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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.
Pros of Pandas
- Easy data frame management21
- Extensive file format compatibility1
Pros of Pandasql
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What is Pandas?
What is Pandasql?
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What companies use Pandas?
What companies use Pandasql?
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What tools integrate with Pandasql?
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