Navigating Africa’s Business Landscape Using Python

Analyzing Top Companies Africa with Python and Pandas

Author
Affiliations

John Karuitha

Karatina University, School of Business

Graduate School of Business Administration, University of the Witwatersrand

Published

August 12, 2023

Modified

August 12, 2023

1 Background

In this analysis, we use data from Kagle(Kagle, 2023) to illustrate the use of Python and Pandas in data analysis(VanderPlas, 2016). The data captures the top 2000 companies in the world and is available for free upon registration on the Kagle website. We filter the data to only include companies from Africa.

2 Objectives

The primary goal of this exercise is to illustrate how to use Python, pandas and other libraries in Python programming to analyse data for meaningful insights about large companies in Africa.

Specifically, we seek to;

  1. Rank companies in Africa by their performance.
  2. Rank countries in Africa based on the concentration of large companies.
  3. Compute the correlation between company size (in terms of assets) and financial performance.
  4. Uncover regional patterns in company performance based on country.
  5. Establish the companies, countries in Africa with the highest returns on assets and profit margins.

3 Summary of Results

  1. Most large companies in the sample are located in South Africa.
  2. There is a positive relationship between company size and financial performance (sales, profits, market value). However, this relationship varies across countries.
  3. Companies from South Africa have the highest sales, profits, asset base, and valuation.
  4. Companies in Nigeria are more efficient in converting sales and assets into profits.

NB: ALL FIGURES ARE IN BILLIONS OF US DOLLARS

Tip

Please visit my rpubs site to see more data projects. Alternatively, copy and paste the link <www.rpubs.com/Karuitha> into your browser. You can also view my linkedin site for my skills and education.

My Tableau public profile contains my data visualizations.

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Tip

Skills & Technologies Applied: Python, Pandas, Geopandas, Matplotlib, Quarto, Data Science.

4 Data

The data is available on kagle as a csv file (registration is required to access the data). I start by loading the necessary packages for the analysis: pandas, numpy, matplotlib, and seaborn (Borjigin, 2023).

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import geopandas as gpd
from shapely.geometry import Point

Next I read in the data using pandas and set the index column as the name of the company given it is a unique and meaningful identifier.

```{python}
company = pd.read_csv("Top2000CompaniesGlobally.csv")

company.columns
```
Index(['Global Rank', 'Company', 'Sales ($billion)', 'Profits ($billion)',
       'Assets ($billion)', 'Market Value ($billion)', 'Country', 'Continent',
       'Latitude', 'Longitude'],
      dtype='object')

Next, I rename the columns using names that are easier to work with. I create a dictionary of old names versus new names and then rename the columns.

```{python}
## Create a dictionary of old names vs new names
names = {'Global Rank':'rank', 
'Sales ($billion)':'sales', 
'Profits ($billion)':'profits',
'Assets ($billion)':'assets', 
'Market Value ($billion)':'market_value', 
'Country':'country', 
'Continent':'continent',
'Latitude':'latitude', 
'Longitude':'longitude',
'Company':'company'}

# Rename the columns in place
company.rename(columns = names, inplace = True)
```

Next, we filter the data to narrow down on Africa.

```{python}
company = company[company['continent'] == "Africa"]
```
```{python}
## Addd the index
company.set_index("company", inplace = True)
```

5 Data Exploration

We now explore the data.

5.1 Number of rows and columns

We start with the number of rows and columns in the data.

```{python}
company.shape
```
(25, 9)

We see that the data has 25 rows (records) and 9 columns (variables).

