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NumPy vs R: What are the differences?
NumPy: Fundamental package for scientific computing with Python. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases; R: 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.
NumPy belongs to "Data Science Tools" category of the tech stack, while R can be primarily classified under "Languages".
NumPy is an open source tool with 11.1K GitHub stars and 3.67K GitHub forks. Here's a link to NumPy's open source repository on GitHub.
Instacart, Zalando, and Thumbtack are some of the popular companies that use R, whereas NumPy is used by Instacart, Suggestic, and Twilio SendGrid. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to NumPy, which is listed in 63 company stacks and 34 developer stacks.
Pros of NumPy
- Great for data analysis9
- Faster than list2
Pros of R Language
- Data analysis83
- Graphics and data visualization62
- Free53
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Preferred Medium6
- Shiny interactive plots6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
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Cons of NumPy
Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0