Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Python

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_269-1

Definitions

Python programming language is an open-source, portable, high-level general-purpose programming language. It features an interpreter which provides interactive environment, dynamic-type system, as well as automatic memory management. Being object oriented in nature, it is widely used and provides a large and comprehensive library for real-world applications. Python 2 and Python 3 (https://www.python.org/downloads/) are two versions of Python interpreters being presently used. Python 3 is not back-compatible with Python 2, but it is gaining ground among developers and will ultimately replace Python 2 entirely. Python gained popularity among data scientist due to availability of easy-to-use libraries and ease of working with variety of file format in both local and remote locations.

High-Level Programming

Python is a general-purpose high-level programming language. It provides easy access to library (called modules here (https://docs.python.org/3/py-modindex.html)) functions...

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References

  1. Blank D, Kumar D, Meeden L, Yanco H (2004) Pyro: a Python-based versatile programming environment for teaching robotics. J Educ Res Comput (JERIC) 4(3):3Google Scholar
  2. Cielen D, Meysman A, Ali M (2016) Introducing data science: big data, machine learning, and more, using Python tools. Manning Publications Co., Shelter IslandGoogle Scholar
  3. Elkner J (2000) Using Python in a high school computer science program. In: Proceedings of the 8th international Python conference, pp 2000–2001Google Scholar
  4. Grandell L, Peltomäki M, Back RJ, Salakoski T (2006) Why complicate things? Introducing programming in high school using Python. In: Proceedings of the 8th Australasian conference on computing education, vol 52, ACE’06. Australian Computer Society, Inc., Darlinghurst, pp 71–80. http://dl.acm.org/citation.cfm?id=1151869.1151880 Google Scholar
  5. Louridas P, Ebert C (2013) Embedded analytics and statistics for big data. IEEE Softw 30(6):33–39. https://doi.org/10.1109/MS.2013.125 CrossRefGoogle Scholar
  6. McKinney W (2012) Python for data analysis: data wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc., SebastopolGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.GD Goenka UniversityGurgaonIndia

Section editors and affiliations

  • Sherif Sakr
    • 1
  1. 1.School of Computer Science and Engineering (CSE)University of New South Wales