5.2 First and Last Rows of the Data

Next, lets examine the first 5 rows.

```{python}
company.head(5)
```
rank sales profits assets market_value country continent latitude longitude
company
Standard Bank Group 231 21.0 1.9 182.0 19.9 South Africa Africa -30.559482 22.937506
Sasol 320 20.8 2.9 24.7 29.4 South Africa Africa -30.559482 22.937506
MTN Group 345 16.5 2.5 21.4 34.5 South Africa Africa -30.559482 22.937506
FirstRand 397 8.4 1.6 94.1 18.0 South Africa Africa -30.559482 22.937506
Sanlam 574 10.8 0.7 52.3 10.7 South Africa Africa -30.559482 22.937506

We also check the last 7 rows.

```{python}
company.tail(7)
```
rank sales profits assets market_value country continent latitude longitude
company
Gold Fields 1435 3.4 0.7 11.2 5.9 South Africa Africa -30.559482 22.937506
Imperial Holdings 1658 9.9 0.4 5.5 4.2 South Africa Africa -30.559482 22.937506
Aspen Pharmacare Holdings 1711 1.9 0.4 3.8 9.7 South Africa Africa -30.559482 22.937506
RMB Holdings 1716 0.6 0.6 3.3 6.1 South Africa Africa -30.559482 22.937506
Banque Centrale Populaire 1871 1.9 0.2 32.0 3.9 Morocco Africa 31.791702 -7.092620
Assore 1948 1.6 0.5 2.1 5.2 South Africa Africa -30.559482 22.937506
Commercial International Bank 1966 1.5 0.3 14.8 2.8 Egypt Africa 26.820553 30.802498

5.3 Visual Summary

let us use seaborn pairplot() function to create a summary picture of the data using all variables, colored by country.

```{python}
#| fig-cap: "Pairs plots for the Variables (A)"
sns.pairplot(company[['country', 'sales', 'profits', 'assets', 'market_value']], hue = "country", corner = True, palette = "Dark2", kind = "scatter")
```

Pairs plots for the Variables (A)
```{python}
#| fig-cap: "Pairs plots for the Variables (B)"
sns.pairplot(company[['country', 'sales', 'profits', 'assets', 'market_value']], corner = True, palette = "Dark2", kind = "kde")
```

Pairs plots for the Variables (B)

5.4 Variable Types

The variable types are also an important data component.

```{python}
company.info()
```
<class 'pandas.core.frame.DataFrame'>
Index: 25 entries, Standard Bank Group to Commercial International Bank
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   rank          25 non-null     int64  
 1   sales         25 non-null     float64
 2   profits       25 non-null     float64
 3   assets        25 non-null     float64
 4   market_value  25 non-null     float64
 5   country       25 non-null     object 
 6   continent     25 non-null     object 
 7   latitude      25 non-null     float64
 8   longitude     25 non-null     float64
dtypes: float64(6), int64(1), object(2)
memory usage: 2.5+ KB
```{python}
company.dtypes
```
rank              int64
sales           float64
profits         float64
assets          float64
market_value    float64
country          object
continent        object
latitude        float64
longitude       float64
dtype: object

5.5 Missing Values

Let us check the data for any missing values for sales, profits, assets, market_value, longitude, and latitude.

```{python}
company[company['sales'].isna()]
```
rank sales profits assets market_value country continent latitude longitude
company 
```{python}
company[company['profits'].isna()]
```
rank sales profits assets market_value country continent latitude longitude
company 
```{python}
company[company['assets'].isna()]
```
rank sales profits assets market_value country continent latitude longitude
company 
```{python}
company[company['market_value'].isna()]
```
rank sales profits assets market_value country continent latitude longitude
company 
```{python}
company[company['longitude'].isna()]
```
rank sales profits assets market_value country continent latitude longitude
company 
```{python}
company[company['latitude'].isna()]
```
rank sales profits assets market_value country continent latitude longitude
company

This is a conveniently clean data without missing values which is a rare occurrence in data science.

