One may switch from MATLAB to Python because Python deals much better with complexity i.e. easier to write, debug and maintain complex code. One of the good reasons for that is, Python is a general purpose language rather than a matrix-manipulation language. Because of this, entities like non-numerical arrays , strings and associative arrays are first-class constructs in Python.
We regularly hear of people and whole research groups that transition from Matlab to Python. The scientific Python ecosystem is maturing fast and Python is an appealing alternative, because it’s free, open source, and becoming ever more powerful. Let's explore of them in details.
Ecosystems of Matlab and Python
Python, by definition, is a programming language. The most common implementation is that in C also known as CPython and is what is mostly referred to as Python. Apart from the programming language and interpreter, Python also consists of an extensive standard library. This library is aimed at programming in general and contains modules for OS specific stuff, threading, networking, databases etc.
Matlab is a commercial numerical computing environment and programming language. The concept of Matlab refers to the whole package, including the IDE. The standard library does not contain as much generic programming functionality, but does include matrix algebra and an extensive library for data processing and plotting. For extra functionality the Mathworks provides toolkits (but these cose you extra).
To do scientific computing in Python, you need additional packages (e.g. Numpy, Scipy, Matplotlib). Additionally, you’ll need an IDE. Many pythoneers come from a Linux environment and use a Python shell and an editor (like vi or Emacs), but people coming from Matlab prefer a feature-rich IDE. Now today there are many handful and powerful IDE available for Python such as PyCharm , Spyder , Jupyter Notebook and many more.
Python packages is being developed by a different (but often overlapping) group of people, who are also users of the packages. Many packages are available for different purposes. In this open source ecosystem most packages are driven by a handful of core developers, but many of a package users contribute to the development by reporting issues, helping with documentation, and making small improvements to the code.
Advantages of Matlab
Let's pick up some of the advantages we can get from Matlab :
- It has a solid amount of functions.
Simulink is a product for which
there is no good alternative yet.
- It might be easier for beginners, because the package includes all
you need, while in Python you need to install extra packages and an
IDE.
- It has a large scientific community; it is used on many universities
(although few companies have the money to buy a license).
The problem with Matlab
Here we do not intend to make Matlab look bad. I used to love Matlab myself. However, i think that Matlab has a few fundamental shortcomings. Most of these arise from its commercial nature:
The algorithms are proprietary, which means you can not see the code
of most of the algorithms you are using and have to trust that they
were implemented correctly.
Matlab is quite expensive, which means that code that is written in
Matlab can only be used by people with sufficient funds to buy a
license.
Naturally, the Mathworks puts restrictions on code portability, the
ability to run your code on someone elses computer. You can run your
compiled application using the Matlab Component Runtime (MCR), but
your portable app must exactly match the version of the installed
MCR, which can be a nuisance considering that Matlab releases a new
version every 6 months.
The proprietary nature also makes it difficult for 3th parties to extend the
functionality of Matlab.
Furtheremore, there are some other issues that stem from Matlabs origins as a matrix manipulation package:
- The semicolon. It can be useful to show the result when you type code in the
console, but in scripts it does not make any sense that one must end a line
with a semicolon in order to suppress output.
- Indexing is done with braces rather than brackets, making it difficult to
distinguish it from a function call.
Advantages of Python
The Python language has diversified application in the software development companies such as in gaming, web frameworks and applications, language development, prototyping, graphic design applications etc. This provides the language a higher plethora over other programming languages used in the industry. Before picking up some advantage of Python, just give a quick tour here. Now let's see some of the advantages of it -
Extensive Support Libraries
It provides large standard libraries that include the areas like string
operations, Internet, web service tools, operating system interfaces and
protocols. Most of the highly used programming tasks are already scripted into
it that limits the length of the codes to be written in Python.
Integration Feature
Python integrates the Enterprise Application Integration that makes it easy to
develop Web services by invoking COM or COBRA components. It has powerful
control capabilities as it calls directly through C, C++ or Java via Jython.
Python also processes XML and other markup languages as it can run on all
modern operating systems through same byte code.
Improved Programmer’s Productivity
The language has extensive support libraries and clean object-oriented designs
that increase two to ten fold of programmer’s productivity while using the
languages like Java, VB, Perl, C, C++ and C#.
Productivity
With its strong process integration features, unit testing framework and
enhanced control capabilities contribute towards the increased speed for most
applications and productivity of applications. It is a great option for
building scaleble multi-protocol network applications.
Disadvantages of Python
Python has varied advantageous features, and programmers prefer this language to other programming languages because it is easy to learn and code too. However, this language has still not made its place in some computing arenas that includes Enterprise Development Shops. Therefore, this language may not solve some of the enterprise solutions, and limitations include-
Difficulty in Using Other Languages
The Python lovers become so accustomed to its features and its extensive
libraries, so they face problem in learning or working on other programming
languages. Python experts may see the declaring of cast values or variable
types, syntactic requirements of adding curly braces or semi colons as an
onerous task.
Weak in Mobile Computing
Python has made its presence on many desktop and server platforms, but it is
seen as a weak language for mobile computing. This is the reason very few
mobile applications are built in it like Carbonnelle.
Gets Slow in Speed
Python executes with the help of an interpreter instead of the compiler, which
causes it to slow down because compilation and execution help it to work
normally. On the other hand, it can be seen that it is fast for many web
applications too.
Run-time Errors
The Python language is dynamically typed so it has many design restrictions
that are reported by some Python developers. It is even seen that it requires
more testing time, and the errors show up when the applications are finally
run.
Underdeveloped Database Access Layers
As compared to the popular technologies like JDBC and ODBC, the Python’s
database access layer is found to be bit underdeveloped and primitive.
However, it cannot be applied in the enterprises that need smooth interaction
of complex legacy data
Advantages of Python over Matlab
Python code is more compact and easier to read than Matlab code
Unlike Matlab, which uses end statement to indicate the end of a block,
Python determines block size based on indentation.
Python uses square brackets for indexing and parentheses for functions and
methods, whereas Matlab uses parentheses for both, making Matlab more
difficult to differentiate and understand.
Python’s better readability leads to fewer bugs and faster debugging.
While most programming languages, including Python, use zero-based indexing,
Matlab uses one-based indexing making it more confusing for users to
translate.
The object-oriented programming (OOP) in Python is simple flexibility while
Matlab's OOP scheme is complex and confusing
Python is free and open
While Python is open source programming, much of Matlab is closed
The developers of Python encourage users to input suggestions for the
software, while the developers of Matlab offer no such interaction
Python offers a wider set of choices in graphics package and toolsets
With NumPy, SciPy and Matplotlib, you pretty much have the whole set of functionality that MATLAB provides out of the box, and quite a lot of stuff that you'd have to buy for separate toolboxes.
However, MatLab community has their own arguments to choose Matlab or Python, (yes seriously). Good reading though.
