Python Programming: An Introduction to Computer Science, Fourth Edition - Tapa blanda

Sinopsis

This new edition has all the familiar elements that made previous editions a leading intro textbook, including:

Extensive use of computer graphics. Coverage includes a simple-to-use graphics package (provided as a Python module) that allows students both to learn the principles of computer graphics and to practice object-oriented concepts without the complexity inherent in a full-blown graphics library and event-driven programming.

Interesting examples. This book is packed with complete programming examples to solve real problems. Readable prose. Key computer science concepts are introduced in a natural way as an outgrowth of a developing discussion.

Flexible spiral coverage. Each chapter is organized so that new ideas are introduced gradually, giving students time to assimilate an increasing level of detail. Ideas that take more time to master are introduced in early chapters and reinforced in later chapters.

Just-in-time object coverage. This book gradually introduces object concepts after a brief initial grounding in the basics of imperative programming. Students learn multiple design techniques, including top-down (functional decomposition), spiral (prototyping), and object-oriented methods.

Extensive end-of-chapter problems. Exercises at the end of every chapter provide ample opportunity for students to reinforce their mastery of the chapter material and to practice new programming skills.

NEW TO THE FOURTH EDITION

The focus of this edition continues to be on providing a solid foundation in computer science appropriate for a first computing course for CS majors (CS1). At the same time, many learners outside of CS are also keen to develop programming skills. All budding programmers are best served by a firm grounding in computer science and software design, so the material here has been streamlined and reorganized to make it more useful as a reference for learners in CS-adjacent fields such as the natural and social sciences, data science, and intelligent systems. Special attention is paid to essential idioms that students are likely to see in Python code that they encounter in the wild. In addition to technology updates throughout, those familiar with the previous edition will notice these changes:

Addition of new material on operating systems and an extended discussion of persistent data including file systems, binary files, and data serialization over networks.

Inclusion of new and additional Python features: f-strings for string formatting, context managers, list comprehensions, sequence functions, and the pathlib and pickle libraries.

Streamlining of early chapters including covering all control structures before going into depth on sequence types such as strings and lists.

Consolidation of objected-oriented design case studies in a dedicated chapter.

Additional programming exercises.

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