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What are the best programming languages for data science?

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The Best 1 of 36 OptionsWhy?

36 Options ConsideredPriceCurrent stable versionGZipped size
Excellent
Python
3.6.422.5MB
Julia
0.6–
Rust
1.24.1–
Clojure
1.93.7Mb, requires JAVA
Scala
2.12.4–
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Excellent

Python

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Endolith
gilch
Top Pro

SciPy (Scientific Python library)

•••
Laura Kyle
Vikram Bhaskar
Top Pro

Rich set of libraries and extensions

•••
Reduces the lines of code required to get stuff done.
Alison Stanton
Top Pro

Full programming language, unlike R

•••
Allows for more rapid deployment of solutions.
Endolith
gilch
Top Pro

Numpy (Numerical Python library)

•••
Monika
Endi Sukaj
Top Pro

Clean syntax

•••
Python's syntax is very clear and readable, making it excellent for beginners. The lack of extra characters like semicolons and curly braces reduces distractions, letting beginners focus on the meaning of the code. Significant whitespace also means that all code is properly and consistently ind...
Barbara Glowa
Top Pro

Portable

•••
Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
Alison Stanton
Top Pro

Pandas Library

•••
Monika
gilch
Top Pro

Easy operator overloading

•••
Via special "dunder" methods.
Barbara Glowa
Top Pro

Databases

•••
Python provides interfaces to all major commercial databases.
TallVarmanpaz
Top Pro

Very similar to pseudo-code

•••
When learning Computer Science concepts such as algorithms and data structures, many texts use pseudo-code. Having a language such as Python whose syntax is very similar to pseudo-code is an obvious advantage that makes learning easier.
TallVarmanpaz
Top Pro

Cross-platform

•••
Installs and works on every major operating systems if not already installed by default (Linux, macOS).
TallVarmanpaz
Top Pro

Active and helpful community

•••
Python has an active and helpful community, such as the comp.lang.python Google Groups, StackOverflow, reddit, etc.
Monika
TallVarmanpaz
Top Pro

Good documentation

•••
The Python community has put a lot of work into creating excellent documentation filled with plain English describing functionality. Contrast this with other languages, such as Java, where documentation often contains a dry enumeration of the API. As a random example, consider GUI toolkit documen...
TallVarmanpaz
Top Pro

Lots of tutorials

•••
Python's popularity and beginner friendliness has led to a wealth of tutorials and example code on the internet. This means that when beginners have questions, they're very likely to be able to find an answer on their own just by searching. This is an advantage over some languages that are...
gilch
Top Pro

SageMath

•••
Belle
Steven Sagaert
Top Con

Slow, unless you use wrapped native libraries or compile to native code (Cython, Numba).

•••
Endi Sukaj
Belle
Steven Sagaert
Top Con

Very limited support for functional programming which is important for data science

•••
Laura Kyle
VersatileFuamnach
Top Con

Lack of statistical and machine learning algorithms

•••
Compared to programming languages like R, which were created for Data Science, Python is missing a lot of old and new statistical approaches.
See 12 more ProsSee 16 more Pros & Cons
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35 Other Options Considered

Julia

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Michael Borregaard
Top Pro

Multiple dispatch creates very clear code

•••
thermoplastics
Top Pro

Nice regular syntax

•••
Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes...
Jonathan
Tim Lebel
Top Pro

Fast computation

•••
Certain benchmarks suggest it is capable of outperforming Python and even C (in certain situations where FORTRAN libraries can be utilized).
thermoplastics
Top Pro

Great standard REPL

•••
Out of the box Julia has a very good Read-Eval-Print-Loop, which both completes functions and types, as well as completion based on history of previous statements. It integrates well with the shell and has an excellent online help system.
gilch
Top Pro

Easy Python interop

•••
Take advantage of Python's larger ecosystem.
MagicalCaerIbormeith
Top Pro

Lisp inspired macros

•••
MagicalCaerIbormeith
Top Pro

Easy parallel computing

•••
MagicalCaerIbormeith
Top Pro

Though dynamically typed, can use type annotations to improve performance and make some errors easier to catch

•••
Alex Libman
Top Pro

Copyfree license (unlike Python and R)

