Data structures and algorithms in Python:
This textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. It is the first mainstream object-oriented book available not only for the Python data structures course, but also provides a comprehensive introduction to data structures and algorithms,...
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| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
John Wiley & Sons, Inc.
[2013]
|
| Subjects: | |
| Online Access: | FHD01 |
| Summary: | This textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. It is the first mainstream object-oriented book available not only for the Python data structures course, but also provides a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation |
| Physical Description: | 1 Online-Ressource (xix, 748 Seiten) |
| ISBN: | 9781118476734 |
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| 245 | 1 | 0 | |a Data structures and algorithms in Python |c Michael T. Goodrich, Department of Computer Science, University of California, Irvine, Roberto Tamassia, Department of Computer Science, Brown University, Michael H. Goldwasser, Department of Mathematics and Computer Science, Saint Louis University |
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| 505 | 8 | |a Cover; Title Page; Copyright Page; Preface; Contents; 1 Python Primer; 1.1 Python Overview; 1.1.1 The Python Interpreter; 1.1.2 Preview of a Python Program; 1.2 Objects in Python; 1.2.1 Identifiers, Objects, and the Assignment Statement; 1.2.2 Creating and Using Objects; 1.2.3 Python's Built-In Classes; 1.3 Expressions, Operators, and Precedence; 1.3.1 Compound Expressions and Operator Precedence; 1.4 Control Flow; 1.4.1 Conditionals; 1.4.2 Loops; 1.5 Functions; 1.5.1 Information Passing; 1.5.2 Python's Built-In Functions; 1.6 Simple Input and Output; 1.6.1 Console Input and Output | |
| 505 | 8 | |a 1.6.2 Files1.7 Exception Handling; 1.7.1 Raising an Exception; 1.7.2 Catching an Exception; 1.8 Iterators and Generators; 1.9 Additional Python Conveniences; 1.9.1 Conditional Expressions; 1.9.2 Comprehension Syntax; 1.9.3 Packing and Unpacking of Sequences; 1.10 Scopes and Namespaces; 1.11 Modules and the Import Statement; 1.11.1 Existing Modules; 1.12 Exercises; 2 Object-Oriented Programming; 2.1 Goals, Principles, and Patterns; 2.1.1 Object-Oriented Design Goals; 2.1.2 Object-Oriented Design Principles; 2.1.3 Design Patterns; 2.2 Software Development; 2.2.1 Design; 2.2.2 Pseudo-Code | |
| 505 | 8 | |a 2.2.3 Coding Style and Documentation2.2.4 Testing and Debugging; 2.3 Class Definitions; 2.3.1 Example: CreditCard Class; 2.3.2 Operator Overloading and Python's Special Methods; 2.3.3 Example: Multidimensional Vector Class; 2.3.4 Iterators; 2.3.5 Example: Range Class; 2.4 Inheritance; 2.4.1 Extending the CreditCard Class; 2.4.2 Hierarchy of Numeric Progressions; 2.4.3 Abstract Base Classes; 2.5 Namespaces and Object-Orientation; 2.5.1 Instance and Class Namespaces; 2.5.2 Name Resolution and Dynamic Dispatch; 2.6 Shallow and Deep Copying; 2.7 Exercises; 3 Algorithm Analysis | |
| 505 | 8 | |a 3.1 Experimental Studies3.1.1 Moving Beyond Experimental Analysis; 3.2 The Seven Functions Used in This Book; 3.2.1 Comparing Growth Rates; 3.3 Asymptotic Analysis; 3.3.1 The "Big-Oh" Notation; 3.3.2 Comparative Analysis; 3.3.3 Examples of Algorithm Analysis; 3.4 Simple Justification Techniques; 3.4.1 By Example; 3.4.2 The "Contra" Attack; 3.4.3 Induction and Loop Invariants; 3.5 Exercises; 4 Recursion; 4.1 Illustrative Examples; 4.1.1 The Factorial Function; 4.1.2 Drawing an English Ruler; 4.1.3 Binary Search; 4.1.4 File Systems; 4.2 Analyzing Recursive Algorithms; 4.3 Recursion Run Amok | |
