Intro to Python for Computer Science and Data Science

Thistitle is a Pearson Global Edition. The Editorial team at Pearson has workedclosely with educators around the world to include content which is especiallyrelevant to students outside the United States. For introductory-level Python programming and/ordata-science courses. A groundbreaking, flexi...

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Detalles Bibliográficos
Autor principal:	Deitel, Paul (-)
Otros Autores:	Dietal, Harvey, autor (autor)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Harlow : Pearson Education, Limited 2021.
Materias:	Python (Computer program language) Python (lenguaje de programa informático) Libros electrónicos.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009667234306719

Tabla de Contenidos:

Cover
Digital Resources for Students
Intro to Python® for Computer Science and Data Science
Half Title
Copyright
Title Page
Dedication
Contents
Preface
Before You Begin
1. Introduction to Computers and Python
1.1 Introduction
1.2 Hardware and Software
1.2.1 Moore's Law
1.2.2 Computer Organization
1.3 Data Hierarchy
1.4 Machine Languages, Assembly Languages and High-Level Languages
1.5 Introduction to Object Technology
1.6 Operating Systems
1.7 Python
1.8 It's the Libraries!
1.8.1 Python Standard Library
1.8.2 Data-Science Libraries
1.9 Other Popular Programming Languages
1.10 Test-Drives: Using IPython and Jupyter Notebooks
1.10.1 Using IPython Interactive Mode as a Calculator
1.10.2 Executing a Python Program Using the IPython Interpreter
1.10.3 Writing and Executing Code in a Jupyter Notebook
1.11 Internet and World Wide Web
1.11.1 Internet: A Network of Networks
1.11.2 World Wide Web: Making the Internet User-Friendly
1.11.3 The Cloud
1.11.4 Internet of Things
1.12 Software Technologies
1.13 How Big Is Big Data?
1.13.1 Big Data Analytics
1.13.2 Data Science and Big Data Are Making a Difference: Use Cases
1.14 Case Study-A Big-Data Mobile Application
1.15 Intro to Data Science: Artificial Intelligence-at the Intersection of CS and Data Science
2. Introduction to Python Programming
2.1 Introduction
2.2 Variables and Assignment Statements
2.3 Arithmetic
2.4 Function print and an Intro to Single- and Double-Quoted Strings
2.5 Triple-Quoted Strings
2.6 Getting Input from the User
2.7 Decision Making: The if Statement and Comparison Operators
2.8 Objects and Dynamic Typing
2.9 Intro to Data Science: Basic Descriptive Statistics
2.10 Wrap-Up
3. Control Statements and Program Development.
3.1 Introduction
3.2 Algorithms
3.3 Pseudocode
3.4 Control Statements
3.5 if Statement
3.6 if…else and if…elif…else Statements
3.7 while Statement
3.8 for Statement
3.8.1 Iterables, Lists and Iterators
3.8.2 Built-In range Function
3.9 Augmented Assignments
3.10 Program Development: Sequence-Controlled Repetition
3.10.1 Requirements Statement
3.10.2 Pseudocode for the Algorithm
3.10.3 Coding the Algorithm in Python
3.10.4 Introduction to Formatted Strings
3.11 Program Development: Sentinel-Controlled Repetition
3.12 Program Development: Nested Control Statements
3.13 Built-In Function range: A Deeper Look
3.14 Using Type Decimal for Monetary Amounts
3.15 break and continue Statements
3.16 Boolean Operators and, or and not
3.17 Intro to Data Science: Measures of Central Tendency-Mean, Median and Mode
3.18 Wrap-Up
4. Functions
4.1 Introduction
4.2 Defining Functions
4.3 Functions with Multiple Parameters
4.4 Random-Number Generation
4.5 Case Study: A Game of Chance
4.6 Python Standard Library
4.7 math Module Functions
4.8 Using IPython Tab Completion for Discovery
4.9 Default Parameter Values
4.10 Keyword Arguments
4.11 Arbitrary Argument Lists
4.12 Methods: Functions That Belong to Objects
4.13 Scope Rules
4.14 import: A Deeper Look
4.15 Passing Arguments to Functions: A Deeper Look
4.16 Function-Call Stack
4.17 Functional-Style Programming
4.18 Intro to Data Science: Measures of Dispersion
4.19 Wrap-Up
5. Sequences: Lists and Tuples
5.1 Introduction
5.2 Lists
5.3 Tuples
5.4 Unpacking Sequences
5.5 Sequence Slicing
5.6 del Statement
5.7 Passing Lists to Functions
5.8 Sorting Lists
5.9 Searching Sequences
5.10 Other List Methods
5.11 Simulating Stacks with Lists
5.12 List Comprehensions.
5.13 Generator Expressions
5.14 Filter, Map and Reduce
5.15 Other Sequence Processing Functions
5.16 Two-Dimensional Lists
5.17 Intro to Data Science: Simulation and Static Visualizations
5.17.1 Sample Graphs for 600, 60,000 and 6,000,000 Die Rolls
5.17.2 Visualizing Die-Roll Frequencies and Percentages
5.18 Wrap-Up
6. Dictionaries and Sets
6.1 Introduction
6.2 Dictionaries
6.2.1 Creating a Dictionary
6.2.2 Iterating through a Dictionary
6.2.3 Basic Dictionary Operations
6.2.4 Dictionary Methods keys and values
6.2.5 Dictionary Comparisons
