What Is Python Used for?Created by Guido Van Rossum, python small learning curve, clear syntax, pretty indentation and thousand of open source libraries has made Python one of the best programming language for all level of developers. The high demand for python developers in the IT industry is also helping in the rapid growth of the python community. If you are new to Python, learning basics and wondering about What can I do with Python? or, what is python good for? or, what can python be used for?. So, here in this article, we have covered all the significant domains where Python is actually used, and we have also mentioned the python libraries or tools you need to master if you want to seek a career in a specific field. So, without further ado, let's discuss the significant domain where it is used. Domains where it can be used:
- For web-development
- For IoT (Internet of Things)
- For Machine Learning
- For startups
- For Fintech.
1. Python in Web DevelopmentRight now, every organization has a website that expands its reach on the online market and provides company existence over the internet. Creating, managing, and deploying a website or web-application comes under Web-development, and using the Python frameworks , we can create a robust and reliable web application for various devices. Web project that can be built using Python:
- App for mobile and desktops.
- Responsive Web-applications.
- AI and ML-based Chatbots.
Top Python Web-FrameworksA web-framework is a collection of libraries that is used to build dynamic web applications. However, we can create a web application using individual libraries, but web frameworks make it easier and faster. 1. Django Django is the most popular and widely used python framework. It is a full-stack web framework that comes with many built-in features such as admin authentication and verification. Django is highly recommended for big and medium-size projects, if you are building small projects like a to-do list or single page web applications, then Django would not be a great choice. Django also provides a built-in library for application testing, which is an extension of the python unit test library. 2. Flask Flask is the second-best python web-framework, but in some index rankings, it overcomes Django. If we compare Flask with Django, Flask is lean toward the small or micro size web-applications, and it often suggests to learn Flask before moving to Django. It is a micro web framework, and here we do not get built-in features like Django, but we can create all those features from scratch. Unlike Django, in Flask, we have to depend on other libraries and install some tools to build a high-quality web application. 3. Bottle The bottle is another light-weighted python web-framework that is widely used for learning and educational purposes. The bottle also does not offer too many built-in features, but it is mostly dependent on the Python standard libraries. It is an excellent framework for prototyping and building small and personal web applications. Here you do not need to worry about the nitty-gritty of various file creation while creating an app. 4. Pyramid The pyramid is a merger of two old python web-frameworks, Pylons and repose.bfg. And there was a time when pyramid was top python frameworks. In a direct comparison with Django, then pyramid has some significant advantages such as it is very easy to customize and build complex apps with ease.
2. Python for IoT (Internet of Things)IoT is one of the buzzwords of 2020, like Artificial Intelligence and Machine learning. If we want to define IoT in a nutshell, we can say that it is a concept about embedded physical objects which can connect with the internet for communication purposes. Now every IoT device comes with a unique IP address that allows it to communicate with the network. These days we can see a rise in IoT devices usages, and the time is not so far when every house will become a smart house and every office a smart office. IoT devices also play a vital role in cyber-systems, Machine Learning, and big data, with the help of wireless networks and sensors.
AdvantagesGenerally, in IoT's we require those programming languages which are fast, light-weight, close to the hardware, and scalable. However, Python is neither quick or close to system hardware, but it still used in IoT because of its popularity, versatility and Raspberry Pi. 1. Python Libraries It has broad community support, and it has thousands of libraries. You name the IT domain, and Python has a library for it. With so many libraries present in Python arsenal, it's become effortless to use Python with anything. 2. System Independent It is portable and expendable, and its code can be run on any system or embedded devices. Right now, Python is supported by many operating systems and single-board computers which help python code to execute embedded systems. 3. Manage Complex data It comes with many built-in robust data structures that are capable of handling and organizing complex data collected by the system. 4. Easy to learn It is straightforward to learn basic Python, and that's one of the underlying reasons why Python is so popular. In Python, you do not need to worry about system formatting and compiling options python cover all and much more for you. 5. Easy syntax and less code It has a beautiful syntax which makes it's code more readable and clean. Python comes very handy for small devices where memory is limited, and we want to write the script with minimum lines of code. 6. Python with data science Currently, Python is widely used for data science and big data, because there are some data science and AI libraries which are only available for Python. Now data has become as valuable as money, and these smart IoT devices have become data collectors for big firms. So now they require such programming language in their IoT systems which can deal with the data flow. 7. Raspberry pi Raspberry pi is a popular microcontroller embedded device which is used for research and education purpose, and by default, it uses Python as its programming languages. 8. Easy Debugging It is an interpreted language that reads and executes code line by line, and if there is an error, the code execution stopped at the error line which makes debugging the program easy. 9. AWS support for Python AWS provides Python SDK for its devices, which helps python developers to write such scripts which allow them to access the AWS IoT devices.
