book

Python for Programmers

by Paul Deitel, Harvey Deitel

March 2019

Intermediate to advanced

640 pages

23h 40m

English

Pearson

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Includes

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Jobs Requiring Data Science SkillsModular ArchitectureKey FeaturesChapter DependenciesJupyter NotebooksDockerSpecial Feature: IBM Watson Analytics and Cognitive ComputingTeaching ApproachSoftware Used in the BookPython DocumentationGetting Your Questions AnsweredGetting Jupyter HelpSupplementsKeeping in Touch with the AuthorsAcknowledgmentsAbout the AuthorsAbout Deitel® & Associates, Inc.
Font and Naming ConventionsGetting the Code ExamplesStructure of the examples FolderInstalling AnacondaUpdating AnacondaPackage ManagersInstalling the Prospector Static Code Analysis ToolInstalling jupyter-matplotlibInstalling the Other PackagesGet a Twitter Developer AccountInternet Connection Required in Some ChaptersSlight Differences in Program OutputsGetting Your Questions Answered

1.1 Introduction1.2 A Quick Review of Object Technology Basics1.3 Python1.4 It’ s the Libraries!1.5 Test-Drives: Using IPython and Jupyter Notebooks1.6 The Cloud and the Internet of Things1.7 How Big Is Big Data?1.8 Case Study—A Big-Data Mobile Application1.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science1.10 Wrap-Up
2.1 Introduction2.2 Variables and Assignment Statements2.3 Arithmetic2.4 Function print and an Intro to Single- and Double-Quoted Strings2.5 Triple-Quoted Strings2.6 Getting Input from the User2.7 Decision Making: The if Statement and Comparison Operators2.8 Objects and Dynamic Typing2.9 Intro to Data Science: Basic Descriptive Statistics2.10 Wrap-Up
3.1 Introduction3.2 Control Statements3.3 if Statement3.4 if else and if3.5 while Statement3.6 for Statement3.7 Augmented Assignments3.8 Sequence-Controlled Iteration; Formatted Strings3.9 Sentinel-Controlled Iteration3.10 Built-In Function range: A Deeper Look3.11 Using Type Decimal for Monetary Amounts3.12 break and continue Statements3.13 Boolean Operators and, or and not3.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode3.15 Wrap-Up
4.1 Introduction4.2 Defining Functions4.3 Functions with Multiple Parameters4.4 Random-Number Generation4.5 Case Study: A Game of Chance4.6 Python Standard Library4.7 math Module Functions4.8 Using IPython Tab Completion for Discovery4.9 Default Parameter Values4.10 Keyword Arguments4.11 Arbitrary Argument Lists4.12 Methods: Functions That Belong to Objects4.13 Scope Rules4.14 import: A Deeper Look4.15 Passing Arguments to Functions: A Deeper Look4.16 Recursion4.17 Functional-Style Programming4.18 Intro to Data Science: Measures of Dispersion4.19 Wrap-Up
5.1 Introduction5.2 Lists5.3 Tuples5.4 Unpacking Sequences5.5 Sequence Slicing5.6 del Statement5.7 Passing Lists to Functions5.8 Sorting Lists5.9 Searching Sequences5.10 Other List Methods5.11 Simulating Stacks with Lists5.12 List Comprehensions5.13 Generator Expressions5.14 Filter, Map and Reduce5.15 Other Sequence Processing Functions5.16 Two-Dimensional Lists5.17 Intro to Data Science: Simulation and Static Visualizations5.18 Wrap-Up
6.1 Introduction6.2 Dictionaries6.3 Sets6.4 Intro to Data Science: Dynamic Visualizations6.5 Wrap-Up
7.1 Introduction7.2 Creating arrays from Existing Data7.3 array Attributes7.4 Filling arrays with Specific Values7.5 Creating arrays from Ranges7.6 List vs. array Performance: Introducing %timeit7.7 array Operators7.8 NumPy Calculation Methods7.9 Universal Functions7.10 Indexing and Slicing7.11 Views: Shallow Copies7.12 Deep Copies7.13 Reshaping and Transposing7.14 Intro to Data Science: pandas Series and DataFrames7.15 Wrap-Up
8.1 Introduction8.2 Formatting Strings8.3 Concatenating and Repeating Strings8.4 Stripping Whitespace from Strings8.5 Changing Character Case8.6 Comparison Operators for Strings8.7 Searching for Substrings8.8 Replacing Substrings8.9 Splitting and Joining Strings8.10 Characters and Character-Testing Methods8.11 Raw Strings8.12 Introduction to Regular Expressions8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging8.14 Wrap-Up
9.1 Introduction9.2 Files9.3 Text-File Processing9.4 Updating Text Files9.5 Serialization with JSON9.6 Focus on Security: pickle Serialization and Deserialization9.7 Additional Notes Regarding Files9.8 Handling Exceptions9.9 finally Clause9.10 Explicitly Raising an Exception9.11 (Optional) Stack Unwinding and Tracebacks9.12 Intro to Data Science: Working with CSV Files9.13 Wrap-Up
