Python - the good and the bad parts

2 minute read

In the last days I’ve seen some questions on Reddit that kind of triggered me. They were basically asking why do people still use Python when it is so slow and it’s ecosystem is very broken (the Python 2 vs 3 problem).

These kind of questions made me want to write down a few reasons why you should use Python and a few reasons why you shouldn’t, because after all, Python is just a tool, just like any other programming language out there for resolving problems and business needs.

The good parts

Firstly, the main reason why you should use Python is because of its community. There are a lot of people and organizations investing time and money in the development of the language itself and libraries surrounding it. There are a lot of awesome and powerful libraries out there that tackle various important areas such as web development (eg. [Django], Flask, Pyramid, Tornado), distributed computing (eg. Celery, dask), machine learning (scikit-learn, Theano, Tensorflow), numeric processing, data analysis and visualizations (numpy, pandas, matplotlib, seaborn) and many others.

Secondly, it offers a huge boost in speed of development. For a business, this is crucial as the time of development directly affects the agility of the business itself and time-to-market.

Another goodie Python brings is that it promotes clean beautiful code (as indentation is part of the language itself, some people might disagree with this) because of its unique syntax. Also, the Zen of Python sums up all the good practices a programmer should follow.

The bad parts

But as any language, Python too has its disadvantages.

The first one that is brought to the table is the lack of performance which is mainly caused by the fact that it is an interpreted language. In contrast with a compiled one where everything is compiled to native code or bytecode, somewhere closer to the machine, when running a Python program multiple steps have to be done before actually executing the code: code parsing, abstract syntax tree building, module lookups. All these steps require time and extra processing but there are methods to boost the overall performance of the Python code (for example, by using PyPy or Cythonizing modules that do heavy numeric computations).

In most cases, the bottleneck of an application is not the CPU, but the disk and the network. If the CPU seems to be limiting your Python program, perhaps you chose the wrong tool for the job.

Other disadvantage is that Python is an interpreted language. That means that all errors occur at runtime and is way harder to catch errors early (for example typos in dictionary keys, everybody hates KeyErrors).

Another thing is the Python2 vs Python3 fiasco. Everybody hates backwards incompatibility, but I like to believe the Python core developers made a good call with this. A lot of weird inconsistencies and bad design choices in the early days of the language were fixed (eg. the string and unicode vs string and bytes, urllib, urllib2, int and long, the print statement, inconsistent naming, some of which still persists in Python3 unfortunately and the list continues).


Before starting a new project, be sure to make clear what you need to accomplish and what tools you need for that. If the projects does not goes as expected because of programming language limitations, there is a big chance you messed up on the technology picking.



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