•••
김태근
Top Pro

Take advantages of matlab, R, Python

•••
gilch
thermoplastics
Top Con

Young language with limited support

•••
Julia was released in 2012. Due to its short existence, there is a limited amount of support for the language. Very few libraries exist as of yet, and the community is still quite small (though growing quickly).
Steven Sagaert
Top Con

Slow startup of programs due to compilation of the programs & (some of) its libs at startup

•••
Bryan
Steven Sagaert
Top Con

Tooling & libs not yet up to par with others like R or Python

•••
Lacks tools like R's Shiny for easy web applications/dashboards. The library ecosystem also doesn't have the breadth yet of R or Python (which is normal). The dataframe/datatable in Julia & data wrangling isn't also up to par yet with R data.frame/data.table/tibble & dplyr.
David Barton
gilch
thermoplastics
Top Con

1-based array and column major

•••
This design probably comes from Matlab, but makes it unnatural to interface C and C++ and Python. However, arbitrary indexing is possible with later versions of Julia (but the norm is still 1-based indexing).
Seth Petersen
GloriousShu
Top Con

Plotting lib is not good

•••
See 13 more Pros & ConsSee 13 more Pros & Cons

Rust

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gilch
PleasantSirsir
Top Pro

Threads without data races

•••
Unique ownership system guarantees a mutable datum to be owned and mutated by only one thread at a time, so there's no data race, and this guarantee is checked at compile time statically. Which means easy multi-threading. Of course, immutable data can be shared among multiple threads freely.
PleasantSirsir
Top Pro

Built-in concurrency

•••
Rust has built-in support for concurrency.
PleasantSirsir
Top Pro

Support for macros

•••
When you identify a part of your code which gets repeated often, which you cannot abstract using functions or classes, you can use Rust's built-in Macros.
PleasantSirsir
Top Pro

Generics support

•••
You don't have to write same array and dictionary classes hundreds and thousands times for strong type check by compiler.
PleasantSirsir
Top Pro

Catch errors at compile-time

•••
Since Rust is statically typed, you can catch multiple errors during compile time. This is extremely helpful with debugging, especially compared with dynamically typed languages that may fail silently during runtime.
PleasantSirsir
Top Pro

Supports cross compilation for different architectures

•••
Since Rust 1.8 you can install additional versions of the standard library for different targets using rustup/multirust. For example: $ rustup target add x86_64-unknown-linux-musl Which then allows for: $ cargo build --target x86_64-unknown-linux-musl
PleasantSirsir
Top Pro

Compiles to machine code allowing for extra efficiency

•••
Rust uses LLVM as a backend, among other things this allows Rust code to compile down to machine languages. This allows developers to write programs that run as efficiently as possible.
PleasantSirsir
Top Pro

Makes developers write optimal code

•••
Rust is a modern programming language written around systems. It was designed from the ground up this way. It's language design makes developers write optimal code almost all the time, meaning you don't have to fully know and understand the compiler's source code in order to optimize yo...
PleasantSirsir
Top Pro

Easy to write understandable code

•••
While not as verbose as Java, it still is much more verbose than languages like Go and Python. This means that the code is very explicit and easy to understand.
Endi Sukaj
thermoplastics
Alexander Arutinyants
Top Pro

Developers of Rust consider data science support in Rust important and work on it

•••
thermoplastics
彭冠文
Top Con

Beginners could be stumped by some special concepts like Ownership, etc and spend extra time on it

•••
PleasantSirsir
Top Con

Hard to find learning resources or libraries

•••
Because it's still a relatively new language, Rust does not enjoy a following as large as other languages/environments. Rust development has also been rather volatile for a long time during the beginning of the development of the language adding to this issue. Because of the small community,...
Steven Sagaert
Top Con

Rust is a systems language not a data science language

•••
See 11 more Pros & ConsSee 11 more Pros & Cons

Clojure

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gilch
Top Pro

Incanter

•••
Know any negative aspects of this option?

