Visualizing companies and its profits in 2000, according to Forbes magazine

In [1]:

# Visualizing companies and its profits in 2000, according to Forbes magazine.

In [2]:

import numpy as np
import pandas as pd
from collections import Counter
from pandas import Series, DataFrame

In [3]:

# Reading the file:
data = pd.read_csv('Forbes2000.csv', nrows=20, index_col=0)
data

Out[3]:

	rank	name	country	category	sales	profits	assets	marketvalue
1	1	Citigroup	United States	Banking	94.71	17.85	1264.03	255.30
2	2	General Electric	United States	Conglomerates	134.19	15.59	626.93	328.54
3	3	American Intl Group	United States	Insurance	76.66	6.46	647.66	194.87
4	4	ExxonMobil	United States	Oil & gas operations	222.88	20.96	166.99	277.02
5	5	BP	United Kingdom	Oil & gas operations	232.57	10.27	177.57	173.54
6	6	Bank of America	United States	Banking	49.01	10.81	736.45	117.55
7	7	HSBC Group	United Kingdom	Banking	44.33	6.66	757.60	177.96
8	8	Toyota Motor	Japan	Consumer durables	135.82	7.99	171.71	115.40
9	9	Fannie Mae	United States	Diversified financials	53.13	6.48	1019.17	76.84
10	10	Wal-Mart Stores	United States	Retailing	256.33	9.05	104.91	243.74
11	11	UBS	Switzerland	Diversified financials	48.95	5.15	853.23	85.07
12	12	ING Group	Netherlands	Diversified financials	94.72	4.73	752.49	54.59
13	13	Royal Dutch/Shell Group	Netherlands/ United Kingdom	Oil & gas operations	133.50	8.40	100.72	163.45
14	14	Berkshire Hathaway	United States	Insurance	56.22	6.95	172.24	141.14
15	15	JP Morgan Chase	United States	Banking	44.39	4.47	792.70	81.94
16	16	IBM	United States	Technology hardware & equipment	89.13	7.58	104.46	171.54
17	17	Total	France	Oil & gas operations	131.64	8.84	87.84	116.64
18	18	BNP Paribas	France	Banking	47.74	4.73	745.09	59.29
19	19	Royal Bank of Scotland	United Kingdom	Banking	35.65	4.95	663.45	90.21
20	20	Freddie Mac	United States	Diversified financials	46.26	10.09	752.25	44.25

In [4]:

# Extracting company names and its profits:
company_names = data['name']
company_profits = data['profits']

print(company_names)

1                   Citigroup
2            General Electric
3         American Intl Group
4                  ExxonMobil
5                          BP
6             Bank of America
7                  HSBC Group
8                Toyota Motor
9                  Fannie Mae
10            Wal-Mart Stores
11                        UBS
12                  ING Group
13    Royal Dutch/Shell Group
14         Berkshire Hathaway
15            JP Morgan Chase
16                        IBM
17                      Total
18                BNP Paribas
19     Royal Bank of Scotland
20                Freddie Mac
Name: name, dtype: object

In [5]:

print(company_profits)

1     17.85
2     15.59
3      6.46
4     20.96
5     10.27
6     10.81
7      6.66
8      7.99
9      6.48
10     9.05
11     5.15
12     4.73
13     8.40
14     6.95
15     4.47
16     7.58
17     8.84
18     4.73
19     4.95
20    10.09
Name: profits, dtype: float64

In [6]:

# Visualizing the data:
import matplotlib.pyplot as plt

In [9]:

plt.style.use("ggplot")
plt.figure(figsize=(14, 14))

plt.barh(company_names, company_profits)
plt.title("Companies.Forbes 2000")
#plt.ylabel("Companies")
plt.xlabel("Profits")

plt.show()

In [8]:

# We can see that ExxonMobil and Citigroup, were two of the most profitable companies in 2000.