Why Python knowledge is essential for a successful programming career
The amount of knowledge available on the internet these days is astounding. If we were to go back 10 years in time even, the extent to which information was available looks like a small rock compared to the large mountain we have today.
This goes for pretty much all recognizable subjects and programming languages have evolved tremendously in terms of the support community. Stack Overflow is arguably one of the most visited forums when it comes to programming help. It was created on September 15th, 2008 and has changed the face of programming tutorials in a recognizable way.
Whatever you’re trying to accomplish can be helped by observing the Stack Overflow forums and a simple Google search for a specific error will undoubtedly return a result from its forums. The influence that this particular website has had on the world of programming can accurately be described in the following meme:
Why Python should be in every developer’s toolkit
Python was released into the world in 1991 and with the intention of being a high-level and easily interpretable language. Due to its beginner-friendliness and open-source nature, Python gained notable popularity and boomed in community acceptance at a pace that is still surprising to this day.
This phenomenon of global acceptance is the sole reason why Python has been developed to cater to a vast variety of tasks. You can perform image processing, machine learning, data science, web development, and app development to say the least.
Python has a number of libraries that make image processing tasks a breeze that would otherwise take an unnecessarily long and complex effort to accomplish on C, C++, and C#. What’s common about all of Python tasks is that the code is often extremely short and easy to change due to the nature of its structure.
OpenCV, Mahotas, Scikit-image, and SciPy are just some of the majorly used libraries that make Python so attractive for this field of work. Each comes with its own set of tools that makes it unique and an incredible experience to use
Numpy and Pandas are two super-important tools of Python that make the task of dealing with a large amount of data a fun and rewarding experience. Numpy is designed in C which allows for lightning-fast computation and that is a necessity when our data evolves into gigabytes.
Numpy objects are arrays which make handling floating-point numbers very easy to manipulate and work with. There are a ton of matrix operations that can be performed with a simple function that takes an input of the Numpy object and an argument of how to change it. The output produced is a result that can be stored into a variable, just like you would with a normal integer or string.
Pandas is more on the side of making data readable for statistical analysis. It offers a wide variety of visualization techniques like making tables with properly defined columns. Other than that, individual columns can be addressed as well. The tables in Pandas are called DataFrames and they can be chopped up into smaller bits to focus on some specific set of data. Since they are designed to achieve maximum efficiency, DataFrames are any data scientist’s best friend.
As Artificial Intelligence (AI) continues to gain popularity around the world, Machine Learning (ML) hitches along too. ML is the subdomain of AI which actually deals with creating systems that are able to make decisions based on real-time data. The techniques which allow such models to be created vary in great detail.
Again, because of Python’s general ease of use, ML developers have created remarkable assets which allow users to create complex models like Random Forest Regressors with a simple function call. Coding such structures on your own while keeping efficiency in mind can take days, if not weeks to do so perfectly.
Python takes that trouble away by providing a huge number of tools (Scikit-learn, SciPy, Scrapy, etc.) that exponentially speed up work and allow for practical creations.
This category of ML techniques is currently all the buzz in Computer Science these days and for good reason. Deep Learning (DL) is a form of neural networks that are focused on the creation of a complex architecture of different types of layers that when combined together, create some particularly astounding applications related to Computer Vision.
Neural networks primarily (but not restricted to) work with images by focusing on their pixels. Networks consist of many weights with activation functions that are often complex, resulting in some very demanding computations. This leads to the use of GPUs in DL and neural networks in general.
Python offers three main instruments for easy construction of these networks: TensorFlow, Pytorch, and Keras.
Tensorflow is the least abstract of the three with Pytorch taking second place and Keras being the highest. In other words, Tensorflow is equivalent to coding neural networks in assembly language, Pytorch being similar to a C++ implementation, and Keras being a Python-level neural network application.
Python being a versatile language also offers extensive support for modern-day web development. The most notable helping tools out there for this kind of work are Django, TurboGears, and Flask.
The support for these tools is growing day-by-day and a large number of developers are becoming more accustomed to this work environment.
As we can see, Python is rapidly becoming the staple choice as a programming language in many fields and will only continue to grow, unless some new magical language comes along that can compete with Python on its level of abstraction and still provide similar results.
The only drawback of using Python is that it can be slow to work with at some points, but a decent system will probably never bottleneck your performance anytime soon. The only exception arises when working with neural networks, for which a GPU is highly recommended.