How to Categorise Python Top Uses

Python language is popular in many areas. Below are the real uses of Python. Surely, many developers confuse to tell all the applications. So I have listed for quick reference.


python real uses

8 Python Real Application Uses

#1. Python Application use - Python Scripts 

Very much popular in scripting. You can write scripts in many areas. Like Interfaces, to access files, to access database, and to access the application programs.

#2. Python Application use - Python in Web Development 

You can use Python in Web development. There are powerful Python frameworks present such as Django, Flask, Web2Py, and Pyramid. You can develop an application in IoS/Android/Windows.

#3. Python Application use - Graphical User Interface

You can develop applications, which need access to GUI. The libraries and APIs like Tkinter, PyQt, PyGTK, and wxPython allow developers to develop GUI-based apps with simple or complex interfaces. 

#4. Python Application use - System Programming

You can use it in System Programming. Like Protocol management, and OS operations. The Python Standard Library (PSL) has OS and POSIX bindings, which can be used for handling files, multi-threading, multi-processing, environment variables, controlling sockets, pipes, and processes. 

#5. Python Application use - Access to database 


Python is used to connect and access data from different types of databases, be it SQL or NoSQL. APIs and connectors exist for these databases like MySQL, MSSQL, MongoDB, Oracle, PostgreSQL, and SQLite. In fact, SQLite, a lightweight relational database, now comes as a part of the Python standard distribution.

#6. Python Application use - Scientific Applications

Very much popular in Scientific Computing and Numerical Analysis.


#7. Python Application use - Machine Learning/Deep Learning 

Python has many libraries and frameworks like Scikit-Learn, h2o, TensorFlow, Keras, PyTorch, and even core libraries like NumPy and SciPy for not only implementing machine learning algorithms but also using them to solve real-world advanced analytics problems.


#8. Python Application use - Text Analytics 

Python can handle text really well and this has led to several popular libraries like NLTK, Gensim, and spaCy for natural language processing, information retrieval, and text analytics. You can also apply standard machine learning algorithms to solve problems related to text analytics.


Reference Books
  • Text Analytics with Python: A Practitioner's Guide to Natural Language Processing, Second Edition  
  • Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration, and Hedging  
  • Python Projects 
Reference Blogs

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