Top 7 Real World Applications of Python
The demand for people who have undergone Python training has been substantially increasing. The ease of development and overall shorter development and testing time are some significant contributing factors to why this language has become so popular. In fact, according to the Tiobe Index, it has become one of the top 5 languages people want to have in their arsenal in 2019.
Let’s look at some of the main areas where Python can be applied and why it makes a developer’s life so much easier.
1. Data Science
Data science uses scientific methods, processes, algorithms, and systems to extract meaningful insights from complex data. And Python makes it extremely easy. Most folks undergo a Python course to use it as a door to data science. Python is the best tool for the visualization and analysis of datasets. This is because of the colossal and active ecosystem of packages like Matplotlib, Scikit-Learn, Numpy, Pandas, and IPython.
2. Machine Learning
Machine learning is the scientific study of statistical data and algorithms to make predictions and decisions based on patterns and inference without being explicitly programmed. Just like how a baby learns to recognize the things around based on various examples and patterns, we feed the machine with a lot of sample data or in ML terms training set which makes it ‘learn’.
This branch of Artificial Intelligence has numerous applications like face recognition, recommendation systems (such as in popular sites like Amazon, Netflix, and YouTube) and email filtering. Some of the popular ML algorithms include Deep Learning, neural networks, and support vector machines.
Python has replaced many other languages in the industry due to the vast collection of Python libraries, frameworks, and modules like sci-kit-learn, TensorFlow, Numpy, and Theano.
3. Web Development
Python web frameworks are used to create backend or server-side code. The backend code is run on the server while frontend is on the client’s device.
Some of the best choices are frameworks like Django and Pyramid, micro-frameworks like Flask and Bottle along with advanced content management systems like Plone and Django CMS. Python supports HTML and XML, email processing, JSON, and other protocols like FTP and IMAP.
The Package Index in Python has another set of libraries like an HTTP client library called Requests and an HTML parser called Beautiful Soup. Additionally, feedparser is used for parsing RSS/ Atom feeds, Paramiko for implementing the protocol called SSH2. Twisted Python is a useful framework for asynchronous network programming.
4. Scientific and Numeric
The powerful language is very commonly used in scientific and numeric computations. Python has advanced libraries like SciPy, NumPy, and Pandas. SciPy is a bunch of packages in mathematics, science, and engineering, while NumPy is helpful in complex numerical calculations. Pandas is a library specific to data analysis and mathematical modeling.
Easy editing and recording of work are achieved with IPython shell along with visualizations and parallel computing.
5. Desktop GUIs
GUI (Graphical User Interface) development is achieved in multiple ways with Python, out of which the most common method is tkinter. This is a thin object-oriented layer on top of Tk/Tcl. It is the quickest and easiest way to create a GUI application.
Other tool kits available in Python are wxWidgets, Kivy and Qt via pyqt or pyside. Apart from these, certain platform-specific tool kits are also available like GTK+ and Delphi.
6. Software Development
Python developers use Python as a support language for testing, build control and management.
Scons is a software construction tool used to build control. Continuous integration systems like Buildbot and Apache Gump are used for continuous compilation and testing. Finally, for bug tracking and project management, developers prefer Roundup and/or Trac. All of these libraries make the software development process much easier.
7. Database Access
High-level web applications need to store and retrieve user data. This reliable and fast structured storage of data is achieved using databases. Common libraries in Python are used to work with such databases.
MySQLdb is used for MySQL, psycopg2 for PostgreSQL and cx Oracle is used for Oracle database. Instead of SQL queries, Python Object-Relational Mappers or ORMs are used to access data from the backend.
Python is Everywhere
Now you know why Python is a hyped language after all. This is indeed so powerful. The language itself was not designed for such wide applications, but it has adapted quite well to the needs of the modern business world. But now that we know, let’s make the best use of it.