6.2.6 Example: Dictionary of Student Grades
6.2.7 Example: Word Counts
6.2.8 Dictionary Method update
6.2.9 Dictionary Comprehensions
6.3 Sets
6.3.1 Comparing Sets
6.3.2 Mathematical Set Operations
6.3.3 Mutable Set Operators and Methods
6.3.4 Set Comprehensions
6.4 Intro to Data Science: Dynamic Visualizations
6.4.1 How Dynamic Visualization Works
6.4.2 Implementing a Dynamic Visualization
6.5 Wrap-Up
7. Array-Oriented Programming with NumPy
7.1 Introduction
7.2 Creating arrays from Existing Data
7.3 array Attributes
7.4 Filling arrays with Specific Values
7.5 Creating arrays from Ranges
7.6 List vs. array Performance: Introducing %timeit
7.7 array Operators
7.8 NumPy Calculation Methods
7.9 Universal Functions
7.10 Indexing and Slicing
7.11 Views: Shallow Copies
7.12 Deep Copies
7.13 Reshaping and Transposing
7.14 Intro to Data Science: pandas Series and DataFrames
7.14.1 pandas Series
7.14.2 DataFrames
7.15 Wrap-Up
8. Strings: A Deeper Look
8.1 Introduction
8.2 Formatting Strings
8.2.1 Presentation Types
8.2.2 Field Widths and Alignment
8.2.3 Numeric Formatting
8.2.4 String's format Method
8.3 Concatenating and Repeating Strings.
8.4 Stripping Whitespace from Strings
8.5 Changing Character Case
8.6 Comparison Operators for Strings
8.7 Searching for Substrings
8.8 Replacing Substrings
8.9 Splitting and Joining Strings
8.10 Characters and Character-Testing Methods
8.11 Raw Strings
8.12 Introduction to Regular Expressions
8.12.1 re Module and Function fullmatch
8.12.2 Replacing Substrings and Splitting Strings
8.12.3 Other Search Functions
Accessing Matches
8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging
8.14 Wrap-Up
9. Files and Exceptions
9.1 Introduction
9.2 Files
9.3 Text-File Processing
9.3.1 Writing to a Text File: Introducing the with Statement
9.3.2 Reading Data from a Text File
9.4 Updating Text Files
9.5 Serialization with JSON
9.6 Focus on Security: pickle Serialization and Deserialization
9.7 Additional Notes Regarding Files
9.8 Handling Exceptions
9.8.1 Division by Zero and Invalid Input
9.8.2 try Statements
9.8.3 Catching Multiple Exceptions in One except Clause
9.8.4 What Exceptions Does a Function or Method Raise?
9.8.5 What Code Should Be Placed in a try Suite?
9.9 finally Clause
9.10 Explicitly Raising an Exception
9.11 (Optional) Stack Unwinding and Tracebacks
9.12 Intro to Data Science: Working with CSV Files
9.12.1 Python Standard Library Module csv
9.12.2 Reading CSV Files into Pandas DataFrames
9.12.3 Reading the Titanic Disaster Dataset
9.12.4 Simple Data Analysis with the Titanic Disaster Dataset
9.12.5 Passenger Age Histogram
9.13 Wrap-Up
10. Object-Oriented Programming
10.1 Introduction
10.2 Custom Class Account
10.2.1 Test-Driving Class Account
10.2.2 Account Class Definition
10.2.3 Composition: Object References as Members of Classes
10.3 Controlling Access to Attributes.
10.4 Properties for Data Access
10.4.1 Test-Driving Class Time
10.4.2 Class Time Definition
10.4.3 Class Time Definition Design Notes
10.5 Simulating "Private" Attributes
10.6 Case Study: Card Shuffling and Dealing Simulation
10.6.1 Test-Driving Classes Card and DeckOfCards
10.6.2 Class Card-Introducing Class Attributes
10.6.3 Class DeckOfCards
10.6.4 Displaying Card Images with Matplotlib
10.7 Inheritance: Base Classes and Subclasses
10.8 Building an Inheritance Hierarchy
Introducing Polymorphism
10.8.1 Base Class CommissionEmployee
10.8.2 Subclass SalariedCommissionEmployee
10.8.3 Processing CommissionEmployees and SalariedCommissionEmployees Polymorphically
10.8.4 A Note About Object-Based and Object-Oriented Programming
10.9 Duck Typing and Polymorphism
10.10 Operator Overloading
10.10.1 Test-Driving Class Complex
10.10.2 Class Complex Definition
10.11 Exception Class Hierarchy and Custom Exceptions
10.12 Named Tuples
10.13 A Brief Intro to Python 3.7's New Data Classes
10.13.1 Creating a Card Data Class
10.13.2 Using the Card Data Class
10.13.3 Data Class Advantages over Named Tuples
10.13.4 Data Class Advantages over Traditional Classes
10.14 Unit Testing with Docstrings and doctest
10.15 Namespaces and Scopes
10.16 Intro to Data Science: Time Series and Simple Linear Regression
10.17 Wrap-Up
11. Computer Science Thinking: Recursion, Searching, Sorting and Big O
11.1 Introduction
11.2 Factorials
11.3 Recursive Factorial Example
11.4 Recursive Fibonacci Series Example
11.5 Recursion vs. Iteration
11.6 Searching and Sorting
11.7 Linear Search
11.8 Efficiency of Algorithms: Big O
11.9 Binary Search
11.9.1 Binary Search Implementation
11.9.2 Big O of the Binary Search
11.10 Sorting Algorithms
11.11 Selection Sort.
11.11.1 Selection Sort Implementation.

Intro to Python for Computer Science and Data Science

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