5.6 Summary Statistics

We use the describe method to make summary statistics for numeric and character variables separately.

```{python}
company.describe(include = "float64")
```
sales profits assets market_value latitude longitude
count 25.000000 25.000000 25.000000 25.000000 25.000000 25.000000
mean 7.068000 0.824000 25.240000 11.064000 -17.915824 19.299126
std 6.109195 0.706093 38.608073 8.497445 23.543621 10.892318
min 0.600000 0.000000 2.100000 2.800000 -30.559482 -9.429499
25% 1.900000 0.400000 5.500000 5.900000 -30.559482 22.937506
50% 5.400000 0.600000 11.200000 8.100000 -30.559482 22.937506
75% 9.900000 0.800000 24.700000 10.700000 -30.559482 22.937506
max 21.000000 2.900000 182.000000 34.500000 31.791702 30.802498

We also do the same for string data types.

```{python}
company.describe(include = "object")
```
country continent
count 25 25
unique 5 1
top South Africa Africa
freq 19 25

Here we see that there are 25 observations with 5 unique countries. The country with the most companies is the South Africa (19 out of the 25 companies in the sample).

6 Analysis

In this section, we explore some key issues from the data, listed in order.

6.1 Which Companies are the Best Performing in Africa?

6.1.1 Companies with the Highest Sales in Africa

We start by examining sales. The Standard Bank Group and Sasol lead the pack in this case. Npte that the top 10 companies are from South Africa with the exception of Royal Carribean Cruises from Liberia.

```{python}
company.nlargest(25, "sales")[['country', 'sales']]
```
country sales
company
Standard Bank Group South Africa 21.0
Sasol South Africa 20.8
MTN Group South Africa 16.5
Bidvest Group South Africa 16.4
Sanlam South Africa 10.8
Shoprite Holdings South Africa 10.1
Steinhoff International South Africa 9.9
Imperial Holdings South Africa 9.9
FirstRand South Africa 8.4
Royal Carribean Cruises Liberia 7.7
MMI Holdings South Africa 6.2
AngloGold Ashanti South Africa 6.1
Orascom Construction Egypt 5.4
Naspers South Africa 5.1
Impala Platinum Holdings South Africa 3.4
Gold Fields South Africa 3.4
Attijariwafa Bank Morocco 3.4
Aspen Pharmacare Holdings South Africa 1.9
Banque Centrale Populaire Morocco 1.9
Remgro South Africa 1.7
Assore South Africa 1.6
Dangote Cement Nigeria 1.5
Exxaro Resources South Africa 1.5
Commercial International Bank Egypt 1.5
RMB Holdings South Africa 0.6

6.1.2 Companies with the Highest Profits in Africa

In terms of profits, Sasol and MTN Group from South Africa come up tops. Dangote Cement of Nigeria is the only company on the top 10 that is outside South Africa.

```{python}
company.nlargest(25, "profits")[['country', 'profits']]
```
country profits
company
Sasol South Africa 2.9
MTN Group South Africa 2.5
Standard Bank Group South Africa 1.9
FirstRand South Africa 1.6
Exxaro Resources South Africa 1.2
Remgro South Africa 1.1
Dangote Cement Nigeria 0.8
AngloGold Ashanti South Africa 0.8
Gold Fields South Africa 0.7
Orascom Construction Egypt 0.7
Steinhoff International South Africa 0.7
Sanlam South Africa 0.7
RMB Holdings South Africa 0.6
Attijariwafa Bank Morocco 0.5
Bidvest Group South Africa 0.5
Impala Platinum Holdings South Africa 0.5
Assore South Africa 0.5
Shoprite Holdings South Africa 0.4
Naspers South Africa 0.4
Imperial Holdings South Africa 0.4
Aspen Pharmacare Holdings South Africa 0.4
MMI Holdings South Africa 0.3
Commercial International Bank Egypt 0.3
Banque Centrale Populaire Morocco 0.2
Royal Carribean Cruises Liberia 0.0

