Introduction
Both SQL and pandas play a pivotal role in data analysis and manipulation. SQL, known for its capacity to interact with large relational databases, and pandas, a Python library that makes data manipulation and analysis effortless, are tools that all data scientists should be familiar with. To give a better understanding of these two tools, we'll delve into their characteristics and use cases, supplemented by examples.

What is SQL?
SQL, which stands for Structured Query Language, is a well-established standard for managing relational database management systems (RDBMS). It is designed to handle the extraction, updating, and deletion of data within databases.
One of SQL's main strengths is its ability to execute complex queries, which makes it easy to combine data from multiple tables and aggregate data. Moreover, as SQL operates directly in a database system, it can process operations swiftly, especially when working with indexed columns
For instance, if you want to select employees who are older than 25 from an employees table, the SQL query would be:
SELECT name, age FROM employees WHERE age > 25;
What is Pandas?
Pandas, on the other hand, is a Python library that excels at handling and analyzing data. Its data structures, Series and DataFrame, are efficient and easy to use.
Pandas can handle a variety of data types, missing data, and also provides methods for filtering, aggregating, and visualizing data. This makes pandas a fantastic tool for data cleaning, transformation, and exploratory analysis.
For example, to filter rows from a DataFrame in pandas where the employee's age is over 25, you would write:
df = pandas.read_csv('employees.csv')
df_filtered = df[df['age'] > 25]