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.
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Notes
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During the writing of this book, the former monolithic IPython was split into two separate projects: Jupyter is providing the front end (the notebook, the qtconsole, and the console), and IPython the computational kernel running the Python commands.
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In my current Windows 10 environment, I have to change the path directly by using the command “regedit” to modify the variable “HKEY_CURRENT_USER — Environment”
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More help on text-files can be found under http://support.smqueue.com/support/solutions/articles/31751-how-to-create-a-plain-text-file-on-a-mac-computer-for-bulk-uploads.
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References
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Olson, R. (2012). Statistical analysis made easy in python. http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/
Pilon, C. D. (2015). Probabilistic programming and Bayesian methods for hackers. http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
Scopatz, A., & Huff, K. D. (2015). Effective computation in physics. Sebastopol: O’Reilly Media.
Sheppard, K. (2015). Introduction to python for econometrics, statistics and data analysis. http://www.kevinsheppard.com/images/0/09/Python_introduction.pdf
Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22:, 392–399.
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Haslwanter, T. (2016). Python. In: An Introduction to Statistics with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-28316-6_2
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DOI: https://doi.org/10.1007/978-3-319-28316-6_2
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