Pandas vs R: 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, R is detailed as "A language and environment for statistical computing and graphics". R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
Pandas can be classified as a tool in the "Data Science Tools" category, while R is grouped under "Languages".
"Easy data frame management" is the top reason why over 16 developers like Pandas, while over 58 developers mention "Data analysis " as the leading cause for choosing R.
Pandas is an open source tool with 20.2K GitHub stars and 8K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
According to the StackShare community, R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to Pandas, which is listed in 73 company stacks and 49 developer stacks.
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What is Pandas?
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