| 505 | 8 | |a 4.3.1 Maximum Recursive Depth in Python4.4 Further Examples of Recursion; 4.4.1 Linear Recursion; 4.4.2 Binary Recursion; 4.4.3 Multiple Recursion; 4.5 Designing Recursive Algorithms; 4.6 Eliminating Tail Recursion; 4.7 Exercises; 5 Array-Based Sequences; 5.1 Python's Sequence Types; 5.2 Low-Level Arrays; 5.2.1 Referential Arrays; 5.2.2 Compact Arrays in Python; 5.3 Dynamic Arrays and Amortization; 5.3.1 Implementing a Dynamic Array; 5.3.2 Amortized Analysis of Dynamic Arrays; 5.3.3 Python's List Class; 5.4 Efficiency of Python's Sequence Types; 5.4.1 Python's List and Tuple Classes | |
| 520 | |a This textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. It is the first mainstream object-oriented book available not only for the Python data structures course, but also provides a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation | ||
| 650 | 4 | |a Python (Computer program language) | |
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| 650 | 7 | |a Python (Computer program language) |2 fast | |
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| author | Goodrich, Michael T. 1961- Tamassia, Roberto 1960- Goldwasser, Michael H. 1969- |
| author_GND | (DE-588)121131181 (DE-588)141557834 (DE-588)133831086 |
| author_facet | Goodrich, Michael T. 1961- Tamassia, Roberto 1960- Goldwasser, Michael H. 1969- |
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| author_sort | Goodrich, Michael T. 1961- |
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| bvnumber | BV046326249 |
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| contents | Cover; Title Page; Copyright Page; Preface; Contents; 1 Python Primer; 1.1 Python Overview; 1.1.1 The Python Interpreter; 1.1.2 Preview of a Python Program; 1.2 Objects in Python; 1.2.1 Identifiers, Objects, and the Assignment Statement; 1.2.2 Creating and Using Objects; 1.2.3 Python's Built-In Classes; 1.3 Expressions, Operators, and Precedence; 1.3.1 Compound Expressions and Operator Precedence; 1.4 Control Flow; 1.4.1 Conditionals; 1.4.2 Loops; 1.5 Functions; 1.5.1 Information Passing; 1.5.2 Python's Built-In Functions; 1.6 Simple Input and Output; 1.6.1 Console Input and Output 1.6.2 Files1.7 Exception Handling; 1.7.1 Raising an Exception; 1.7.2 Catching an Exception; 1.8 Iterators and Generators; 1.9 Additional Python Conveniences; 1.9.1 Conditional Expressions; 1.9.2 Comprehension Syntax; 1.9.3 Packing and Unpacking of Sequences; 1.10 Scopes and Namespaces; 1.11 Modules and the Import Statement; 1.11.1 Existing Modules; 1.12 Exercises; 2 Object-Oriented Programming; 2.1 Goals, Principles, and Patterns; 2.1.1 Object-Oriented Design Goals; 2.1.2 Object-Oriented Design Principles; 2.1.3 Design Patterns; 2.2 Software Development; 2.2.1 Design; 2.2.2 Pseudo-Code 2.2.3 Coding Style and Documentation2.2.4 Testing and Debugging; 2.3 Class Definitions; 2.3.1 Example: CreditCard Class; 2.3.2 Operator Overloading and Python's Special Methods; 2.3.3 Example: Multidimensional Vector Class; 2.3.4 Iterators; 2.3.5 Example: Range Class; 2.4 Inheritance; 2.4.1 Extending the CreditCard Class; 2.4.2 Hierarchy of Numeric Progressions; 2.4.3 Abstract Base Classes; 2.5 Namespaces and Object-Orientation; 2.5.1 Instance and Class Namespaces; 2.5.2 Name Resolution and Dynamic Dispatch; 2.6 Shallow and Deep Copying; 2.7 Exercises; 3 Algorithm Analysis 3.1 Experimental Studies3.1.1 Moving Beyond Experimental Analysis; 3.2 