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Python

  • Thomas Haslwanter
Chapter
  • 16k Downloads
Part of the Statistics and Computing book series (SCO)

Abstract

This chapter is a kick-start into Python. It shows how to install Python under Windows, Linux, or MacOS, and walks step-by-step through documented programming examples. The most important statistics packages for Python are introduced. Tips are given to help avoid some of the problems frequently encountered while learning Python.

Keywords

Command Line Python Script Group Object Python Program Python Package 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Harms, D., & McDonald, K. (2010). The quick python book (2nd ed.). Greenwich: Manning Publications Co.Google Scholar
  2. Pilon, C. D. (2015). Probabilistic programming and Bayesian methods for hackers. http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ Google Scholar
  3. Scopatz, A., & Huff, K. D. (2015). Effective computation in physics. Sebastopol: O’Reilly Media.Google Scholar
  4. Sheppard, K. (2015). Introduction to python for econometrics, statistics and data analysis. http://www.kevinsheppard.com/images/0/09/Python_introduction.pdf Google Scholar
  5. Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22:, 392–399.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thomas Haslwanter
    • 1
  1. 1.School of Applied Health and Social SciencesUniversity of Applied Sciences Upper AustriaLinzAustria

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