Question

What is Data Merging? Mention one scenario where it is required.

Hint:

\textbf{Remember:} Data merging = Combining datasets using a common key.

Answer 1

Data Merging and Its Scenario:

Definition:
Data merging is the process of combining two or more datasets into a single, unified dataset based on a common column or key. It allows analysts to consolidate information from different sources to perform comprehensive analysis.

Key Points:
1️⃣ Merging can be done using various types of joins:
• Inner Join: Keeps only matching records from both datasets.
• Left/Right Join: Keeps all records from one dataset and matches from the other where possible.
• Outer Join: Combines all records from both datasets, filling missing values where there is no match.

2️⃣ Ensures consistency and avoids duplication when combining data from multiple sources.
3️⃣ Typically used in data preprocessing in Python using libraries like pandas.

Scenario:
Suppose a company has two datasets:
• Dataset A: Employee personal details (Employee ID, Name, Department)
• Dataset B: Employee salary details (Employee ID, Salary, Bonus)
To perform a complete analysis of employees including both personal and salary information, we merge the two datasets on the common column Employee ID.

Example in Python:

import pandas as pd

# Employee personal details
df1 = pd.DataFrame({'EmployeeID': [101, 102, 103],
                    'Name': ['Alice', 'Bob', 'Charlie'],
                    'Department': ['HR', 'Finance', 'IT']})

# Employee salary details
df2 = pd.DataFrame({'EmployeeID': [101, 102, 103],
                    'Salary': [50000, 60000, 55000],
                    'Bonus': [5000, 6000, 5500]})

# Merging on EmployeeID
merged_df = pd.merge(df1, df2, on='EmployeeID')
print(merged_df)

Conclusion:
Data merging is essential for combining related datasets to perform unified analysis, ensuring that all relevant information is available in one dataset.