The Seven Functions Used in This Book; 3.2.1 Comparing Growth Rates; 3.3 Asymptotic Analysis; 3.3.1 The "Big-Oh" Notation; 3.3.2 Comparative Analysis; 3.3.3 Examples of Algorithm Analysis; 3.4 Simple Justification Techniques; 3.4.1 By Example; 3.4.2 The "Contra" Attack; 3.4.3 Induction and Loop Invariants; 3.5 Exercises; 4 Recursion; 4.1 Illustrative Examples; 4.1.1 The Factorial Function; 4.1.2 Drawing an English Ruler; 4.1.3 Binary Search; 4.1.4 File Systems; 4.2 Analyzing Recursive Algorithms; 4.3 Recursion Run Amok 4.3.1 Maximum Recursive Depth in Python4.4 Further Examples of Recursion; 4.4.1 Linear Recursion; 4.4.2 Binary Recursion; 4.4.3 Multiple Recursion; 4.5 Designing Recursive Algorithms; 4.6 Eliminating Tail Recursion; 4.7 Exercises; 5 Array-Based Sequences; 5.1 Python's Sequence Types; 5.2 Low-Level Arrays; 5.2.1 Referential Arrays; 5.2.2 Compact Arrays in Python; 5.3 Dynamic Arrays and Amortization; 5.3.1 Implementing a Dynamic Array; 5.3.2 Amortized Analysis of Dynamic Arrays; 5.3.3 Python's List Class; 5.4 Efficiency of Python's Sequence Types; 5.4.1 Python's List and Tuple Classes |
| ctrlnum | (OCoLC)880838944 (DE-599)BVBBV046326249 |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV046326249 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:41:43Z |
| institution | BVB |
| isbn | 9781118476734 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031703196 |
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| physical | 1 Online-Ressource (xix, 748 Seiten) |
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| publishDate | 2013 |
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| record_format | marc |
| spelling | Goodrich, Michael T. 1961- Verfasser (DE-588)121131181 aut Data structures and algorithms in Python Michael T. Goodrich, Department of Computer Science, University of California, Irvine, Roberto Tamassia, Department of Computer Science, Brown University, Michael H. Goldwasser, Department of Mathematics and Computer Science, Saint Louis University New York, NY John Wiley & Sons, Inc. [2013] 1 Online-Ressource (xix, 748 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover; Title Page; Copyright Page; Preface; Contents; 1 Python Primer; 1.1 Python Overview; 1.1.1 The Python Interpreter; 1.1.2 Preview of a Python Program; 1.2 Objects in Python; 1.2.1 Identifiers, Objects, and the Assignment Statement; 1.2.2 Creating and Using Objects; 1.2.3 Python's Built-In Classes; 1.3 Expressions, Operators, and Precedence; 1.3.1 Compound Expressions and Operator Precedence; 1.4 Control Flow; 1.4.1 Conditionals; 1.4.2 Loops; 1.5 Functions; 1.5.1 Information Passing; 1.5.2 Python's Built-In Functions; 1.6 Simple Input and Output; 1.6.1 Console Input and Output 1.6.2 Files1.7 Exception Handling; 1.7.1 Raising an Exception; 1.7.2 Catching an Exception; 1.8 Iterators and Generators; 1.9 Additional Python Conveniences; 1.9.1 Conditional Expressions; 1.9.2 Comprehension Syntax; 1.9.3 Packing and Unpacking of Sequences; 1.10 Scopes and Namespaces; 1.11 Modules and the Import Statement; 1.11.1 Existing Modules; 1.12 Exercises; 2 Object-Oriented Programming; 2.1 Goals, Principles, and Patterns; 2.1.1 Object-Oriented Design Goals; 2.1.2 Object-Oriented Design Principles; 2.1.3 Design Patterns; 2.2 Software Development; 2.2.1 Design; 2.2.2 Pseudo-Code 2.2.3 Coding Style and Documentation2.2.4 Testing and Debugging; 2.3 Class Definitions; 2.3.1 Example: CreditCard Class; 2.3.2 Operator Overloading and Python's Special Methods; 2.3.3 Example: Multidimensional Vector Class; 2.3.4 