In the last blog post we saw the advantages of using Python. By now you might be wondering what can be done with Python or whether Python is right for me. In this episode we will look at various applications of Python –
GUI based software development
When we think of any software, the first thing that comes to our eyes is a graphical user interface or GUI. There is no substitute for a graphical interface when it comes to creating software for general use. Common users will never be interested in using any software using command line interface or CLI.
Python has several libraries that make it easy to develop GUI applications. It doesn’t need anything extra, just know the functions of that library. Below is a list of some popular libraries –
- Tkinter
- Kiwi
- PyQT
- WxPython
- PySide
- pyGUI
- PyGTK etc
Later, we will learn GUI programming with Tkinter.
System Programming
Even complex tasks like system programming can be done with Python’s built-in interface. Complex tasks like Memory Management, Networking, Process Management, Signals, Threading, Shell command, File Handling, etc. can be easily done in Python.
Database Programming
There is no substitute for database for creating modern and dynamic applications. And so there are Python interfaces for all popular relational database systems such as MySQL, Oracle, SQLite, ODBC, PostgreSQL, etc., and these interfaces can be handled very easily.
Python has Portable Database API through which databases can be used on different platforms without any changes.
Scientific Programming
Matlab has wonderfully good for numerical and scientific programming. But this space was quickly taken over by Python. Python has a variety of libraries for any kind of scientific and numerical calculations. Some popular libraries are mentioned below –
- NumPy
- SciPy
- Pandas
- SymPy
- PIL
- Astropy
- ScientificPython etc
Here is a list of many more such libraries. You can visit and see, if you want.
Machine Leaning and Deep Learning
Artificial Intelligence has been in the ascendancy for several years now. And Python itself has been a partner in this victory. All the best libraries, frameworks or APIs available today have support for Python. That means you can easily design any develop deep learning model with these frameworks. GitHub also has tons of code. Some of the APIs and frameworks are mentioned below –
- TensorFlow
- Pytorch
- Keras
- MXNet
- Sonnet
- DL4J etc.
Data Analysis & visualization
Data analysis is used to extract useful information, to arrive at correct conclusions by analyzing any data, to simplify decision making processes and many more. And the role of Python is very much in this work. Moreover, data visualization has a beautiful relationship with data analysis. As a result, both tasks can be done through Python.
Data visualization is another popular area where Python is used. Data visualization can be done very easily and beautifully with Python. Because Python has flexibility and has a large number of graphics libraries, data visualization can be done in less time. Even a simple graphical visualization can be made more beautiful and interactive with Python.
Summary
In addition to the above reason, Python does many other important tasks. Web development, automation and scripting, web scraping, programming applications, game development, design, data mining, blockchain, hacking, embedded system and robotic programming, software testing can be done.. In fact, it’s hard to find anything that can’t be done with Python, meaning that any kind of application can be built with Python.