Pandas vs SQL - What's The Difference?
SQL, or Structured Query Language, is a powerful language that's specifically designed for working with data stored in relational databases. With SQL, you can write queries that allow you to select, filter, and transform data in order to extract the information you need. SQL is widely used in industries such as finance, healthcare, and retail, and it's a must-know skill for anyone who works with data in these fields.
On the other hand, Pandas is a popular library for Python that provides similar functionality to SQL for working with data in a tabular format. With Pandas, you can use Python to perform complex data manipulation tasks, including filtering, aggregating, and transforming data. Pandas is often used in conjunction with other scientific computing libraries in Python, such as NumPy and SciPy, to perform advanced data analysis tasks.
So, how do SQL and Pandas compare? Well, they both have their strengths and weaknesses. Let's find out.
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