THE PRACTICE OF COMPUTING USING PYTHON

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The Practice Of Computing Using Python

Author : William F. Punch
ISBN : 9780134380179
Genre : Computers
File Size : 20.41 MB
Format : PDF, Mobi
Download : 125
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This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For courses in Python Programming Introduces Python programming with an emphasis on problem-solving Now in its Third Edition, Practice of Computing Using Python continues to effectively introduce readers to computational thinking using Python, with a strong emphasis on problem solving through computer science. The authors have chosen Python for its simplicity, powerful built-in data structures, advanced control constructs, and practicality. The text is built from the ground up for Python programming, rather than having been translated from Java or C++. Focusing on data manipulation and analysis as a theme, the text allows readers to work on real problems using Internet-sourced or self-generated data sets that represent their own work and interests. The authors also emphasize program development and provide readers of all backgrounds with a practical foundation in programming that suit their needs. Among other changes, the Third Edition incorporates a switch to the Anaconda distribution, the SPYDER IDE, and a focus on debugging and GUIs. Also available with MyProgrammingLab™ MyProgrammingLab is an online learning system designed to engage students and improve results. MyProgrammingLab consists of a set of programming exercises correlated to specific Pearson CS1/Intro to Programming textbooks. Through practice exercises and immediate, personalized feedback, MyProgrammingLab improves the programming competence of beginning students who often struggle with the basic concepts of programming languages. Note: You are purchasing a standalone product; MyLab™ & Mastering™ does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase boththe physical text and MyLab & Mastering, search for: 0134520513 / 9780134520513 The Practice of Computing Using Python plus MyProgrammingLab with Pearson eText -- Access Card Package, 3/e Package consists of: 0134381327 / 9780134381329 MyProgrammingLab with Pearson eText -- Access Card Package 0134379764 / 9780134379760 The Practice of Computing Using Python, 3/e
Category: Computers

The Practice Of Computing Using Python

Author : William F. Punch
ISBN : 0136110673
Genre : Computers
File Size : 32.56 MB
Format : PDF
Download : 347
Read : 1002

For CS1 courses in Python Programming including majors and non-majors alike. A problem-solving approach to programming with Python. The Practice of Computing Using Pythonintroduces CS1 students (majors and non-majors) to computational thinking using Python. With data-manipulation as a theme, students quickly see the value in what they’re learning and leave the course with a set of immediately useful computational skills that can be applied to problems they encounter in future pursuits. The book takes an “object-use-first” approach–writing classes are covered only after students have mastered using objects.
Category: Computers

The Practice Of Computing Using Python

Author : William F. Punch
ISBN : 013280557X
Genre : Computers
File Size : 65.97 MB
Format : PDF, ePub, Mobi
Download : 426
Read : 818

NOTE: You are purchasing a standalone product; MyProgrammingLab does not come packaged with this content. If you would like to purchase both the physical text andMyProgrammingLabsearch for ISBN-10: 0132992833/ISBN-13: 9780132992831. That package includes ISBN-10: 013280557X/ISBN-13: 9780132805575 and ISBN-10:0132831325/ISBN-13: 9780132831321. MyProgrammingLab should only be purchased when required by an instructor. A problem-solving approach to programming with Python. The Practice of Computing Using Python introduces CS1 students (majors and non-majors) to computational thinking using Python. With data-manipulation as a theme, readers quickly see the value in what they're learning and leave the course with a set of immediately useful computational skills that can be applied to problems they encounter in future pursuits. The book takes an “object-use-first” approach—writing classes is covered only after students have mastered using objects. This edition is available with MyProgrammingLab, an innovative online homework and assessment tool. Through the power of practice and immediate personalized feedback, MyProgrammingLab helps students fully grasp the logic, semantics, and syntax of programming.
Category: Computers

The Practice Of Computing Using Python With Access Code

Author : William F. Punch
ISBN : 0132992833
Genre : Computers
File Size : 69.85 MB
Format : PDF, Kindle
Download : 453
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NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, and registrations are not transferable. To register for and use Pearson's MyLab & Mastering products, you may also need a Course ID, which your instructor will provide. Used books, rentals, and purchases made outside of Pearson If purchasing or renting from companies other than Pearson, the access codes for Pearson's MyLab & Mastering products may not be included, may be incorrect, or may be previously redeemed. Check with the seller before completing your purchase. A problem-solving approach to programming with Python. The Practice of Computing Using Python introduces CS1 students (majors and non-majors) to computational thinking using Python. With data-manipulation as a theme, readers quickly see the value in what they're learning and leave the course with a set of immediately useful computational skills that can be applied to problems they encounter in future pursuits. The book takes an "object-use-first" approach--writing classes is covered only after students have mastered using objects. 0132992833/9780132992831 Practice of Computing Using Python plus MyProgrammingLab with Pearson eText -- Access Card Package, The, 2/e Package consists of: 013280557X/ 9780132805575 Practice of Computing Using Python, The, 2/e 0132831325/ 9780132831321 MyProgrammingLab with Pearson eText -- Access Card -- for Practice of Computing using Python, 2/e
Category: Computers

