Pandas vs R

Overview

R Language

Stacks3.9K

Followers1.9K

Votes418

Pandas

Stacks2.1K

Followers1.3K

Votes23

Pandas vs R: What are the differences?

Data Manipulation: One key difference between Pandas and R is in the way they handle data manipulation. Pandas is a Python library that provides data structures and functions for efficiently manipulating and analyzing data, while R is a programming language specifically designed for statistical computing and graphics. Pandas uses a DataFrame object, which is similar to a table or spreadsheet, to store and manipulate data, while R uses data frames and other data structures like matrices and arrays.
Syntax: Another difference between Pandas and R is the syntax they use. Pandas uses Python syntax, which is known for its simplicity and readability. This makes it easier for programmers to write and understand code. On the other hand, R has its own syntax, which can be more complex and harder to learn for programmers who are not familiar with the language.
Integration with other libraries: Pandas is part of the larger Python ecosystem, which means it can easily be integrated with other libraries and tools commonly used in data analysis and machine learning, such as NumPy and scikit-learn. This allows for seamless integration and interoperability between different libraries. In contrast, R has its own ecosystem of libraries and tools, which may not always integrate as smoothly with libraries from other programming languages.
Visualization: Pandas provides limited options for data visualization compared to R. While Pandas has built-in plotting functions, it often requires additional libraries, such as Matplotlib, to create more complex visualizations. R, on the other hand, has a wide range of powerful and flexible packages for data visualization, such as ggplot2 and lattice, which allow for advanced plotting techniques and highly customizable graphics.
Community Support: Both Pandas and R have strong and active communities, providing support, documentation, and resources for users. However, Python as a programming language has a larger and more diverse community compared to R. This means that there are more online forums, tutorials, and resources available for Python and Pandas users, making it easier to find help and solutions to common problems.
Speed and Performance: Pandas is built on top of the high-performance NumPy library, which allows for efficient computation and processing of large datasets. This makes Pandas generally faster in terms of data manipulation and analysis compared to R. R, on the other hand, is slower in certain operations due to its interpreted nature and less optimized implementation. However, R has specialized libraries, such as data.table and dplyr, which are specifically designed for high-speed data manipulation.

In summary, Pandas and R differ in their data manipulation techniques, syntax, integration with other libraries, visualization capabilities, community support, and performance characteristics. While Pandas is known for its simplicity and integration with the Python ecosystem, R offers more advanced visualization options and specialized libraries for high-speed data manipulation.

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Detailed Comparison

R Language	Pandas
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.	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
-	Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Statistics
Stacks 3.9K	Stacks 2.1K
Followers 1.9K	Followers 1.3K
Votes 418	Votes 23
Pros & Cons
Pros 86 Data analysis 64 Graphics and data visualization 55 Free 45 Great community 38 Flexible statistical analysis toolkit Cons 6 Very messy syntax 4 Tables must fit in RAM 3 Arrays indices start with 1 2 Messy syntax for string concatenation 2 No push command for vectors/lists	Pros 21 Easy data frame management 2 Extensive file format compatibility
Integrations
No integrations available	Python

What are some alternatives to R Language, Pandas?

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Related Comparisons

Pandas vs R: What are the differences?

Data Manipulation: One key difference between Pandas and R is in the way they handle data manipulation. Pandas is a Python library that provides data structures and functions for efficiently manipulating and analyzing data, while R is a programming language specifically designed for statistical computing and graphics. Pandas uses a DataFrame object, which is similar to a table or spreadsheet, to store and manipulate data, while R uses data frames and other data structures like matrices and arrays.
Syntax: Another difference between Pandas and R is the syntax they use. Pandas uses Python syntax, which is known for its simplicity and readability. This makes it easier for programmers to write and understand code. On the other hand, R has its own syntax, which can be more complex and harder to learn for programmers who are not familiar with the language.
Integration with other libraries: Pandas is part of the larger Python ecosystem, which means it can easily be integrated with other libraries and tools commonly used in data analysis and machine learning, such as NumPy and scikit-learn. This allows for seamless integration and interoperability between different libraries. In contrast, R has its own ecosystem of libraries and tools, which may not always integrate as smoothly with libraries from other programming languages.
Visualization: Pandas provides limited options for data visualization compared to R. While Pandas has built-in plotting functions, it often requires additional libraries, such as Matplotlib, to create more complex visualizations. R, on the other hand, has a wide range of powerful and flexible packages for data visualization, such as ggplot2 and lattice, which allow for advanced plotting techniques and highly customizable graphics.
Community Support: Both Pandas and R have strong and active communities, providing support, documentation, and resources for users. However, Python as a programming language has a larger and more diverse community compared to R. This means that there are more online forums, tutorials, and resources available for Python and Pandas users, making it easier to find help and solutions to common problems.
Speed and Performance: Pandas is built on top of the high-performance NumPy library, which allows for efficient computation and processing of large datasets. This makes Pandas generally faster in terms of data manipulation and analysis compared to R. R, on the other hand, is slower in certain operations due to its interpreted nature and less optimized implementation. However, R has specialized libraries, such as data.table and dplyr, which are specifically designed for high-speed data manipulation.

Pandas vs R

Overview