All About Python - what can Python do?

 

One of the most commonly asked questions about Python is : What Python can do? As a fast growing language, Python has been used in many domains, including data analytics, data visualization, model development, natural language processing, and many others.

1. Python for data analytics

This is a the domain usually dominated by R, SAS, SQL, Matlab, etc. With the rich Python libraries, one can achieve almost everything that these language/software can do. And there is only one single programming language to learn. Sounds amazing? You may use SAS for data processing and you are familiar with working on a table. You need to do summaries, aggregations, table joining. The Python alternative is 'pandas'. Pandas provides a table interface called - data frame and tons of functions, capable to do anything you can imagine. What if you need to work on arrays, matrix, high dimensional data, 'numpy' is the library to provide an array interface to process list of lists ...

2. Python for data visualization

Data visualization or graphics is not usually easily adopted in other languages. This is an area used to be dominated by softwares, such as Matlab, origin, etc. With Python, there are so many amazing libraries to realize the graphics, such as the matplotlib, bokeh, seaborn, etc.

3. Python for model development

Model development, or machine learning is the most attractive applications of Python. For research/study, sklearn is good enough to cover major machine learning models, and facilities to build models. The best feature is the model development API that is now popular to the model developers. For more advanced areas, like machine learning, the most prevalently used libraries are Tensorflow and PyTorch, which are originated from Google and Facebook separately.

4. Python for natural language processing

Natural language processing (NLP) is one of the trending subjects of artificial intelligence. Most programming language has zero touch on this subjects. The study and research, the natural language toolkit (NLTK) is a good library to get started. Some other libraries, such as spaCy provides more advanced capabilities to processing natural language.