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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Alyuruk, Hakan (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier, [2019]
Edición:First edition.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Alyuruk, Hakan,  |e author. 
245 1 0 |a R and Python for oceanographers :  |b a practical guide with applications /  |c Hakan Alyuruk. 
250 |a First edition. 
264 1 |a Amsterdam, Netherlands :  |b Elsevier,  |c [2019] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
588 |a Online resource; title from PDF title page (EBSCO, viewed June 13, 2019). 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
520 |a 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. 
650 0 |a Oceanography  |x Data processing. 
650 0 |a R (Computer program language) 
650 0 |a Python (Computer program language) 
650 6 |a Oc�eanographie  |0 (CaQQLa)201-0003898  |x Informatique.  |0 (CaQQLa)201-0380011 
650 6 |a R (Langage de programmation)  |0 (CaQQLa)201-0368319 
650 6 |a Python (Langage de programmation)  |0 (CaQQLa)201-0351937 
650 7 |a SCIENCE  |x Earth Sciences  |x Geography.  |2 bisacsh 
650 7 |a SCIENCE  |x Earth Sciences  |x Geology.  |2 bisacsh 
650 7 |a Oceanography  |x Data processing  |2 fast  |0 (OCoLC)fst01043680 
650 7 |a Python (Computer program language)  |2 fast  |0 (OCoLC)fst01084736 
650 7 |a R (Computer program language)  |2 fast  |0 (OCoLC)fst01086207 
776 0 8 |i Print version:  |z 0128134917  |z 9780128134917  |w (OCoLC)1065724851 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128134917  |z Texto completo