Python: Journey from Novice to Expert. Journey from Novice to Expert

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• Python: Journey from Novice to Expert
  • Table of Contents
  • Python: Journey from Novice to Expert
  • Python: Journey from Novice to Expert
  • Credits
  • Preface
    • What this learning path covers
    • What you need for this learning path
    • Who this learning path is for
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Errata
      • Piracy
      • Questions
  • 1. Module 1
    • 1. Introduction and First Steps Take a Deep Breath
      • A proper introduction
      • Enter the Python
      • About Python
        • Portability
        • Coherence
        • Developer productivity
        • An extensive library
        • Software quality
        • Software integration
        • Satisfaction and enjoyment
      • What are the drawbacks?
      • Who is using Python today?
      • Setting up the environment
        • Python 2 versus Python 3 the great debate
      • Installing Python
        • Setting up the Python interpreter
        • About virtualenv
        • Your first virtual environment
        • Your friend, the console
      • How you can run a Python program
        • Running Python scripts
        • Running the Python interactive shell
        • Running Python as a service
        • Running Python as a GUI application
      • How is Python code organized
        • How do we use modules and packages
      • Pythons execution model
        • Names and namespaces
        • Scopes
        • Object and classes
      • Guidelines on how to write good code
      • The Python culture
      • A note on the IDEs
      • Summary
    • 2. Built-in Data Types
      • Everything is an object
      • Mutable or immutable? That is the question
      • Numbers
        • Integers
        • Booleans
        • Reals
        • Complex numbers
        • Fractions and decimals
      • Immutable sequences
        • Strings and bytes
          • Encoding and decoding strings
          • Indexing and slicing strings
        • Tuples
      • Mutable sequences
        • Lists
        • Byte arrays
      • Set types
      • Mapping types dictionaries
      • The collections module
        • Named tuples
        • Defaultdict
        • ChainMap
      • Final considerations
        • Small values caching
        • How to choose data structures
        • About indexing and slicing
        • About the names
      • Summary
    • 3. Iterating and Making Decisions
      • Conditional programming
        • A specialized else: elif
        • The ternary operator
      • Looping
        • The for loop
          • Iterating over a range
          • Iterating over a sequence
        • Iterators and iterables
        • Iterating over multiple sequences
        • The while loop
        • The break and continue statements
        • A special else clause
      • Putting this all together
        • Example 1 a prime generator
        • Example 2 applying discounts
      • A quick peek at the itertools module
        • Infinite iterators
        • Iterators terminating on the shortest input sequence
        • Combinatoric generators
      • Summary
    • 4. Functions, the Building Blocks of Code
      • Why use functions?
        • Reduce code duplication
        • Splitting a complex task
        • Hide implementation details
        • Improve readability
        • Improve traceability
      • Scopes and name resolution
        • The global and nonlocal statements
      • Input parameters
        • Argument passing
        • Assignment to argument names don't affect the caller
        • Changing a mutable affects the caller
        • How to specify input parameters
          • Positional arguments
          • Keyword arguments and default values
          • Variable positional arguments
          • Variable keyword arguments
          • Keyword-only arguments
          • Combining input parameters
          • Avoid the trap! Mutable defaults
      • Return values
        • Returning multiple values
      • A few useful tips
      • Recursive functions
      • Anonymous functions
      • Function attributes
      • Built-in functions
      • One final example
      • Documenting your code
      • Importing objects
        • Relative imports
      • Summary
    • 5. Saving Time and Memory
      • map, zip, and filter
        • map
        • zip
        • filter
      • Comprehensions
        • Nested comprehensions
        • Filtering a comprehension
        • dict comprehensions
        • set comprehensions
      • Generators
        • Generator functions
        • Going beyond next
        • The yield from expression
        • Generator expressions
      • Some performance considerations
      • Don't overdo comprehensions and generators
      • Name localization
      • Generation behavior in built-ins
      • One last example
      • Summary
    • 6. Advanced Concepts OOP, Decorators, and Iterators
      • Decorators
        • A decorator factory
      • Object-oriented programming
        • The simplest Python class
        • Class and object namespaces
        • Attribute shadowing
        • I, me, and myself using the self variable
        • Initializing an instance
        • OOP is about code reuse
          • Inheritance and composition
        • Accessing a base class
        • Multiple inheritance
          • Method resolution order
        • Static and class methods
          • Static methods
          • Class methods
        • Private methods and name mangling
        • The property decorator
        • Operator overloading
        • Polymorphism a brief overview
      • Writing a custom iterator
      • Summary
    • 7. Testing, Profiling, and Dealing with Exceptions
      • Testing your application
        • The anatomy of a test
        • Testing guidelines
        • Unit testing
          • Writing a unit test
          • Mock objects and patching
          • Assertions
          • A classic unit test example
          • Making a test fail
          • Interface testing
          • Comparing tests with and without mocks
          • Boundaries and granularity
          • A more interesting example
      • Test-driven development
      • Exceptions
      • Profiling Python
        • When to profile?
