Pandas vs SQL: When Data Scientists Should Use Each Tool
These days, data scientists have a long list of toolkits, platforms, and libraries to choose from, but they prefer to use Panda and SQL. Both of these tools are valuable and essential for them. Some of their functions and features are similar, but they cannot be considered as similar tools. The tools have specific roles in the data workflow. Thus today, we will take a look at when to use Panda and when to SQL. Have a look!
# What is Panda?
Panda is a popular python-based open-source data analysis and manipulation tool. It is considered a powerful, flexible, and easy-to-use tool that helps in analyzing data. Using pandas, data scientists and analysts can generate, manipulate and combine different data frames.
# What is SQL?
Structured Query Language (SQL) is a domain-specific programming language that is widely used to manage data. It is a feature-rich language that enables users to handle information with the help of tables. Moreover, SQL assists users in manipulating data in a very organized way. It is an excellent tool that plays a vital role in extracting data from the database.
# When to Use Panda & When to use SQL?
Well, SQL language is used when the user has to work with large, separate tables and when there’s no upper limit of data. On the other hand, if we talk about Pandas, it deals with a modestly extensive database. Furthermore, it is best to use this tool to create calculated fields from existing features. You can divide the elements into two columns and then process data from them.
On the Whole:
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