Chapter 1. Introduction to Python

• What is Python
• Uses of Python
• Installing Python
• Python Package Manager (PIP)
• Using the Python Shell
• Python Code Conventions
• Importing Modules
• The Help(object) Command
• The Help Prompt
• Summary

Chapter 2. Python Scripts

• Executing Python Code
• Python Scripts
• Writing Scripts
• Running Python Scripts
• Self Executing Scripts
• Accepting Command-Line Parameters
• Accepting Interactive Input
• Retrieving Environment Settings
• Summary

Chapter 3. Data Types and Variables

• Creating Variables
• Displaying Variables
• Basic Concatenation
• Data Types
• Strings
• Strings as Arrays
• String Methods
• Combining Strings and Numbers
• Numeric Types
• Integer Types
• Floating Point Types
• Boolean Types
• Checking Data Type
• Summary

Chapter 4. Python Collections

• Python Collections
• List Type
• Modifying Lists
• Sorting a List
• Tuple Type
• Python Sets
• Modifying Sets
• Dictionary (Map) Type
• Dictionary Methods
• Sequences
• Summary

Chapter 5. Control Statements and Looping

• If Statement
• elif Keyword
• Boolean Conditions
• Single Line If Statements
• For-in Loops
• Looping over an Index
• Range Function
• Nested Loops
• While Loops
• Exception Handling
• Built-in Exceptions
• Exceptions thrown by Built-In Functions
• Summary

Chapter 6. Functions in Python

• Defining Functions
• Naming Functions
• Using Functions
• Function Parameters
• Named Parameters
• Variable Length Parameter List
• How Parameters are Passed
• Variable Scope
• Returning Values
• Docstrings
• Best Practices
• Single Responsibility
• Returning a Value
• Function Length
• Pure and Idempotent Functions
• Summary

Chapter 7. Working With Data in Python

• Data Type Conversions
• Conversions from other Types to Integer
• Conversions from other Types to Float
• Conversions from other Types to String
• Conversions from other Types to Boolean
• Converting Between Set, List and Tuple Data Structures
• Modifying Tuples
• Combining Set, List and Tuple Data Structures
• Creating Dictionaries from other Data Structures
• Summary

Chapter 8. Reading and Writing Text Files

• Opening a File
• Writing a File
• Reading a File
• Appending to a File
• File Operations Using the
• With
• Statement
• File and Directory Operations
• Reading JSON
• Writing JSON
• Summary

Chapter 9. Functional Programming Primer

• What is Functional Programming?
• Benefits of Functional Programming
• Functions as Data
• Using Map Function
• Using Filter Function
• Lambda expressions
• List.sort() Using Lambda Expression
• Difference Between Simple Loops and map/filter Type Functions
• Additional Functions
• General Rules for Creating Functions
• Summary

Chapter 10. Introduction to Apache Spark

• What is Apache Spark
• A Short History of Spark
• Where to Get Spark?
• The Spark Platform
• Spark Logo
• Common Spark Use Cases
• Languages Supported by Spark
• Running Spark on a Cluster
• The Driver Process
• Spark Applications
• Spark Shell
• The spark-submit Tool
• The spark-submit Tool Configuration
• The Executor and Worker Processes
• The Spark Application Architecture
• Interfaces with Data Storage Systems
• Limitations of Hadoop's MapReduce
• Spark vs MapReduce
• Spark as an Alternative to Apache Tez
• The Resilient Distributed Dataset (RDD)
• Datasets and DataFrames
• Spark Streaming (Micro-batching)
• Spark SQL
• Example of Spark SQL
• Spark Machine Learning Library
• GraphX
• Spark vs R
• Summary

Chapter 11. The Spark Shell

• The Spark Shell
• The Spark v.2 + Command-Line Shells
• The Spark Shell UI
• Spark Shell Options
• Getting Help
• Jupyter Notebook Shell Environment
• Example of a Jupyter Notebook Web UI (Databricks Cloud)
• The Spark Context (sc) and Spark Session (spark)
• Creating a Spark Session Object in Spark Applications
• The Shell Spark Context Object (sc)
• The Shell Spark Session Object (spark)
• Loading Files
• Saving Files
• Summary

Chapter 12. Spark RDDs

• The Resilient Distributed Dataset (RDD)
• Ways to Create an RDD
• Supported Data Types
• RDD Operations
• RDDs are Immutable
• Spark Actions
• RDD Transformations
• Other RDD Operations
• Chaining RDD Operations
• RDD Lineage
• The Big Picture
• What May Go Wrong
• Checkpointing RDDs
• Local Checkpointing
• Parallelized Collections
• More on parallelize() Method
• The Pair RDD
• Where do I use Pair RDDs?
• Example of Creating a Pair RDD with Map
• Example of Creating a Pair RDD with keyBy
• Miscellaneous Pair RDD Operations
• RDD Caching
• RDD Persistence
• Summary

Chapter 13. Parallel Data Processing with Spark

• Running Spark on a Cluster
• Data Partitioning
• Data Partitioning Diagram
• Single Local File System RDD Partitioning
• Multiple File RDD Partitioning
• Special Cases for Small-sized Files
• Parallel Data Processing of Partitions
• Spark Application, Jobs, and Tasks
• Stages and Shuffles
• The "Big Picture"
• Summary

Chapter 14. Shared Variables in Spark

• Shared Variables in Spark
• Broadcast Variables
• Creating and Using Broadcast Variables
• Example of Using Broadcast Variables
• Problems with Global Variables
• Example of the Closure Problem
• Accumulators
• Creating and Using Accumulators
• Example of Using Accumulators (Scala Example)
• Example of Using Accumulators (Python Example)
• Custom Accumulators
• Summary

Chapter 15. Introduction to Spark SQL

• What is Spark SQL?
• Uniform Data Access with Spark SQL
• Hive Integration
• Hive Interface
• Integration with BI Tools
• What is a DataFrame?
• Creating a DataFrame in PySpark
• Commonly Used DataFrame Methods and Properties in PySpark
• Grouping and Aggregation in PySpark
• The "DataFrame to RDD" Bridge in PySpark
• The SQLContext Object
• Examples of Spark SQL / DataFrame (PySpark Example)
• Converting an RDD to a DataFrame Example
• Example of Reading / Writing a JSON File
• Using JDBC Sources
• JDBC Connection Example
• Performance, Scalability, and Fault-tolerance of Spark SQL
• Summary

Chapter 16. Repairing and Normalizing Data

• Repairing and Normalizing Data
• Dealing with the Missing Data
• Sample Data Set
• Getting Info on Null Data
• Dropping a Column
• Interpolating Missing Data in pandas
• Replacing the Missing Values with the Mean Value
• Scaling (Normalizing) the Data
• Data Preprocessing with scikit-learn
• Scaling with the scale() Function
• The MinMaxScaler Object
• Summary

Chapter 17. Data Grouping and Aggregation in Python

• Data Aggregation and Grouping
• Sample Data Set
• The pandas.core.groupby.SeriesGroupBy Object
• Grouping by Two or More Columns
• Emulating SQL's WHERE Clause
• The Pivot Tables
• Cross-Tabulation
• Summary