6.1.3 Companies with the Highest Market Value in Africa

The picture replicates itself in market valuation.

```{python}
company.nlargest(25, "market_value")[["country", "market_value"]]
```
country market_value
company
MTN Group South Africa 34.5
Sasol South Africa 29.4
Naspers South Africa 27.7
Standard Bank Group South Africa 19.9
FirstRand South Africa 18.0
Dangote Cement Nigeria 15.1
Sanlam South Africa 10.7
Shoprite Holdings South Africa 10.2
Remgro South Africa 9.9
Aspen Pharmacare Holdings South Africa 9.7
AngloGold Ashanti South Africa 9.5
Impala Platinum Holdings South Africa 9.3
Bidvest Group South Africa 8.1
Royal Carribean Cruises Liberia 7.4
Orascom Construction Egypt 7.0
Attijariwafa Bank Morocco 6.7
Exxaro Resources South Africa 6.4
RMB Holdings South Africa 6.1
Gold Fields South Africa 5.9
Assore South Africa 5.2
Steinhoff International South Africa 5.0
Imperial Holdings South Africa 4.2
MMI Holdings South Africa 4.0
Banque Centrale Populaire Morocco 3.9
Commercial International Bank Egypt 2.8

6.2 Which Countries and Continents Have the Highest Concentration of Large Companies?

In this section, we utilize the global rank in size variable to identify the countries and continents with the highest number of top-ranking companies. We see that companies from USA, Japan, China, UK, Canada, and South Korea dominate the rankings. Note that these are countries mainly from North America and Asia (with the exception of the UK) which illustrates the dominance of these two continents in global trade.

```{python}
company['country'].value_counts()
```
country
South Africa    19
Morocco          2
Egypt            2
Nigeria          1
Liberia          1
Name: count, dtype: int64
```{python}
company['continent'].value_counts()
```
continent
Africa    25
Name: count, dtype: int64

We then visualize this information on a world map using latitude and longitude data to show the geographic distribution of these companies. The size of the dots represents profits. We find that while there are the profitable companies are mainly in Asia, Europe, and North America. The rest of the world generates very few profits.

```{python}
# Create a GeoDataFrame
geometry = [Point(lon, lat) for lon, lat in zip(company['longitude'], company['latitude'])]
gdf = gpd.GeoDataFrame(company, geometry=geometry, columns=['company', 'sales', 'profits', 'assets'])
```
```{python}
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
```
```{python}
#| fig-cap: "Top Companies by Sales on a World Map"
# Create a subplot
fig, ax = plt.subplots(figsize=(10, 10))

# Plot the world map
world.boundary.plot(ax=ax, color = "lightgrey")

# Plot your data on the map
gdf.plot(ax=ax, marker='o', color='red', markersize='profits', label='Companies')

# Add labels and legend
ax.set_title('Top Companies in Africa by Profits on a Map')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.legend()

plt.show()
```

Top Companies by Sales on a World Map

6.3 Is There a Correlation Between Company Size (in terms of assets) and Financial Performance?

Here, we analyze the relationship between the asset base of companies, which is a proxy for size and financial metrics such as sales, profits, and market value. The goal is to determine if larger companies tend to have higher sales, profits, and market value, or if there are exceptions. Overall, there appears to be a positive link between the size of a company and its financial performance in terms of sales, profits, and market value. However, this relationship varies by country.

```{python}
#| fig-cap: "Pairs plots for the Variables (C)"
sns.pairplot(company[["country", "assets", "sales", "profits", "market_value"]], hue = "country")
```

Pairs plots for the Variables (C)
```{python}
company[["assets", "sales", "profits", "market_value"]].corr()
```
assets sales profits market_value
assets 1.000000 0.503100 0.388891 0.273381
sales 0.503100 1.000000 0.637116 0.579276
profits 0.388891 0.637116 1.000000 0.766124
market_value 0.273381 0.579276 0.766124 1.000000

