Python is a very popular general purpose programming language — with good reason. It’s object oriented, semantically structured, extremely versatile, and well supported. Programmers and data scientists favour Python because it’s easy to use and learn, offers a good set of built-in features, and is highly extensible. Python’s readability makes it an excellent first programming language.
Data analysis is a process of inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-making.
Here’s our verdict of the finest Python data analysis tools captured in a legendary LinuxLinks-style ratings chart. Only free and open source software is eligible for inclusion.
| Python Data Analysis | |
|---|---|
| pandas | Fundamental high-level building block for doing practical, real world data analysis |
| NumPy | Core package for scientific computing with Python |
| SciPy | Ecosystem for mathematics, science, and engineering |
| Dask | Advanced parallelism for analytics |
| Polars | DataFrame interface on top of an OLAP Query Engine |
| Orange | Component-based framework for machine learning and data mining |
| Modin | Drop-in replacement for pandas |
| Vaex | Fast visualization of big data |
| AWS DW | Extends the power of pandas library |
| HoloViews | Make Data Analysis and Visualization Seamless |
| datatable | Manipulate 2-dimensional tabular data structures |
| yt | Multi-code Toolkit for Analyzing and Visualizing Volumetric Data |
| Optimus | Agile Data Preparation Workflows |
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Python is a general-purpose high-level programming language. Its design philosophy emphasizes programmer productivity and code readability. It has a minimalist core syntax with very few basic commands and simple semantics, but it also has a large and comprehensive standard library, including an Application Programming Interface (API).
It features a fully dynamic type system and automatic memory management, similar to that of Scheme, Ruby, Perl, and Tcl, avoiding many of the complexities and overheads of compiled languages. The language was created by Guido van Rossum in 1991, and continues to grow in popularity, in part because it is easy to learn with a readable syntax. The name Python derives from the sketch comedy group Monty Python, not from the snake.
The prominence of Python is, in part, due to its flexibility, with the language frequently used by web and desktop developers, system administrators, data scientists, and machine learning engineers. It’s easy to learn and powerful to develop any kind of system with the language. Python’s large user base offers a virtuous circle. There’s more support available from the open source community for budding programmers seeking assistance.
I prefer R.