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R and Python for oceanographers : a practical guide with applications /
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 so...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2019]
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; R and Python for Oceanographers: A Practical Guide with Applications; Copyright; Contents; Chapter 1: Introduction to R and Python; 1.1. Introduction to R; 1.2. R environment; 1.2.1. Base system of R; 1.2.2. Add-on packages; 1.3. Installation of R; 1.3.1. Installation of R base software; 1.3.2. Installation of add-on packages; 1.4. Integrated development environments (IDEs) and editors for R; 1.4.1. Official R console and editor; 1.4.2. RStudio; 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 distributions1.10.1. Using pip package; 1.10.2. Using package managers; 1.10.3. Using source files of packages; 1.11. Getting help for Python; 1.12. Some useful packages and libraries in R and Python for oceanography; References; Chapter 2: Data import and export in R and Python; 2.1. Object types in R; 2.1.1. Vectors; 2.1.2. Matrices; 2.1.3. Arrays; 2.1.4. Data frames; 2.1.5. Lists; 2.1.6. Factors; 2.1.7. Functions; 2.2. Data import in R; 2.2.1. Import from txt, dat, csv and Excel xls, xlsx files; 2.2.2. Import from netCDF4 file
- 2.2.3. Import from mat files2.2.4. Import from SeaBird cnv files; 2.2.5. Import data from online databases; Import tab delimited text data from LOBO-0010 Northwest Arm, Halifax, Canada; 2.2.6. Import csv.gz file from WOA13 V2; 2.3. Data export in R; 2.3.1. Export as txt, dat, csv and Excel xls, xlsx files; 2.3.2. Export as netCDF4 file; 2.3.3. Export as Matlab mat files; 2.4. Object types in Python; 2.4.1. Arrays in Numpy package; Create an array; Create array from existing data; 2.4.2. Series and data frame objects in Pandas package; Pandas Series; Pandas Data Frames
- 2.4.3. User-defined functions in Python2.5. Data import in Python; 2.5.1. Import from txt, dat, csv, and Excel xls, xlsx files with Numpy loadtxt; 2.5.2. Import from Excel xls files with Pandas read_excel; 2.5.3. Import from Excel xlsx files with Pandas read_excel; 2.5.4. Import from netCDF4 file; 2.5.5. Import from Matlab mat files; 2.5.6. Import from Seabird cnv file; 2.5.7. Import data from online databases; Import tab delimited text data from LOBO-0010 Northwest Arm, Halifax, Canada; Import all data in a csv file; Import block of data at defined ranges of rows and columns
- Subset data based on multiple criteria using column values2.6. Data export in Python; 2.6.1. Export as txt, dat, and csv files with Numpy savetxt; 2.6.2. Export as txt, dat and csv files with Pandas DataFrame.to_csv; 2.6.3. Export as xls, xlsx with Pandas ExcelWriter; 2.6.4. Export as netCDF4 files; 2.6.5. Export as Matlab mat files; References; Chapter 3: Plotting; 3.1. Plots in R; 3.1.1. High-level plotting functions; plot() function; Other high-level plotting functions; Arguments to use with high-level plotting functions; Low-level plotting functions; 3.1.2. Graphical parameters