.htm
NaN
NaN
2537
1996
Winthrop Rockefeller
416
United States
USA
1.0
inherited
inherited
male
47.0
...
relation
1870.0
8.100200e+12
NaN
3rd generation
NaN
Agency celebrates 50th with Rockefeller tribut...
NaN
NaN
NaN
2547
2014
Wu Chung-Yi
1565
Taiwan
Taiwan
1.0
self-made
executive
male
55.0
...
investor
1991.0
NaN
manufacturing
NaN
NaN
http://www.forbes.com/profile/wu-chung-yi/
http://en.wikipedia.org/wiki/Tingyi_(Cayman_Is...
Tingyi-Campbell sale talks stay on track South...
NaN
2549
2014
Wu Xiong
1565
China
CHN
1.0
self-made
executive
male
NaN
...
owner
1999.0
NaN
infant formula
NaN
NaN
http://www.forbes.com/profile/wu-xiong/
NaN
NaN
NaN
2561
2014
Yang Keng
1565
China
CHN
1.0
self-made
self-made finance
male
53.0
...
chairman
NaN
NaN
real estate
NaN
NaN
http://www.forbes.com/profile/yang-keng/
NaN
NaN
NaN
2591
2014
Zdenek Bakala
1565
Czech Republic
CZE
1.0
self-made
privatized and resources
male
53.0
...
founder
1994.0
NaN
coal
NaN
NaN
http://cs.wikipedia.org/wiki/Zden%C4%9Bk_Bakala
NaN
NaN
NaN
2607
2014
Zhu Wenchen
1565
China
CHN
1.0
self-made
executive
male
48.0
...
chairman
1999.0
NaN
pharmaceuticals
NaN
NaN
http://www.furenpharm.com/aboutus.asp?cid=82
http://www.forbes.com/profile/zhu-wenchen/
NaN
NaN
171 rows × 30 columns
In [24]:
df['networthusbillion'].describe()
Out[24]:
count 2614.000000
mean 3.531943
std 5.088813
min 1.000000
25% 1.400000
50% 2.000000
75% 3.500000
max 76.000000
Name: networthusbillion, dtype: float64
In [25]:
df.groupby("gender")["networthusbillion"].describe()
Out[25]:
gender
female count 249.000000
mean 3.819277
std 5.046177
min 1.000000
25% 1.400000
50% 2.100000
75% 3.700000
max 36.700000
male count 2328.000000
mean 3.516881
std 5.123194
min 1.000000
25% 1.400000
50% 2.000000
75% 3.400000
max 76.000000
married couple count 3.000000
mean 1.300000
std 0.264575
min 1.000000
25% 1.200000
50% 1.400000
75% 1.450000
max 1.500000
Name: networthusbillion, dtype: float64
In [28]:
#Adding the wealt of all the billionaires in a given country.
df.groupby('countrycode')['networthusbillion'].sum().sort_values(ascending=False)
Out[28]:
countrycode
USA 3542.1
DEU 671.0
RUS 434.9
CHN 377.1
HKG 338.2
FRA 336.9
JPN 280.3
BRA 224.7
ITA 220.3
IND 210.8
MEX 202.6
GBR 190.6
CAN 175.3
CHE 168.6
SWE 164.8
ESP 142.4
Taiwan 112.0
SAU 106.3
AUS 93.2
KOR 86.8
MYS 86.4
IDN 80.6
SGP 68.4
PHL 67.6
ISR 65.7
TUR 65.4
THA 60.3
CHL 52.7
NLD 38.5
COL 36.3
...
ARE 16.5
KWT 15.5
PRT 13.6
FIN 13.3
PER 12.9
POL 12.8
NZL 10.8
KAZ 9.2
BEL 9.0
DNK 8.9
MAR 7.4
GEO 5.2
MCO 4.6
AGO 3.7
SWZ 3.7
DZA 3.2
LIE 2.9
MAC 2.8
GGY 2.4
OMN 2.3
BMU 2.1
VNM 1.6
KNA 1.2
ECU 1.2
ROU 1.2
NPL 1.1
UGA 1.1
BHR 1.0
LTU 1.0
TZA 1.0
Name: networthusbillion, dtype: float64
In [30]:
#What are the most common industries for billionaires to come from?
df['industry'].value_counts()
Out[30]:
Consumer 471
Retail, Restaurant 281
Real Estate 280
Money Management 249
Media 219
Technology-Computer 208
Diversified financial 167
Energy 132
Technology-Medical 111
Non-consumer industrial 107
Constrution 97
Mining and metals 90
Other 83
Hedge funds 67
Private equity/leveraged buyout 25
0 16
Venture Capital 8
banking 1
services 1
Name: industry, dtype: int64
In [32]:
df.groupby('industry')['networthusbillion'].sum()
Out[32]:
industry
0 24.7
Constrution 236.4
Consumer 1756.3
Diversified financial 702.9
Energy 433.3
Hedge funds 223.8
Media 852.5
Mining and metals 282.8
Money Management 710.7
Non-consumer industrial 348.9
Other 222.8
Private equity/leveraged buyout 87.9
Real Estate 844.2
Retail, Restaurant 1161.3
Technology-Computer 1015.2
Technology-Medical 311.0
Venture Capital 14.2
banking 1.3
services 1.2
Name: networthusbillion, dtype: float64
In [33]:
young_bills = df[df['age'] < 40]
young_bills.plot(kind='barh', x='name', y='networthusbillion').sort_values()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-33-246e6df5715b> in <module>()
1 young_bills = df[df['age'] < 40]
----> 2 young_bills.plot(kind='barh', x='name', y='networthusbillion').sort_values()
AttributeError: 'AxesSubplot' object has no attribute 'sort_values'
In [ ]:
import pandas as pd
import matplotlib.pplot as plt
plt.style.use("ggplot")
%matplot
Content source: kbennion/foundations-hw
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