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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 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.
References
T.E. Oliphant, A Guide to NumPy (Trelgol Publishing, 2006)
L. Wilkinson, D. Wills, D. Rope, A. Norton, R. Dubbs, The Grammar of Graphics. Statistics and Computing (Springer, Berlin, 2006)
F. Perez, B.E. Granger et al., IPython software package for interactive scientific computing. http://ipython.org/
W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (O’Reilly, 2012)
O. Certik et al., SymPy: python library for symbolic mathematics. http://sympy.org/
H.P. Langtangen, Python Scripting for Computational Science, vol. 3, 3rd edn. Texts in Computational Science and Engineering (Springer, Berlin, 2009)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Unpingco, J. (2019). Getting Started with Scientific Python. In: Python for Probability, Statistics, and Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-18545-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-18545-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18544-2
Online ISBN: 978-3-030-18545-9
eBook Packages: EngineeringEngineering (R0)