6.4 Are There Regional Patterns in Company Performance Based on Continent?

In this section, I group the data by country and analyze the average sales, profits, and assets for companies in each country. South African companies in the sample have by far the highest sales, profits, asset base, and market valuation .

```{python}
#| fig-cap: "Sales by Companies"
company.groupby("country")["sales"].sum().sort_values().plot(kind = "barh", title = "Total Sales of Top Companies in Africa by Country")
```
<Axes: title={'center': 'Total Sales of Top Companies in Africa by Country'}, ylabel='country'>

Sales by Companies

```{python}
#| fig-cap: "Total Profits of Top 2000 Companies globally by Continent"
company.groupby("country")["profits"].sum().sort_values().plot(kind = "barh", title = "Total Profits of Top Companies in Africa by Country")
```
<Axes: title={'center': 'Total Profits of Top Companies in Africa by Country'}, ylabel='country'>

Total Profits of Top 2000 Companies globally by Continent

```{python}
#| fig-cap: "Total Assets of Top 2000 Companies globally by Continent"
company.groupby("country")["assets"].sum().sort_values().plot(kind = "barh", title = "Total Assets of Top Companies in Africa by Country")
```
<Axes: title={'center': 'Total Assets of Top Companies in Africa by Country'}, ylabel='country'>

Total Assets of Top 2000 Companies globally by Continent

6.5 Which Companies, Contiments have the Highest Returns on Assets and Profit margins?

The return on assets is the ratio of profits to assets and captures the efficiency with which a company utilises its assets to generate profits. The net profit margin also captures how well a company is able to convert its sales into profits. The margin can also proxy the management efforts towards expenses management.

Dangote cement of Nigeria leads with a return on assets of 0.25. This is phenomenal!! The companies in the top 5 all have a return greater than 15%, which is an exceptional performance.

```{python}
company["roa"] = company["profits"] / company["assets"]
company["margin"] = company["profits"] / company["sales"]
```
```{python}
company['roa'].sort_values(ascending = False).head()
```
company
Dangote Cement      0.250000
Exxaro Resources    0.244898
Assore              0.238095
RMB Holdings        0.181818
Remgro              0.154930
Name: roa, dtype: float64

Next, I compute the median ROA for each continent. Nigeria leads, though the country has only one company in the sample. South Africa is in second place with a median ROA of 7%.

```{python}
company.groupby("country")['roa'].median().sort_values(ascending = False)
```
country
Nigeria         0.250000
South Africa    0.072727
Egypt           0.045849
Morocco         0.008872
Liberia         0.000000
Name: roa, dtype: float64

Let us do the same for profit margin. RMB Holdings has a profit margin of 1 which implies that the company generated profits equal to sales. Overall, the conversion of sales to profits is impressive among the industry leaders.

```{python}
company['margin'].sort_values(ascending = False).head()
```
company
RMB Holdings        1.000000
Exxaro Resources    0.800000
Remgro              0.647059
Dangote Cement      0.533333
Assore              0.312500
Name: margin, dtype: float64

Nigeria and Egypt leads in median profit margin. Again this could be die to the low number of companies from the coutries in this sample.

```{python}
company.groupby("country")['margin'].median().sort_values(ascending = False)
```
country
Nigeria         0.533333
Egypt           0.164815
South Africa    0.139423
Morocco         0.126161
Liberia         0.000000
Name: margin, dtype: float64

7 Conclusion

In this analysis, we use data from Kagle to illustrate the use of Python and Pandas in data analysis. From the analysis, we find that;

  1. Most large companies in the sample are located in South Africa.
  2. There is a positive relationship between company size and financial performance (sales, profits, market value). However, this relationship varies across countries.
  3. Companies from South Africa have the highest sales, profits, asset base, and valuation.
  4. Companies in Nigeria are more efficient in converting sales and assets into profits.

References

Borjigin, C. (2023). Python data science. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-7702-2
Kagle. (2023). Top 2000 companies globally. https://www.kaggle.com/datasets/joebeachcapital/top-2000-companies-globally.
VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. " O’Reilly Media, Inc.".