Practice Of Computing Using Python The Student Value Edition

Author : William F. Punch
ISBN : 0134380312
Genre : Computers
File Size : 74.28 MB
Format : PDF
Download : 174
Read : 923

NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, and registrations are not transferable. To register for and use Pearson's MyLab & Mastering products, you may also need a Course ID, which your instructor will provide. Used books, rentals, and purchases made outside of Pearson If purchasing or renting from companies other than Pearson, the access codes for Pearson's MyLab & Mastering products may not be included, may be incorrect, or may be previously redeemed. Check with the seller before completing your purchase. "For courses in Python Programming" "This package includes MyProgrammingLab " Introduces Python programming with an emphasis on problem-solving Now in its Third Edition, "Practice of Computing Using Python" continues to effectively introduce readers to computational thinking using Python, with a strong emphasis on problem solving through computer science. The authors have chosen Python for its simplicity, powerful built-in data structures, advanced control constructs, and practicality. The text is built from the ground up for Python programming, rather than having been translated from Java or C++. Focusing on data manipulation and analysis as a theme, the text allows readers to work on real problems using Internet-sourced or self-generated data sets that represent their own work and interests. The authors also emphasize program development and provide readers of all backgrounds with a practical foundation in programming that suit their needs. Among other changes, the Third Edition incorporates a switch to the Anaconda distribution, the SPYDER IDE, and a focus on debugging and GUIs. 0134520513 / 9780134520513" " "The" " Practice of Computing Using Python plus MyProgrammingLab with Pearson eText -- Access Card Package, 3/e " Package consists of: 0134381327 / 9780134381329 " MyProgrammingLab with Pearson eText -- Access Card Package " 0134379764 / 9780134379760 " The Practice of Computing Using Python, 3/e " "
Category: Computers

Multiscale Modeling Of Deep Water Channel Deposits

Author :
ISBN : STANFORD:ns884fs4450
Genre :
File Size : 65.90 MB
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Sedimentological models capture the processes and subsequent deposits that explain the distribution of facies within a depositional system. The first sedimentological models for deep-water depositional systems were portrayed as idealized shelf break to slope submarine basin sediment dispersal systems. These models were developed from ancient outcrop exposures (Mutti and Lucchi, 1972) and from the modern day seafloor (Normark, 1970, 1978). More recent model development has been based largely on observations from modern slope channels including the Amazon Channel (Pirmez and Imran; 2003), offshore West African (Abreu et al., 2003; Deptuck et al., 2003), and attempts at generalization from multiple studies (Mayall et al., 2006), as well as ancient outcrop studies (e.g., Brushy Canyon; Gardner et al., 2003). Concepts from these sedimentological models have been the principle foundation for development of quantitative geostatistical models. A geostatistical model adapts the conceptualization of facies distribution from the sedimentological model. This information is then coded into a three-dimensional, gridded computer model directly constrained to available data (i.e., wireline logs, core data, and seismic attributes). Geostatistical models developed for deep-water depositional systems have primarily focused on either sinuous channels confined by levees or erosional surfaces (e.g., Larue and Hovadik, 2006; Labourdette et al., 2007; Pyrcz et al., 2008; McHargue et al., 2010; Sylvester et al., 2010) or basin-floor or overbank lobes associated with loss of confinement from sinuous channels (Pyrcz et al., 2005; Wellner et al., 2006; Zhang et al., 2009). Although widely used, such geostatistical models have limited applicability in fitting all deep-water depositional systems, and cases exists that require modification of such models or creation of entirely new models. In this dissertation I show the importance of synthesizing sedimentological and geostatistical models based on observations from the data. The primary objectives of this dissertation are 1) to present methodologies to enable the creation of better sedimentological models from remote sensing data, and 2) to present a means to model depositional architectures for a system that cannot currently be captured with standard geostatistical modeling approaches. The main contributions are threefold. The first contribution, presented in Chapter 1, is a methodology designed to extract subseismic, lithologic information from inverted pre-stack seismic reflectivities. Also, in Chapter 1, the predictive power of this methodology is demonstrated on a dataset from the subsurface of the Molasse Basin in Upper Austria. Beyond this dissertation, Bernhardt et al. (in review) adopted the methodology to support the development of a more predictive sedimentological model for the same dataset. The second contribution, presented in Chapter 2, is a new approach for building predictive quantitative spatial models for a deep-water channel belt, in which sand deposition is controlled by mass-transport-deposit-topography. This methodology leverages sedimentological interpretations derived from subseismic, lithologic information as presented in Chapter 1 and the sedimentological work of Bernhardt et al. (in review). The final contribution of this dissertation is presented in two outcrop studies. Chapters 3 and 4 utilize extensive data collected from deep-water channel outcrops to build digital outcrop models. The model from Chapter 3 is used to demonstrate the predictive power of pre-stack seismic-reflectivity data in interpreting the large-scale architecture of a heterolithic deep-water channel system exposed in the sea cliffs along Blacks Beach near La Jolla, California. Finally, the outcrop modeling study presented in Chapter 4 presents a methodology to capture structural and stratigraphic uncertainty in outcrop observations in order to analyze the three-dimensional channel morphology of the Cerro
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