First edition. - Amsterdam, Netherlands : Elsevier, 
Book — 1 online resource.
1. Introduction to R and Python 1.1 Introduction to R 1.2 R environment 1.3 Installation of R 1.4 Integrated development environments (IDEs) and editors for R 1.5 Useful R commands 1.6 Getting help for R 1.7 Introduction to Python 1.8 Modules and packages in Python 1.9 Python IDEs 1.10 Installing Python and scientific Python distributions 1.11 Getting help for Python 1.12 Some useful packages and libraries in R and Python for oceanography
2. Data import and export in R and Python 2.1 Object types in R 2.2 Data import in R 2.3 Data export in R 2.4 Object types in Python 2.5 Data import in Python 2.6 Data export in Python
3. Plotting 3.1 Plots in R 3.2 Plotting in Python
4. Physical oceanography examples 4.1 Vertical profiling plots in R 4.2 Time-series plots in R 4.3 Temperature-salinity diagrams in R 4.4 Maps in R 4.5 Transect plots in R 4.6 Surface plots in R 4.7 Vertical profiling plots in Python 4.8 Time series plots in Python 4.9 Temperature-salinity diagrams in Python 4.10 Maps in Python 4.11 Transect plots in Python 4.12 Surface plots in Python 4.13 Animations in R and Python
5. Chemical oceanography examples 5.1 Vertical profiling plots in R 5.2 Time-series plots in R 5.3 Barplots in R 5.4 Boxplots in R 5.5 Pie charts in R 5.6 3D plots in R 5.7 Ternary plots in R 5.8 Vertical profiling plots in Python 5.9 Time-series plots in Python 5.10 Barplots in Python 5.11 Boxplots in Python 5.12 Pie charts in Python 5.13 3D plots in Python 5.14 Ternary plots in Python.
(source: Nielsen Book Data)
R and Python for Oceanographers: A Practical Guide with Applications describes the uses of scientific Python packages and R in oceanographic data analysis, including both script codes and graphic outputs. Each chapter begins with theoretical background that is followed by step-by-step examples of software applications, including scripts, graphics, tables and practical exercises for better understanding of the subject. Examples include frequently used data analysis approaches in physical and chemical oceanography, but also contain topics on data import/export and GIS mapping. The examples seen in book provide uses of the latest versions of Python and R libraries. (source: Nielsen Book Data)