Pandas vs Pandasql: What are the differences?
Developers describe Pandas as "High-performance, easy-to-use data structures and data analysis tools for the Python programming language". Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. On the other hand, Pandasql is detailed as "Make python speak SQL". pandasql allows you to query pandas DataFrames using SQL syntax. It works similarly to sqldf in R. pandasql seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or pandas.
Pandas belongs to "Data Science Tools" category of the tech stack, while Pandasql can be primarily classified under "Database Tools".
Pandas and Pandasql are both open source tools. It seems that Pandas with 20.2K GitHub stars and 8K forks on GitHub has more adoption than Pandasql with 738 GitHub stars and 109 GitHub forks.
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Pandas?
What is Pandasql?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Pandasql?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Pandasql?
Sign up to get full access to all the tool integrationsMake informed product decisions