10.1 Introduction10.2 Custom Class Account10.3 Controlling Access to Attributes10.4 Properties for Data Access10.5 Simulating “Private” Attributes10.6 Case Study: Card Shuffling and Dealing Simulation10.7 Inheritance: Base Classes and Subclasses10.8 Building an Inheritance Hierarchy; Introducing Polymorphism10.9 Duck Typing and Polymorphism10.10 Operator Overloading10.11 Exception Class Hierarchy and Custom Exceptions10.12 Named Tuples10.13 A Brief Intro to Python 3.7’s New Data Classes10.14 Unit Testing with Docstrings and doctest10.15 Namespaces and Scopes10.16 Intro to Data Science: Time Series and Simple Linear Regression10.17 Wrap-Up
11.1 Introduction11.2 TextBlob111.3 Visualizing Word Frequencies with Bar Charts and Word Clouds11.4 Readability Assessment with Textatistic11.5 Named Entity Recognition with spaCy11.6 Similarity Detection with spaCy11.7 Other NLP Libraries and Tools11.8 Machine Learning and Deep Learning Natural Language Applications11.9 Natural Language Datasets11.10 Wrap-Up
12.1 Introduction12.2 Overview of the Twitter APIs12.3 Creating a Twitter Developer Account12.4 Getting Twitter Credentials—Creating an App12.5 What’s in a Twitter API Response?12.6 Installing Tweepy, geopy, folium and deep-translator12.7 Authenticating with Twitter Via Tweepy to Access Twitter v2 APIs12.8 Getting Information About a Twitter Account12.9 Intro to Tweepy Paginators: Getting More Than One Page of Results12.10 Searching Recent Tweets; Intro to Twitter v2 API Search Operators12.11 Spotting Trending Topics12.12 Cleaning/Preprocessing Tweets for Analysis12.13 Twitter Streaming API12.14 Tweet Sentiment Analysis12.15 Geocoding and Mapping12.16 Storing Tweets12.17 Twitter and Time Series12.18 Wrap-Up
13.1 Introduction: IBM Watson and Cognitive Computing13.2 IBM Cloud Account and Cloud Console13.3 Watson Services13.4 Additional Services and Tools13.5 Watson Developer Cloud Python SDK13.6 Case Study: Traveler’s Companion Translation App13.7 Watson Resources13.8 Wrap-Up
14.1 Introduction to Machine Learning14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 114.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 214.4 Case Study: Time Series and Simple Linear Regression14.5 Case Study: Multiple Linear Regression with the California Housing Dataset14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction14.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering14.8 Wrap-Up
15.1 Introduction15.2 Keras Built-In Datasets15.3 Custom Anaconda Environments15.4 Neural Networks15.5 Tensors15.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset15.7 Visualizing Neural Network Training with TensorBoard15.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization15.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset15.10 Tuning Deep Learning Models15.11 Convnet Models Pretrained on ImageNet15.12 Wrap-Up
16.1 Introduction16.2 Relational Databases and Structured Query Language (SQL)16.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour16.4 Case Study: A MongoDB JSON Document Database16.5 Hadoop16.6 Spark16.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack16.8 Internet of Things and Dashboards16.9 Wrap-Up

Overview

The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies

Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python—one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.

In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more.

Features

500+ hands-on, real-world, live-code examples from snippets to case studies
IPython + code in Jupyter® Notebooks
Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code
Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions
Procedural, functional-style and object-oriented programming
Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames
Static, dynamic and interactive visualizations
Data experiences with real-world datasets and data sources
Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression
AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® Watson™, machine learning, deep learning, computer vision, Hadoop®, Spark™, NoSQL, IoT
Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more

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