Scala

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Endi Sukaj
TactfulMefitis
Top Pro

Algebraic types

•••
Bryan
Steven Sagaert
Top Pro

Several data science libraries available (Scala or Java based)

•••
Bryan
Steven Sagaert
Top Pro

Modern object-functional language that allows high level abstraction

•••
Bryan
Steven Sagaert
Top Pro

Based on the JVM hence cross platform and much tooling

•••
Bryan
Steven Sagaert
Top Pro

It's the language of Spark, the leading big data platform

•••
thermoplastics
Bryan
Steven Sagaert
Top Con

Complex language

•••
Not for beginners.
See 4 more ProsSee 4 more Pros

R

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Endi Sukaj
Павел Моисеев
Top Pro

Rich choice of high quality libraries

•••
Be it data manipulation or plotting there is always a wide variety at CRAN available.
Alex
Steven Sagaert
Top Pro

RStudio is a great IDE

•••
It's free, lightweight, and easy to use, making working with R a pleasure.
Steven Sagaert
Top Pro

knitr + Rmarkdown for (dynamic) documents

•••
Endi Sukaj
Павел Моисеев
Top Pro

Open source

•••
R is available under an open-source license, which means that anyone can download and modify the code.
IndependentKinichAhau
Top Pro

ggplot2

•••
Steven Sagaert
Top Pro

Shiny for web applications/dashboards

•••
Steven Sagaert
Ryan
Top Pro

Microsoft is now involved

•••
Which means more high quality tooling (support for R in Visual Studio) & libs & integrations (e.g. R in SQLServer) & cloud solutions for scalability beyond single core & single host (R server , Spark via sparkR/sparklyr lib,... all on Azure supported by MS) . It also means a lot...
Steven Sagaert
Top Pro

There are so many R packages there's also always already a package for an algorithm described in a paper.

•••
Monika
김태근
Top Con

Too slow

•••
R is too slow to handle big data.
Steven Sagaert
Top Con

The library ecosystem (mostly CRAN but also github, BioConductor & others) is rather fragmented

•••
There are over 10000 R packages and rising and there are many packages with overlapping functionality. It's not always clear which package is "best" for your needs. The situation is getting better thanks to Hadley Wickham who has rewritten a lot of core functionality from R base &...
See 8 more Pros & ConsSee 8 more Pros & Cons

Lua

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WhiteLilac
Top Pro

Great documentation

•••
The official Lua documentation is very helpful and thorough. There are also a large number of online resources or books with lots of helpful information for beginners and advanced users alike.
WhiteLilac
Top Pro

Fast

•••
Lua's performance compares very well to other languages, If performance needs to be further improved you can: Implement critical parts in C Use the LuaJIT compiler. The LuaJIT compiler is a drop in replacement for the stock compiler and provides significant performance improvements. Fr...
Know any negative aspects of this option?
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SAS

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Haskell

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DetailedTiddalik
Top Pro

Hand-writeable concise syntax

•••
Conciseness of Haskell lets us to write the expression on the whiteboard or paper and discuss with others easily. This is a strong benefit to learn FP over other languages.
DetailedTiddalik
Top Pro

Easy to read

•••
Haskell is a very terse language, particularly due to its type inference. This means there's nothing to distract from the intent of the code, making it very readable. This is in sharp contrast to languages like Java, where skimming code requires learning which details can be ignored. Haskell...
DetailedTiddalik
Top Pro

Mathematical consistency 

•••
As Haskell lends itself exceedingly well to abstraction, and borrows heavily from the culture of pure mathematics, it means that a lot more code conforms to very high-level abstractions. You can expect code from vastly different libraries to follow the same rules, and to be incredibly self-consiste...
DetailedTiddalik
Top Pro

Forces you to learn pure functional programming

•••
It is pure and does not mix other programming paradigms into the language. This forces you to learn functional programming in its most pure form. This avoids you falling back on old habits and learn an entirely new way to program.
Know any negative aspects of this option?
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F#

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Robert McAlery
Top Pro

Full access to all .NET libraries

•••
Robert McAlery
Top Pro

Native support for Algebraic Data Types

•••
Algebraic Data Types enables the creation of complex data structures in a very readable and reliable manner.
Robert McAlery
Top Pro

