Splitting/Joining

Splitting and joining are essential processes for organizing data. Splitting involves breaking a single column into multiple parts, such as separating a Customer Name column into First Name and Last Name. This makes the data more detailed and easier to analyze.

Joining, on the other hand, combines two or more datasets based on a common key, like merging columns from the customer table with columns in the loan table using Customer ID as the common key. Splitting helps extract specific details from a single dataset, while joining creates a complete view by linking related information from multiple datasets.

Example

Let us consider a dataset where customer names are stored in a single column as Customer Name. To make the data more useful, we can split this column into First Name and Last Name.

Original Dataset:

Loan ID	Customer Name	Loan Amount
LOAN001	Alice Johnson	10,000
LOAN002	Bob Smith	15,000
LOAN003	Carol O’Reilly	20,000

After Splitting:

Loan ID	First Name	Last Name	Loan Amount
LOAN001	Alice	Johnson	10,000
LOAN002	Bob	Smith	15,000
LOAN003	Carol	O’Reilly	20,000