Why Python for Data Analysis?

For many people (myself among them), the Python language is easy to fall in love with. Since its first appearance in 1991, Python has become one of the most popular dynamic, programming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular in recent years for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages as they can be used to write quick-and-dirty small programs or scripts. I don’t like the term “scripting language” as it carries a connotation that it cannot be used for building mission-critical software. Among interpreted languages, Python is distinguished by its large and active scientific computing community. Adopttion of Python for scientific computing in both industrial applications and academic research has increased significantly since the early 2000s.

For data analysis and interactive, exploratory computing and data visualization, Python will inevitably draw comparisons with the many other domain-specific open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years, Python’s improved library support (primarily pandas) has made it a strong alternative for data manipulation tasks. Combined with Python’s strength in general-purpose programming, it is an excellent choice as a single language for building data-centric applications.

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