High performance

•••
Has the same performance profile as C#.
Robert McAlery
Top Pro

Easy access to R libraries via the R Type Provider

•••
Robert McAlery
Top Pro

Simple ML-style expression based syntax

•••
Steven Sagaert
Top Con

Only one worthy big ML lib available (Accord.net) and it's not even in F# but C#

•••
Steven Sagaert
Top Con

No real data science/ML community in .NET & F#

•••
See 5 more Pros & ConsSee 5 more Pros & Cons

J

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gilch
Top Pro

Free and open source

•••
J is licensed under the GNU GPLv3.
gilch
Top Pro

High performance

•••
Despite being interpreted, the array orientation gives the runtime good locality of reference for good cache performance and the repeated operators give it natural parallelism.
gilch
Top Pro

Array-oriented

•••
J's verbs (operators) naturally work on multidimensional arrays with no need for manual looping. This makes J especially good at data analysis, and was the inspiration for Numpy's arrays.
gilch
Top Pro

Notation as a tool of thought

•••
J's notation is very powerful. Once you learn it well, it allows you to to fit more into your limited human working memory.
Know any negative aspects of this option?
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Bash/awk/sed

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Scheme

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Java

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schlaumeier
Top Pro

Broad number of skilled developer avaialbe

•••
Know any negative aspects of this option?

Common Lisp

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Hy

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Nim

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Solon
Top Pro

Great portability and optimization

•••
Nim gains portability and optimization because it compiles to C, which offers a lot of choices from modern C compilers.
Solon
Top Pro

Cross-platform

•••
Works on Windows, macOS, and Linux.
Know any negative aspects of this option?
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Scilab

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WhiteLilac
Steven Sagaert
Top Pro

Free

•••
WhiteLilac
Steven Sagaert
Top Con

Misses some basic functionality

•••
For example like how to connect to a relational DB.
Alex
WhiteLilac
Steven Sagaert
Top Con

Small community

•••
WhiteLilac
Steven Sagaert
Top Con

Doesn't have a lot of third party packages

•••
See 2 more ConsSee 2 more Cons

APL

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GNU Octave

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gilch
Top Pro

Matlab compatibility

•••
The languages are so similar that it's easy to port Matlab libraries.
김태근
Top Con

Too slow & unstable

•••

Wolfram Mathematica

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Laura Kyle
Steven Sagaert
Top Pro

Functional & rule based programming language

•••
Francisco
Steven Sagaert
Laura Kyle
Top Pro

Rich ecosystem of libraries

•••
Because all the libraries in Mathematica are developed by Wolfram itself, they are highly coherent and interoperable.
Laura Kyle
Steven Sagaert
Top Pro

Very high level, can solve complex problems in a few lines

•••
Laura Kyle
Steven Sagaert
Top Pro

Beautiful graphics

•••
Laura Kyle
Steven Sagaert
Top Pro

Excellent for symbolic computing

•••
e.g. solving differential equations analytically.
Steven Sagaert
Top Pro

Mathematica invented notebooks more than 20 years ago, long before iPython cloned it

•••
Laura Kyle
Steven Sagaert
Top Con

Not free or open source, expensive for commercial non-academic use

•••
Nuance: you can experiment with it for free now in the Wolfram cloud.
김태근
Top Con

Slow & no generic programming

•••
See 6 more Pros & ConsSee 6 more Pros & Cons

167 Community Recommendations

gilch
Martin Valgur
schlaumeier
Sergio García
Richard Eng
Steffen Schneider
Luis Lopez
Eugene A. Simonenko
ElectronWill
Endolith
Stefan Karpinski
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LearnedKaskuh
LearnedKaskuh
Contributor for 4 months
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Julia
Recommended 4 months ago
Nice language
Pro
 Nice regular syntax
Pro
 Great standard REPL
Con
 1-based array and column major
Pro
 Fast computation
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FascinatingBunene
FascinatingBunene
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2
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Clojure
4 months ago
Not famous or lacks features essential for data science
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Antonello L.
Antonello
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Julia
Recommended 5 months ago
je
Con
 1-based array and column major
Pro
 Fast computation
Pro
 Multiple dispatch creates very clear code
Pro
 Easy Python interop
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