Abstract
In this chapter, we cover just enough of Python to get you going. If you’re new to programming, this chapter will get you started well enough to be productive and we’ll call out ways to learn more at the end. Python is a great place to learn to program because its syntax is simpler and it has less overhead (e.g. memory management) than traditional programming languages such as Java or C+ +. If you’re an experienced programmer in another language, you should skim this chapter to learn the essentials.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anaconda, Inc (2019) Anaconda Software Distribution. URL https://www.anaconda.com
Bush BM (1996) The perils of floating point. URL http://www.lahey.com/float.htm
Hunter JD (2007) Matplotlib: A 2d graphics environment. Computing In Science & Engineering 9(3):90–95, https://doi.org/10.1109/MCSE.2007.55
Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, Kelley K, Hamrick J, Grout J, Corlay S, Ivanov P, Avila D, Abdalla S, Willing C (2016) Jupyter notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B (eds) Positioning and Power in Academic Publishing: Players, Agents and Agendas, IOS Press, pp 87–90
McKinney W (2010) Data structures for statistical computing in Python . In: van der Walt S, Millman J (eds) Proceedings of the 9th Python in Science Conference, pp 51 – 56
McKinney W (2018) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd edn. O’Reilly Media, URL https://github.com/wesm/pydata-book
Oliphant T (2006–2020) A Guide to NumPy. URL http://www.numpy.org/, [Online; accessed <today>]
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12:2825–2830
Peterson B (2008–2019) Python 2.7 Release Schedule. URL https://www.python.org/dev/peps/pep-0373/
RStudio (2019) RStudio: Integrated Development Environment for R. RStudio, Boston, MA, URL http://www.rstudio.org/, version 1.2.5033
Seabold S, Perktold J (2010) Statsmodels: Econometric and statistical modeling with Python. In: 9th Python in Science Conference
Waskom M, Botvinnik O, O’Kane D, Hobson P, Ostblom J, Lukauskas S, Gemperline DC, Augspurger T, Halchenko Y, Cole JB, Warmenhoven J, de Ruiter J, Pye C, Hoyer S, Vanderplas J, Villalba S, Kunter G, Quintero E, Bachant P, Martin M, Meyer K, Miles A, Ram Y, Brunner T, Yarkoni T, Williams ML, Evans C, Fitzgerald C, Brian, Qalieh A (2018) mwaskom/seaborn: v0.9.0 (July 2018). https://doi.org/10.5281/zenodo.1313201
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Schwarz, J.S., Chapman, C., McDonnell Feit, E. (2020). An Overview of Python. In: Python for Marketing Research and Analytics. Springer, Cham. https://doi.org/10.1007/978-3-030-49720-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-49720-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49719-4
Online ISBN: 978-3-030-49720-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)