Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (188 pages)

ISBN: 0-12-813492-5

Content: "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"--

Note: 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 distributions -- 1.10.1. 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 files -- 2.2.4. 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 Python -- 2.5. 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 values -- 2.6. 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 -- par() function -- 3.1.3. Graphical parameters as arguments in graphics functions -- 3.1.4. Most frequently used graphical parameters -- 3.1.5. Plotting multiple figures -- 3.1.6. Device drivers -- 3.1.7. Example plots in R -- 3.2. Plotting in Python -- 3.2.1. Pyplot API -- 3.2.2. Matplotlib API -- References -- Chapter 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 -- References -- Chapter 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 -- References -- Index -- Back Cover.

Additional Edition: ISBN 0-12-813491-7

Language: English

UID:

Format: 1 online resource (188 pages)

ISBN: 0-12-813492-5

Content: "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"--

Note: 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 distributions -- 1.10.1. 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 files -- 2.2.4. 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 Python -- 2.5. 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 values -- 2.6. 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 -- par() function -- 3.1.3. Graphical parameters as arguments in graphics functions -- 3.1.4. Most frequently used graphical parameters -- 3.1.5. Plotting multiple figures -- 3.1.6. Device drivers -- 3.1.7. Example plots in R -- 3.2. Plotting in Python -- 3.2.1. Pyplot API -- 3.2.2. Matplotlib API -- References -- Chapter 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 -- References -- Chapter 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 -- References -- Index -- Back Cover.

Additional Edition: ISBN 0-12-813491-7

Language: English

UID:

Format: 1 online resource (188 pages)

ISBN: 0-12-813492-5

Content: "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"--

Note: 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 distributions -- 1.10.1. 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 files -- 2.2.4. 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 Python -- 2.5. 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 values -- 2.6. 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 -- par() function -- 3.1.3. Graphical parameters as arguments in graphics functions -- 3.1.4. Most frequently used graphical parameters -- 3.1.5. Plotting multiple figures -- 3.1.6. Device drivers -- 3.1.7. Example plots in R -- 3.2. Plotting in Python -- 3.2.1. Pyplot API -- 3.2.2. Matplotlib API -- References -- Chapter 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 -- References -- Chapter 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 -- References -- Index -- Back Cover.

Additional Edition: ISBN 0-12-813491-7

Language: English