Iterators; 2.3.5 Example: Range Class; 2.4 Inheritance; 2.4.1 Extending the CreditCard Class; 2.4.2 Hierarchy of Numeric Progressions; 2.4.3 Abstract Base Classes; 2.5 Namespaces and Object-Orientation; 2.5.1 Instance and Class Namespaces; 2.5.2 Name Resolution and Dynamic Dispatch; 2.6 Shallow and Deep Copying; 2.7 Exercises; 3 Algorithm Analysis 3.1 Experimental Studies3.1.1 Moving Beyond Experimental Analysis; 3.2 The Seven Functions Used in This Book; 3.2.1 Comparing Growth Rates; 3.3 Asymptotic Analysis; 3.3.1 The "Big-Oh" Notation; 3.3.2 Comparative Analysis; 3.3.3 Examples of Algorithm Analysis; 3.4 Simple Justification Techniques; 3.4.1 By Example; 3.4.2 The "Contra" Attack; 3.4.3 Induction and Loop Invariants; 3.5 Exercises; 4 Recursion; 4.1 Illustrative Examples; 4.1.1 The Factorial Function; 4.1.2 Drawing an English Ruler; 4.1.3 Binary Search; 4.1.4 File Systems; 4.2 Analyzing Recursive Algorithms; 4.3 Recursion Run Amok 4.3.1 Maximum Recursive Depth in Python4.4 Further Examples of Recursion; 4.4.1 Linear Recursion; 4.4.2 Binary Recursion; 4.4.3 Multiple Recursion; 4.5 Designing Recursive Algorithms; 4.6 Eliminating Tail Recursion; 4.7 Exercises; 5 Array-Based Sequences; 5.1 Python's Sequence Types; 5.2 Low-Level Arrays; 5.2.1 Referential Arrays; 5.2.2 Compact Arrays in Python; 5.3 Dynamic Arrays and Amortization; 5.3.1 Implementing a Dynamic Array; 5.3.2 Amortized Analysis of Dynamic Arrays; 5.3.3 Python's List Class; 5.4 Efficiency of Python's Sequence Types; 5.4.1 Python's List and Tuple Classes This textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. It is the first mainstream object-oriented book available not only for the Python data structures course, but also provides a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation Python (Computer program language) Data structures (Computer science) Computer algorithms Computer algorithms fast Data structures (Computer science) fast Python (Computer program language) fast Tamassia, Roberto 1960- Verfasser (DE-588)141557834 aut Goldwasser, Michael H. 1969- Verfasser (DE-588)133831086 aut Erscheint auch als Druck-Ausgabe 978-1-118-29027-9 |
| spellingShingle | Goodrich, Michael T. 1961- Tamassia, Roberto 1960- Goldwasser, Michael H. 1969- Data structures and algorithms in Python Cover; Title Page; Copyright Page; Preface; Contents; 1 Python Primer; 1.1 Python Overview; 1.1.1 The Python Interpreter; 1.1.2 Preview of a Python Program; 1.2 Objects in Python; 1.2.1 Identifiers, Objects, and the Assignment Statement; 1.2.2 Creating and Using Objects; 1.2.3 Python's Built-In Classes; 1.3 Expressions, Operators, and Precedence; 1.3.1 Compound Expressions and Operator Precedence; 1.4 Control Flow; 1.4.1 Conditionals; 1.4.2 Loops; 1.5 Functions; 1.5.1 Information Passing; 1.5.2 Python's Built-In Functions; 1.6 Simple Input and Output; 1.6.1 Console Input and Output 1.6.2 Files1.7 Exception Handling; 1.7.1 Raising an Exception; 1.7.2 Catching an Exception; 1.8 Iterators and Generators; 1.9 Additional Python Conveniences; 1.9.1 Conditional Expressions; 1.9.2 Comprehension Syntax; 1.9.3 Packing and Unpacking of Sequences; 1.10 Scopes and Namespaces; 1.11 Modules and the Import Statement; 1.11.1 Existing Modules; 1.12 Exercises; 2 Object-Oriented Programming; 2.1 Goals, Principles, and Patterns; 2.1.1 Object-Oriented Design Goals; 2.1.2 Object-Oriented Design Principles; 2.1.3 Design Patterns; 2.2 Software Development; 2.2.1 Design; 2.2.2 Pseudo-Code 2.2.3 Coding Style and Documentation2.2.4 Testing and Debugging; 2.3 Class Definitions; 