What is Python used for?

Python, as an object-oriented language, is built on the concept of classes (which are a fundamental part of object-oriented programming and should be the backbone of any serious Python project).In this view, classes exist to abstract away problems specific to their domain; they’re not “data structures” and they don’t come with default behaviors.

They are “abstractions” which can be applied selectively to specific tasks on your behalf.By analogy: Classes exist to give people more freedom in how they express their code.

If you want to do something like “add a function to a class definition,” you can do so by subclassing the Python class definition, like this:class MyClass(object): pass def add_function_to_my_class(self): return 1 def print_function(self): print(self)

But it illustrates that classes are essentially an abstraction which can be tailored for specific needs and goals. As such, classes aren’t just data structures — they act as more than that! They also have behaviors (and as such are abstractions). And as we will see below with regard to how we store our data in files and databases, their behavior is crucial for making things work!

The above is another way of thinking about Python: what it does well is provide a clear set of abstractions which let people express their ideas in a way that works for them — but what it doesn’t do well is include enough capabilities for everyone!

The best way around this is through subclassing — or at least providing enough capabilities for people who want them!


Python is Free and Open Source


It’s been a while since I’ve written about Python, but there’s been a lot going on. The recent release of the Python 3.6 brought along with it some significant changes and some extensions to the language itself (but not all of them).

It was a good time to take a look at what has happened, what is happening, and where it is headed.The biggest new feature in this release is the new identity package, which allows you to share code between projects without worrying about versioning.

This means that you can develop code for both projects and share it between them easily — from one project to another — making it easier to collaborate with others on the same project (for example in the same team or within an organization).

The next step is more powerful type inference for Python 2.x (which was already quite powerful). In other words, if you can describe some data structure as an instance of some class in terms of Python, then you can simply say “instance of myclass” rather than requiring typing “instance of myclass(data)”.

The next major change is in memory management; using flat-buffered text files now means that the memory used by your programs will be less than if you were using files containing character data.

This should make things easier for people working on large projects — where they might want to write their programs in many different languages — and especially for people who are running out of disk space: they shouldn’t have to worry about writing programs which may use more than one byte per statement or require lots of memory while running.Finally, there are several other improvements including a new standard library module called lambdas (for “lambdaless functions”) which means that all methods which take an object as an argument return that object rather than raising a TypeError .

It also makes cases like dictionaries clearer and allows methods such as cmp , str , list , tuple , zip , etc., to work properly when used with objects whose contents are strings instead of numbers or other data types like lists .I don’t claim that these changes will completely solve all problems with existing code but they should make them clearer and more consistent; hence I think they will make life easier for most Python programmers on both new projects as well as old ones.


Python has a Rich Repository of Libraries


At the moment, Python has a very rich repository of libraries. But there are some things that might be more important than just having a library directory:

• You have to decide what you want your code to do.

• There’s no one thing to look for in a library directory that tells you what someone else is doing.

• That means you have to learn how to sift through hundreds of thousands of libraries and make informed decisions about which ones you want (and why).This might not always be obvious and is more difficult than it sounds, but having a good library directory makes subsequent research much easier for you. If you don’t know where to start looking, it can be hard to identify the relevant libraries when asking questions in the Python community.

The following points may help:

• What kinds of things are people writing about? You can find out by searching for terms like “python library for” or “python code generator”; or by browsing some popular news sites (e.g., Stackoverflow) or using programs like JIRA and Google Groups .

If your company uses Python, chances are that someone else does too! This can also give you clues about what kind of problems people have with Python (e.g., they report issues with performance, they write tutorials on how to use it), and whether there is an active community around them (e.g., if people are maintaining their own projects).

• What kind of things do people request? You can also search these forums by adding “python” as part of your search terms; if nobody has asked before, chances are that nobody will after! These should tell you whether there is an active community around your problem (as opposed to merely an interest in the language), whether there is any documentation around it, etc..

Finally, this list will give you some hints on the content of your library directory: if something that looks interesting appears in several places (e.g., lists all available modules), it wouldn’t be unexpected for a person unfamiliar with your language but interested in learning it to check here first!

The above list doesn’t encapsulate everything – if something comes up again soon I’ll update this post with a more comprehensive list…


Python is Simple


Python is a programming language, but it’s not a programming language, it’s a suite of tools. In many ways, Python is the same as any other language (for example, there are different ways to write code in Ruby and Perl), but Python is also a lot more than that.

There are lots of very nice libraries out there (all open-source) that help you build powerful applications quickly. For example, there’s NumPy (which will help you with matrix math), SciPy (which will help you with machine learning), pandas (a data analysis library), IPython Notebook (a visual studio plugin for allowing you to run your code interactively using the command line interface), and tensorflow (the go-to library for machine learning and neural networks). On top of all that, there are also two main ways to both get started with Python:

1. You can dive straight in using the python command line interface with pip , which gives you installation instructions.

2. You can use the Python SDK, which has an installer for Windows, OSX and Linux users guiding them through the process of installing Python in their preferred environment.


Python is Easy to Learn


The term “easy to learn” is often used in relation to software. This is a bit of an oxymoron, as anyone who has tried to learn a new language can attest to. What makes Python easy to learn is that it allows you to focus on the task at hand, rather than worrying about remembering all the syntax rules.

It makes everything else — not just programming languages but also other tools and constructions such that you can get stuff done in Python — easy too.For example:You don’t need to know what Python does; all you need is your computer and some Python code.

The language does all the heavy lifting for you (if it hasn’t already done so) and lets you focus on getting stuff done — not remembering syntax rules or trying to remember whether a particular command returns an integer or a float or a list of strings.It’s very common in these posts to mention that one day I’ll write another post about how important flexibility is in software development, but the truth is that there are many ways of achieving this — including using flexible languages like Ruby and Groovy, which are designed with ease of use (and even security) in mind as well as flexibility (and rapid deployment/versioning) in mind.


Conclusion


Many people will think that this is a very technical post, because it is full of numbers and graphs. However, the point here is that python has been used to solve many different problems, and there are plenty of good reasons to use it in applications beyond data science.

For example:

• Web development – Python is an awesome tool for building dynamic web pages. It’s also perfect for creating interactive charts.

• Automation – Python runs inside your computer as a virtual machine, which means that you can run your programs on a remote server or headless client (like an Arduino). You don’t need to install anything on your local computer because it’s already the system running on your computer.

• Data science – Python is great for performing complex math and data analysis like data processing or machine learning. It’s also perfect for creating interactive visualizations like trees, bar graphs, and scatter plots.

• Software development – Python allows you to create reusable code, which means that you can reuse code written in other languages without having to learn new syntaxes or libraries (like C++). For example: PyQt5 allows you to create all kinds of graphical user interfaces with Python quite easily; csvread allows you to read CSV files straight out of Excel; Pandas allows you to perform complex data analysis with ease using Python; Seaborn helps you visualize your data using matplotlib; etc…

Python is great for doing things that other languages cannot do well (and vice versa). It works well with other technologies too: if its name doesn’t give away what software it supports (like Pandas does), then it should be easy-to-use as well as powerful; if its name doesn’t give away what domain its intended for (like Seaborn does), then it should be easy-to-use as well as applicable in domains outside its own domain… And so on…