Python is not limited to any particular domain or range it is a vast resource language which provides vast scopes to different fields. For example – Web Development, Hacking, Data analysis, Machine learning, Game development web scraping and many more. As a result, python has become a more important programming language in these growing times for data science. Python for data science is a stepping stone which will help you understand how data works. And how through data you can predict the future without any fuss as python is very easy to code.
What is Python?
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development. As well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.
- Open Source and Free
- Support for GUI
- Object-Oriented Approach
- High-Level Language
- Integrated by Nature
- Highly Portable
- Highly Dynamic
What exactly can I use Python for?
Well we can’t answer that because it will take a length of 3-4 blogs to cover that as there are so many applications for python.
But overtime we have observed some of the fields grow exponentially and some of the fields are:
- Web Development
- Data Science — including machine learning, data analysis, and data visualization
- Python Robotics
Web frameworks that are run on python like Django and flask have become popular in this time and day for web development.
These frameworks have been used by the world most important and popular sites like Spotify, Mozilla, Reddit, the Washington Post and Yelp. These servers help in creating backend code in python. That is the code that runs in the background on your servers as opposed to users’ browsers front end code.
Why do I need a web framework?
Framework is used to make the work easy as it lessens the burden. A framework gives you idea of turning a idea into a code. There are bunch of libraries that will help gives you code out of which to build your ideas, but no process. You must understand each library enough to get them to work together. A framework inverts that and says “understand this process and we’ll take care of the rest”.
Which Python web framework should I use?
There are two famous frameworks that the world are using that is Django and Flask. We recommend you to start from one of these frameworks.
Difference between Django and Flask?
Flask provides simplicity and well grained control. It is classified as a microframework because it does not require particular tools or libraries.
Django is an open source python web framework used for rapid development pragmatic, maintainable, clean design, and secure websites.
If you are a beginner you should choose Flask as it has fewer options and easy to understand for a beginner. And flask has better options for customization.
On the other hand, if you want to finish your task and Django will get you there faster.
Now let’s hop to another topic.
Python for machine learning:
There are many important and fine machine learning libraries and frameworks for python.
3 of the most popular ones are Numpy, scikit-learn, Tensorflow:
Numpy: Numpy is a python library used for working with arrays. It functions for working in domain of linear algebra, fourier transform, and matrices.
Scikit-learn: It is a machine learning learning library for python. It has various features and support algorithms like support vector machine, random forests, and k-neighbours.
Tensorflow: Tensor-flow is very low-level library that allows you to build custom machine learning models of low level.
We are recommending you to start with scikit-learn library if you are a beginner.
Data analysis and data visualization?
To help you get a jest of it lets give you a simple example:
So lets say you are going to university and you want to analyze how many boys and girls are there in you university which you can simply do it by data visualization.
From this graph you can see the frequency of females to male ratio.
Data analysis / visualization with Python
One of the most important libraries to make your sheet visually appealing is Matplotlib.
It is good library to get started with as you visually appealing data will interact with you more and the one you are making the sheet for. Some features are:
- Some other libraries such as seaborn is based on it. So, learning Matplotlib will help you learn these other libraries later on.
- You can start this easily
Scripting is used for writing scripts to automate small tasks. These contain a series of commands run one by one at runtime unlike programming languages that are compiled first before running. Scripts are generally used for Web development where they are used for dynamic web applications.
Python works with embedded applications like Raspberry Pi. It seems a popular application among the hardware hobbyists.
What about gaming?
Pygame is a library you could use to make games we have provided blogs of few games like flappy bird, helicopter, car and pong. It’s a popular gaming engine out there. You could use it to build hobby project or a major or minor in your college. But you shouldn’t do it if you are serious about game development,
Rather we would recommend to start with Unity with C#, Its more popular in the gaming scene. It allows you to build a game for many platforms, including Mac, Windows, iOS, and Android.
You can also use Python for designing brain for a robot. You can make robot react to its base surroundings and perform multiple tasks.
These six cool things made possible by this programming language is just a fraction of what you can do with it. Python has released 3.6 recently which has new features in the asyncio module (which is no longer provisional with a surprisingly stable API), formatted string literals, and the addition of a file system path protocol.
Further, python libraries which are the new JIT compiler helps to accelerate CPython by boosting its stock interpreter. You can achieve this with a JIT API from CoreCLR project (Microsoft).
The language is also evolving fast within the data science space. The Python ecosystem is now full of data science tools, so a lot of the data science work that’s currently taking place is being done with open-source tools like Python.
We have provided you with enough details on how python can do almost anything. And how there are libraries that you help you as a professional or a beginner. So learn python to boost your career and for great innovations.