Counting Unique Companies by Country After Merging DataFrames

Merging DataFrames and Counting Companies by Country

As a data analyst or scientist, you often find yourself working with datasets that contain information about companies across different countries. In this article, we’ll explore how to merge two DataFrames containing company data from different sources and count the number of unique companies in each country.

Introduction

Let’s start with an example. Suppose we have two DataFrames, c1 and c2, which contain information about companies operating in the United States, China, United Kingdom, and Japan.

## Data of c1: 6 company in 4 countries.
   Country    Company
0   USA    Walmart
1   USA    Apple
2   China  CNPC 
3   China  State_grid 
4   UK     BP 
5   Japan  Toyota    

## Data of c2: 10 company in the same 4 countries.    
   Country    Company
0   USA    Walmart
1   USA    Apple
2   USA    Verizon
3   USA    JP_Morgan
4   China  CNPC 
5   China  China_Bank 
6   UK     BP 
7   Japan  Toyota
8   Japan  Honda
9   Japan  Sony

We can notice that some companies appear in both c1 and c2, while others are unique to each DataFrame. Our goal is to combine these two DataFrames into a single dataset and count the number of unique companies in each country.

Merging DataFrames

To merge c1 and c2, we can use the concat function from the pandas library, which concatenates two or more Series or DataFrames. We’ll assign the result to a new DataFrame, c.

c = pd.concat([c1, c2])

Removing Duplicate Companies

After merging the DataFrames, we have duplicate companies that appear in both c1 and c2. To remove these duplicates, we can use the drop_duplicates function with the subset parameter set to ['Country', 'Company'], which specifies the columns to consider for duplicate removal. We’ll also set reset_index to reset the index of the resulting DataFrame.

c = pd.concat([c1, c2]).drop_duplicates(subset=['Country','Company']).reset_index(drop=True)

Counting Unique Companies by Country

Now that we have removed duplicates from our merged DataFrame, we can count the number of unique companies in each country using the value_counts function.

print(c.Country.value_counts())
USA      4
China    3
Japan    3
UK       1
Name: Country, dtype: int64

Conclusion

In this article, we explored how to merge two DataFrames containing company data from different sources and count the number of unique companies in each country. By using the concat, drop_duplicates, and value_counts functions, we can efficiently analyze large datasets and extract meaningful insights.

Common Use Cases for Merging DataFrames

Merging DataFrames is a fundamental operation in data analysis, and it has many applications in various fields. Here are some common use cases:

Data fusion: When working with multiple datasets from different sources, merging them into a single dataset can help you combine and analyze the data more effectively.
Data matching: If you have two or more datasets that contain similar information, merging them can help you identify matching records and perform analysis on the combined data.
Data transformation: Merging DataFrames can be used as an intermediate step in data transformation pipelines. By combining multiple datasets, you can create a new dataset with transformed data.

Best Practices for Merging DataFrames

When working with DataFrames, it’s essential to follow best practices when merging them. Here are some guidelines:

Use the correct merge type: Different types of merges (inner, left, right, outer) have different implications for your analysis. Choose the appropriate merge type based on your goals and data structure.
Specify the join columns: When using an inner or left merge, specify the columns to use for joining the DataFrames. This ensures that you get only the matching records between the two datasets.
Use data types correctly: When merging DataFrames, ensure that the data types of the combined columns match those in the original datasets. Incorrect data types can lead to errors or unexpected results.

Conclusion

Merging DataFrames is an essential operation in data analysis, and it has many applications in various fields. By understanding how to merge DataFrames effectively and following best practices, you can unlock new insights from your data and improve your analytical skills.

Last modified on 2024-10-23