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Published in:
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2019 | OriginalPaper | Chapter

1. Getting Started with Scientific Python

Author : José Unpingco

Published in: Python for Probability, Statistics, and Machine Learning

Publisher: Springer International Publishing

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Abstract

Python is fundamental to data science and machine learning, as well as an ever-expanding list of areas including cyber-security, and web programming. The fundamental reason for Python’s widespread use is that it provides the software glue that permits easy exchange of methods and data across core routines typically written in Fortran or C.

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next chapter Probability
Footnotes
1
Wheel files are a Python distribution format that you download and install using pip as in pip install file.whl. Christoph names files according to Python version (e.g., cp27 means Python 2.7) and chipset (e.g., amd32 vs. Intel win32).
 
2
See arrayobject.h in the Numpy source code.
 
3
You can also do this in the plain Python interpreter by doing import matplotlib;matplotlib.interactive(True).
 
4
Note this kind of on-the-fly memory extension is not possible in regular Numpy. For example, x = np.array([1,2]); x[3]=3 generates an error.
 
Literature
1.
T.E. Oliphant, A Guide to NumPy (Trelgol Publishing, 2006)
2.
L. Wilkinson, D. Wills, D. Rope, A. Norton, R. Dubbs, The Grammar of Graphics. Statistics and Computing (Springer, Berlin, 2006)
3.
4.
W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (O’Reilly, 2012)
5.
6.
H.P. Langtangen, Python Scripting for Computational Science, vol. 3, 3rd edn. Texts in Computational Science and Engineering (Springer, Berlin, 2009)
Metadata
Title
Getting Started with Scientific Python
Author
José Unpingco
Copyright Year
2019
Book
Python for Probability, Statistics, and Machine Learning

Print ISBN: 978-3-030-18544-2

Electronic ISBN: 978-3-030-18545-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-18545-9_1