Python 3 and data analytics : pocket primer

This book is intended primarily for developers who have little or no experience with Python or Pandas. It contains a fast-paced introduction to Python and Python-based solutions to various tasks. Chapter 1 provides a quick tour of basic Python 3, followed by a chapter that shows how to work with loops and conditional logic in Python. Chapter 3 discusses data structures in Python, followed by a chapter that features code samples for tasks with strings and arrays in Python. Chapter 5 contains concepts in object-oriented programming, along with code samples that illustrate how they are implemented in Python. Chapter 6 introduces recursion and some fundamental topics in combinatorics. Finally, the appendix provides an introduction to Pandas. Companion files with code and figures are available for downloading from the publisher. Features Provides the reader with basic Python 3 and Pandas programming concepts Companion files with code and figures Table of Contents 1: Introduction to Python. 2: Conditional Logic in Python. 3: Data Structures in Python. 4: Strings and Arrays. 5: Built-In Functions and Custom Classes. 6: Recursion and Combinatorics. Appendix: Introduction to Pandas. Index. About the Author Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, and NLP. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer , Python 3 for Machine Learning , and the NLP Using R Pocket Primer (all Mercury Learning). Cover Half-Title Title Copyright Dedication Contents Preface Chapter 1: Introduction to Python Tools for Python Python Installation Setting the PATH Environment Variable (Windows Only) Launching Python on Your Machine Python Identifiers Lines, Indentation, and Multilines Quotation and Comments in Python Saving Your Code in a Module Some Standard Modules in Python The help() and dir() Functions Compile Time and Runtime Code Checking Simple Data Types in Python Working With Numbers Working With Fractions Unicode and UTF-8 Working With Unicode Working With Strings Uninitialized Variables and the Value None in Python Slicing and Splicing Strings Search and Replace a String in Other Strings Remove Leading and Trailing Characters Printing Text Without NewLine Characters Text Alignment Working With Dates Exception Handling in Python Handling User Input Command-Line Arguments Summary Chapter 2: Conditional Logic in Python Precedence of Operators in Python Python Reserved Words Working With Loops in Python Nested Loops The split() Function With for Loops Using the split() Function to Compare Words Using the split() Function to Print Justified Text Using the split() Function to Print Fixed Width Text Using the split() Function to Compare Text Strings Using the split() Function to Display Characters in a String The join() Function Python while Loops Conditional Logic in Python The break/continue/pass Statements Comparison and Boolean Operators Local and Global Variables Scope of Variables Pass by Reference versus Value Arguments and Parameters Using a while Loop to Find the Divisors of a Number User-Defined Functions in Python Specifying Default Values in a Function Functions With a Variable Number of Arguments Summary Chapter 3: Data Structures in Python Working With Lists Sorting Lists of Numbers and Strings Concatenating a List of Words The Python range() Function Lists and the append() Function Working With Lists and the split() Function Counting Words in a List Iterating Through Pairs of Lists List Slices Other List-Related Functions Working With Vectors Working With Matrices Queues Tuples (Immutable Lists) Sets Dictionaries Dictionary Functions and Methods Ordered Dictionaries Other Sequence Types in Python Mutable and Immutable Types in Python Packing/Unpacking Sequences Lambda Expressions Functional Programming in Python: The map() Function Functional Programming in Python: The filter() Function Summary Chapter 4: Strings and Arrays Time and Space Complexity Task: Maximum and Minimum Powers of an Integer Task: Binary Substrings of a Number Task: Common Substring of Two Binary Numbers Task: Multiply and Divide via Recursion Task: Sum of Prime and Composite Numbers Task: Count Word Frequencies Task: Check if a String Contains Unique Characters Task: Insert Characters in a String Task: String Permutations Task: Find All Subsets of a Set Task: Check for Palindromes Task: Check for Longest Palindrome Working With Sequences of Strings Task: Longest Sequences of Substrings Working With 1D Arrays Task: Invert Adjacent Array Elements Working With 2D Arrays The Transpose of a Matrix Search Algorithms Well-Known Sorting Algorithms Merge Sort Summary Chapter 5: Built-In Functions and Custom Classes A Python Module versus Package Python Functions versus Methods Functionally Oriented Programming in Python Importing Custom Python Modules How to Create Custom Classes Construction and Initialization of Objects Compiled Modules Classes, Functions, and Methods in Python Accessors and Mutators versus @property Creating an Employee Custom Class Working With a List of Employees Working With Linked Lists in Python Custom Classes and Linked Lists Custom Classes and Dictionaries Custom Classes and Priority Queues Overloading Operators Serialize and Deserialize Data Encapsulation Single Inheritance A Concrete Example of Inheritance Inheritance and Overriding Methods Multiple Inheritance Polymorphism The Python abc Module Summary Chapter 6: Recursion and Combinatorics What Is Recursion? Arithmetic Series Geometric Series Factorial Values Fibonacci Numbers Task: Reverse an Array of Strings via Recursion Task: Check for Balanced Parentheses Task: Calculate the Number of Digits Task: Determine if a Positive Integer is Prime Task: Find the Prime Factorization of a Positive Integer Task: Goldbach’s Conjecture Task: Calculate the GCD (Greatest Common Divisor) Task: Calculate the LCM (Lowest Common Multiple) What Is Combinatorics? Task: Calculate the Sum of Binomial Coefficients The Number of Subsets of a Finite Set Summary Appendix: Introduction to Pandas Index As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, and data cleaning. The book includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regular expressions. Companion files with source code and color figures are available online by emailing the publisher with proof of purchase at info@merclearning.com.FEATURES:* Includes a concise introduction to Python 3 * Provides a thorough introduction to data and data cleaning * Covers NumPy and Pandas * Introduces statistical concepts and data visualization (Matplotlib/Seaborn) * Features an appendix on regular expressions * Includes companion files with source code and figures "As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, and data cleaning. The book includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regular expressions. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 Provides a thorough introduction to data and data cleaning. Covers NumPy and Pandas Introduces statistical concepts and data visualization (Matplotlib/Seaborn). Features an appendix on regular expressions. Includes companion files with source code and figures."--Publisher's website