Python — Basic Overview. Python has many unique features that…
By: Dan Root
Tags: Machine Learning, Data Science, Python Programming, Python, Data Analysis

Python has many unique features that help make it what it is. Some of these features include:

Python is Interpreted — Python compiles at runtime.

Python’s REPL — Interactive Python terminal

Python is Multi-Paradigm — Multiple categories separated by features

Python uses PIP package manager to install and manage packages in Python. (Uses PyPl as default source for packages and dependencies.

Python supports OOP, structured programing, aspects of functional programing, as well as AOP (Aspect Orientated Programming). With extensions Python supports logical programming and design by contract.

Python uses dynamic typing, which means there is more overhead.

Python uses reference counting and cycle detecting garbage collecting for memory management.

Python focuses on clean and readable code.

Python can be downloaded from https://python.org.

As always read the docs 👉 https://docs.python.org/3/.

Some of the major reasons that I personally love Python include the fact that it is just so easy to jump into as a beginner for a wide variety of projects. Python is a General-Purpose Language. That means that it does well at many different programming goals and systems. You can code the backend of a website, a business tool, do Explorative Data Analysis, create complex software, large calculations, or even machine learning / DEEP learning tasks. Also, for exploring data, you can use the REPL (Read Evaluate Print Loop) to bounce ideas off, figure out data structures, inspect data, test logic, and debug programming errors. Also, with the REPL you can dive into learning Python and seeing how variables and functions interact and behave. Another powerful tool in the Python toolbox is the Jupyter Notebooks, https://jupyter.org/. Jupyter Notebooks allow you to interact with Python inputs and receive clear outputs in a notebook like structure. The Jupyter Notebooks are used for an incredible range of purposes but are commonly used for data science and EDA (Exploratory Data Analysis). I love to use Python in this fashion myself. Just being able to see the process step by step and the ability to analyze different outputs is so important sometimes. One of the best implementations of this type of thinking is Google’s Colab Notebooks. https://colab.research.google.com. I use these notebooks all the time and they prove to be an incredible tool for an insurmountable number of tasks. In a way, the Colab Notebooks connect the power of the Jupyter Notebooks with the mobility of the Google Cloud.

Python also follows Modular Package Importing, which means that the language is modular in design. I find this feature of Python to be especially useful and fun. Instead of having to code out classes, methods, and functions yourself you can effortlessly import packages of pre-written code. Close to every language has some level of grouping and encapsulation, so I am satisfied that Python choose to take this paradigm seriously. Partially due to Python being Open Source, it has a thriving community. With one of the most active fan bases of any programming language, Python boasts a sizable number of packages, large volumes of discussion on the internet, and an almost unmatchable amount of resources. There is no doubt that Python is a behemoth of a language.

For an effective way to get the most out of Python for Data Science you can download a Distribution of Python and R named Anaconda. This distribution is geared towards scientific computing. Anaconda is an entire article alone though. If you are interested check out the homepage here, https://www.anaconda.com/. The individual edition is open source. It comes with many unique and robust tools.

As for IDEs there is, of course, a plethora of choices. Out of the sea of software my favorites are as follows:

PyCharm — https://www.jetbrains.com/pycharm/ — I cannot tell you how many times I use this IDE for my routine Python work. It is just the perfect setup for me. Not only the ease of having everything ready for Python use, but also, a full set of basic and advanced toolsets and properties that set this software in a different league, as far as, Python goes. Visual Studio Code — https://code.visualstudio.com/ — Not only do you have the clean, sleek, nice feel of the Microsoft Visual Studio product, but you get a ton of useful plugins, tools, and extensions. In my experience, VS Code handles Python beautifully. Visual Studio — https://visualstudio.microsoft.com/? — If a fully featured flagship IDE is something you are looking for then I recommend Visual Studio. Visual Studio comes in Community, Professional, and Enterprise editions. This is good for serious or in-depth projects. Atom — https://atom.io/ — Sometimes an incredibly hackable IDE is suitable for the task and if so then GitHub’s Atom IDE is an excellent choice.

Python is increasing in popularity, functionality, and efficiency every day. It is never too late to jump on board with this language. With how truly mighty Python can be, it is no wonder that people all around the globe use it every day to solve simple to major involved issues. And with truly massive resources and packages for Machine Learning, DEEP Learning and AI, Python doesn’t look like it is going anywhere anytime soon. Some of them include:

Tensor Flow Py Tourch SciKit Learn Keras Theano

If you are looking to get into the Data Science aspects of Python, then https://kaggle.com is the place for you to dip your toes in. Not only do they offer free courses in Python, but they have a large community and plenty of fun challenges and competitions. Not to mention that Kaggle gives you a research environment and datasets within their platform. Also, they can be a good place to hire or get hired in the Data Science field. I really do like how Kaggle put together their system. It feels exceptionally clean gives you the important knowledge you need in a fun interactive way.

Another good learning resource if you are looking for more in-depth training is https://datacamp.com. They offer some free courses and some paid. They are incredibly good for diving into real problems and learning with incredible minds at the same time. I have learned to appreciate the time they put into making everything simple, sleek, and enjoyable. The relaxing UI and professional videos on top of the overall feel of the platform make me want to come back and learn more all the time. They also, have a huge community with lots to do within the platform. I recommend checking them out to anyone interested in Python for Data Science or R for Data Science.

Thank you for reading this article. If you saw any inaccurate or incorrect information, please leave it in the comments below. I plan to release more articles in the future, so keep an eye out. Happy programming!