2.3.1 Example: CreditCard Class; 2.3.2 Operator Overloading and Python's Special Methods; 2.3.3 Example: Multidimensional Vector Class; 2.3.4 Iterators; 2.3.5 Example: Range Class; 2.4 Inheritance; 2.4.1 Extending the CreditCard Class; 2.4.2 Hierarchy of Numeric Progressions; 2.4.3 Abstract Base Classes; 2.5 Namespaces and Object-Orientation; 2.5.1 Instance and Class Namespaces; 2.5.2 Name Resolution and Dynamic Dispatch; 2.6 Shallow and Deep Copying; 2.7 Exercises; 3 Algorithm Analysis 3.1 Experimental Studies3.1.1 Moving Beyond Experimental Analysis; 3.2 The Seven Functions Used in This Book; 3.2.1 Comparing Growth Rates; 3.3 Asymptotic Analysis; 3.3.1 The "Big-Oh" Notation; 3.3.2 Comparative Analysis; 3.3.3 Examples of Algorithm Analysis; 3.4 Simple Justification Techniques; 3.4.1 By Example; 3.4.2 The "Contra" Attack; 3.4.3 Induction and Loop Invariants; 3.5 Exercises; 4 Recursion; 4.1 Illustrative Examples; 4.1.1 The Factorial Function; 4.1.2 Drawing an English Ruler; 4.1.3 Binary Search; 4.1.4 File Systems; 4.2 Analyzing Recursive Algorithms; 4.3 Recursion Run Amok 4.3.1 Maximum Recursive Depth in Python4.4 Further Examples of Recursion; 4.4.1 Linear Recursion; 4.4.2 Binary Recursion; 4.4.3 Multiple Recursion; 4.5 Designing Recursive Algorithms; 4.6 Eliminating Tail Recursion; 4.7 Exercises; 5 Array-Based Sequences; 5.1 Python's Sequence Types; 5.2 Low-Level Arrays; 5.2.1 Referential Arrays; 5.2.2 Compact Arrays in Python; 5.3 Dynamic Arrays and Amortization; 5.3.1 Implementing a Dynamic Array; 5.3.2 Amortized Analysis of Dynamic Arrays; 5.3.3 Python's List Class; 5.4 Efficiency of Python's Sequence Types; 5.4.1 Python's List and Tuple Classes Python (Computer program language) Data structures (Computer science) Computer algorithms Computer algorithms fast Data structures (Computer science) fast Python (Computer program language) fast |
| title | Data structures and algorithms in Python |
| title_auth | Data structures and algorithms in Python |
| title_exact_search | Data structures and algorithms in Python |
| title_full | Data structures and algorithms in Python Michael T. Goodrich, Department of Computer Science, University of California, Irvine, Roberto Tamassia, Department of Computer Science, Brown University, Michael H. Goldwasser, Department of Mathematics and Computer Science, Saint Louis University |
| title_fullStr | Data structures and algorithms in Python Michael T. Goodrich, Department of Computer Science, University of California, Irvine, Roberto Tamassia, Department of Computer Science, Brown University, Michael H. Goldwasser, Department of Mathematics and Computer Science, Saint Louis University |
| title_full_unstemmed | Data structures and algorithms in Python Michael T. Goodrich, Department of Computer Science, University of California, Irvine, Roberto Tamassia, Department of Computer Science, Brown University, Michael H. Goldwasser, Department of Mathematics and Computer Science, Saint Louis University |
| title_short | Data structures and algorithms in Python |
| title_sort | data structures and algorithms in python |
| topic | Python (Computer program language) Data structures (Computer science) Computer algorithms Computer algorithms fast Data structures (Computer science) fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Data structures (Computer science) Computer algorithms |
| work_keys_str_mv | AT goodrichmichaelt datastructuresandalgorithmsinpython AT tamassiaroberto datastructuresandalgorithmsinpython AT goldwassermichaelh datastructuresandalgorithmsinpython |