Julia vs Python: Which is best for data science?

Python has become a data science and machine learning mainstay, while Julia was built from the ground up to do the job

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(Image: Python, Julia)

Among the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient.

covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient.

But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”—Python isn’t fast or convenient enough. Python represents a trade-off, good for some parts of data analytics work but terrible for others.

What is the Julia language?
Created in 2009 by a four-person team and unveiled to the public in 2012, Julia is meant to address the shortcomings in Python and other languages and applications used for scientific computing and data processing. “We are greedy,” they wrote. They wanted more: 

“We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.”

Here are some of the ways Julia
implements those aspirations:

• Julia is compiled, not interpreted. For faster runtime performance, Julia is just-in-time
  (JIT) compiled using the LLVM compiler framework. At its best, Julia can
  approach or match the speed of C.
• Julia is interactive. Julia includes a REPL (read-eval-print loop), or
  interactive command line, similar to what Python offers. Quick one-off scripts
  and commands can be punched right in.
• Julia has a straightforward syntax. Julia’s syntax is similar to Python’s—terse, but also
  expressive and powerful.
• Julia combines the benefits of
  dynamic typing and static typing. You
  can specify types for variables, like “unsigned 32-bit integer.” But you can
  also create hierarchies of types to allow general cases for handling variables
  of specific types—for instance, to write a function that accepts integers without
  specifying the length or signing of the integer. You can even do without typing
  entirely if it isn’t needed in a particular context.
• Julia can call Python, C, and Fortran
  libraries. Julia can
  interface directly with external libraries written in C and Fortran. It’s also
  possible to interface with Python code by way of the PyCall library,
  and even share data between Python and Julia.
• Julia supports metaprogramming. Julia programs can generate other Julia programs, and even modify
  their own code, in a way that is reminiscent of languages like Lisp.
• Julia has a full-featured debugger. Julia 1.1 introduced a debugging suite,
  which executes code in a local REPL and allows you to step through the results,
  inspect variables, and add breakpoints in code. You can even perform
  fine-grained tasks like stepping through a function generated by code.

Perfect for IT, Python simplifies many kinds of work, from system automation to working in cutting-edge fields like machine learning.

Julia language advantages
Julia was designed from the start for scientific and numerical computation. Thus it is no surprise that Julia has many features advantageous for such use cases:

• Julia is fast: Julia’s JIT compilation and type declarations mean it
  can routinely beat “pure,” unoptimised Python by orders of magnitude. Python
  can be made faster by way of external
  libraries, third-party JIT compilers (PyPy), and optimisations with tools like
  Cython, but Julia is designed to be faster right out of the gate.
• Julia has a math-friendly syntax: A major target audience for Julia is users of
  scientific computing languages and environments like Matlab, R, Mathematica,
  and Octave. Julia’s syntax for math operations looks more like the way math
  formulas are written outside of the computing world, making it easier for
  non-programmers to pick up on.
• Julia has automatic memory management:
  Like Python, Julia doesn’t burden the
  user with the details of allocating and freeing memory, and it provides some
  measure of manual control over garbage collection. The idea is that if you
  switch to Julia, you don’t lose one of Python’s common conveniences.
• Julia offers superior parallelism. Math and scientific computing thrive when you can make
  use of the full resources available on a given machine, especially multiple
  cores. Both Python and Julia can run operations in parallel. However,
  Python’s methods for parallelising operations often require data to be serialised
  and deserialised between threads or nodes, while Julia’s parallelisation is
  more refined. Further, Julia’s parallelisation syntax is less top-heavy than
  Python’s, lowering the threshold to its use.

Python advantages
Although Julia
is purpose-built for data science, whereas Python has more or less evolved into
the role, Python offers some compelling advantages to the data scientist. Some
of the reasons “general purpose” Python may be the better choice for data
science work:

• Julia arrays are 1-indexed: This might seem like an obscure issue, but it’s a potentially
  jarring one. In most languages, Python and C included, the first element of an
  array is accessed with a zero—e.g., string[0] in Python for the first character in a string. Julia
  uses 1 for the first element in an array. This isn’t an arbitrary decision;
  many other math and science applications, like Mathematica, use 1-indexing, and
  Julia is intended to appeal to that audience. It’s possible to support
  zero-indexing in Julia with an experimental feature, but 1-indexing by
  default may stand in the way of adoption by a more general-use audience with ingrained
  programming habits.
• Python is mature: The Julia language is young. Julia has been under
  development only since 2009, and has undergone a fair amount of feature churn
  along the way. By contrast, Python has been around for almost 30 years.
• Python has far more third-party
  packages: The breadth and usefulness of
  Python’s culture of third-party packages remains one of the language’s biggest
  attractions. Again, Julia’s relative newness means the culture of software
  around it is still small. Some of that is offset by the ability to use existing
  C and Python libraries, but Julia needs libraries of its own to thrive.
• Python has millions of users: A language is nothing without a large, devoted, and
  active community around it. The community around Julia is enthusiastic and
  growing, but it is still only a fraction of the size of the Python community.
  Python’s huge community is a huge advantage. 
• Python is getting faster: Aside from gaining improvements to the Python
  interpreter (including improvements to multi-core and parallel processing),
  Python has become easier to speed up. The mypyc project translates type-annotated
  Python into native C, far less clunkily than Cython. It typically yields
  four-fold performance improvements, and often much more for pure mathematical
  operations.

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