Tools for Python in IoT1. Raspberry pi Raspberry pi is a small-single bord computer, widely used for teaching and research purposes of basic computer science in schools and colleges. It is highly portable and cheap, which makes it more popular among researchers and robotic developers. However, it does not come with any peripheral device like a monitor, keyboard, mouse, etc., but you can connect them separately to its USB and HTML ports. Raspberry pi is like a small and portable CPU that fits in your pocket. Raspberry has been used for many IoT projects because it comes with a Linux distro which makes it faster and more secure. Unlike other computers, system raspberry can run on little energy and support python as its default programming language. Projects that can build using Raspberry pi
- Retro games.
- Robot controller
- A small web-server.
- mini desktop.
- Security systems.
- Testing machine
3. Python in Machine LearningIt is doing great in Machine learning and Data science domains. AI and ML have hyped Python in the last few years, which also surged in python developers. Currently, many big tech firms such as Google, IMB, Netflix, and Amazon investing millions of dollars in their machine learning projects, and here Python standing as one of the front-line programming languages.
Advantage1. Machine and Data Science libraries Libraries are the main assets of Python, and It has libraries for every computer science domain. It has many open-sources and third party numerical computational libraries which heavily used by ML engineers for machine learning models. 2. Simple Syntax Machine Learning algorithms are already complicated, so we require such programming language which can implement it with ease, and here Python serves this purpose. 3. Integration with other Programming languages: It can be easily integrated with different programming languages; there are various python compilers that could do that.
Top Python Libraries for Machine LearningIt is well known for its machine learning and data science libraries, except R programming language, It has the highest number of libraries for data science. Here are some of the python libraries you must know if you seek a career in Machine Learning as a Python developer. 1. Scikit-Learn Scikit-learn is one of the most popular and influential pythons machine learning libraries. It is built using two other numerical computational python libraries "SciPy" and "NumPy", which means you get full support for all the mathematical computation in Scikit-Learn. Apart from mathematical computation, Scikit provides all the essential machine learning algorithms such as regression, clustering, classification, pre-processing, model selection, dimensionality reduction and much more. As a machine learning library, it provides modules for data mining and data analysis for simple and complex data science models. 2. TensorFlow It is a well-known powerful library developed by Google, and it is only available for 2 or 3 programming languages. It comes with very advanced modules with robust numeric computation. Google itself uses this library for his machine learning and neural network projects. It is maintained by Google, and they release it updates in a regular interval of time. Its flexibility and high-performance architecture make it a powerful tool for numerical computation. Apart from Google, many other companies such as Dropbox, Uber, eBay, Snapchat, and Coca Cola use it too. 3. Nilearn Like other python data science and machine learning libraries, Nilearn is also a robust and fast library. It is highly recommended for statistic and neuroimaging data. This library is built on scikit-learn, which give it a head-on over scikit. In Nilearn, we get various inbuilt machine learning algorithms for pattern recognition, predictive modelling, collective analysis and multivariate statistics. 4. mlpy mlpy library stands for Machine Leaning python, and this library is specially designed for machine learning using python 3 and 2. It is built on other popular python libraries such as NumPy, SciPy and other GNU scientific libraries which promise high performance in supervised and unsupervised machine learning problems. mlpy provide various methods for regression, classification, Linear Discriminate analysis, clustering, dimensionality reduction and much more.
What problems can we solve using python Machine learning libraries?There are three major types of machine learning problems, and using python libraries can solve all those problems. 1. Supervised Learning In supervised machine learning the model work on a provided data set, and here we already know the output. Supervised learning can be performed using two main techniques "Classification" and "Regression". 2. Classification In the classification technique, the model categories the data into different phases and classes, then predict the output of a discrete value. 3. Regression In regression, the output is predicted by the closest value from the continuous data set. Python libraries are capable of solving all the major supervised problems using these techniques. Here is the list of all the major libraries which are used for supervised machine learning problems.
4. Python in StartupsIn startups, we have to build projects from scratch, and there we require such programming languages that can deliver quick and efficient results. There are many companies which start their projects using Python, let's take Instagram as an example, Instagram is entirely written on Django, that proves that for startups and new projects we can ultimately depend on Python.
Why choose Python for startups?There are many reasons why it should be your first choice for your startup. The community is the primary reason why to choose Python, and a strong community always come helpful when you get stuck somewhere while writing the code. Finding a python developer is also very easy; it's not like that it has limited developers, post a job you will find hundreds of professionals whom you can work. It is also very scalable, which means it can be easily integrated with other popular technologies, so with time, you can quickly move your project from one platform to another.
5. Python in Fintech (Financial Technology)It is one of the oldest players in programming languages, although it has gained popularity in the last 6 and 7 years, still, it is used in many Financial technical applications. If you want to create a fintech project, then you can opt for Python as a core programming language because it is the fastest-growing programming language and provide high performance with high scalability.
AdvantagesIt offers many features and solutions for hedge funding and investment banking, which make it an ideal choice for fintech.
- With Python, we can create risk and trade management platform.
- Python numeric and statistic libraries can be used to solve the quantitative rate problem.
- Python libraries can also be used to regulate, compile, and analyze the data generated by the firms.