      • Summary
    • 8. The Edges GUIs and Scripts
      • First approach scripting
        • The imports
        • Parsing arguments
        • The business logic
      • Second approach a GUI application
        • The imports
        • The layout logic
        • The business logic
          • Fetching the web page
          • Saving the images
          • Alerting the user
        • How to improve the application?
      • Where do we go from here?
        • The tkinter.tix module
        • The turtle module
        • wxPython, PyQt, and PyGTK
        • The principle of least astonishment
        • Threading considerations
      • Summary
    • 9. Data Science
      • IPython and Jupyter notebook
      • Dealing with data
        • Setting up the notebook
        • Preparing the data
        • Cleaning the data
        • Creating the DataFrame
          • Unpacking the campaign name
          • Unpacking the user data
          • Cleaning everything up
        • Saving the DataFrame to a file
        • Visualizing the results
      • Where do we go from here?
      • Summary
    • 10. Web Development Done Right
      • What is the Web?
      • How does the Web work?
      • The Django web framework
        • Django design philosophy
          • The model layer
          • The view layer
          • The template layer
        • The Django URL dispatcher
          • Regular expressions
      • A regex website
        • Setting up Django
          • Starting the project
          • Creating users
        • Adding the Entry model
        • Customizing the admin panel
        • Creating the form
        • Writing the views
          • The home view
          • The entry list view
          • The form view
        • Tying up URLs and views
        • Writing the templates
      • The future of web development
        • Writing a Flask view
        • Building a JSON quote server in Falcon
      • Summary
    • 11. Debugging and Troubleshooting
      • Debugging techniques
        • Debugging with print
        • Debugging with a custom function
        • Inspecting the traceback
        • Using the Python debugger
        • Inspecting log files
        • Other techniques
          • Profiling
          • Assertions
        • Where to find information
      • Troubleshooting guidelines
        • Using console editors
        • Where to inspect
        • Using tests to debug
        • Monitoring
      • Summary
    • 12. Summing Up A Complete Example
      • The challenge
      • Our implementation
      • Implementing the Django interface
        • The setup
        • The model layer
        • A simple form
        • The view layer
          • Imports and home view
          • Listing all records
          • Creating records
          • Updating records
          • Deleting records
        • Setting up the URLs
        • The template layer
          • Home and footer templates
          • Listing all records
          • Creating and editing records
          • Talking to the API
          • Deleting records
      • Implementing the Falcon API
        • The main application
        • Writing the helpers
          • Coding the password validator
          • Coding the password generator
        • Writing the handlers
          • Coding the password validator handler
          • Coding the password generator handler
        • Running the API
        • Testing the API
          • Testing the helpers
          • Testing the handlers
      • Where do you go from here?
      • Summary
  • 2. Module 2
    • 1. Object-oriented Design
      • Introducing object-oriented
      • Objects and classes
      • Specifying attributes and behaviors
        • Data describes objects
        • Behaviors are actions
      • Hiding details and creating the public interface
      • Composition
      • Inheritance
        • Inheritance provides abstraction
        • Multiple inheritance
      • Case study
      • Exercises
      • Summary
    • 2. Objects in Python
      • Creating Python classes
        • Adding attributes
        • Making it do something
          • Talking to yourself
          • More arguments
        • Initializing the object
        • Explaining yourself
      • Modules and packages
        • Organizing the modules
          • Absolute imports
          • Relative imports
      • Organizing module contents
      • Who can access my data?
      • Third-party libraries
      • Case study
      • Exercises
      • Summary
    • 3. When Objects Are Alike
      • Basic inheritance
        • Extending built-ins
        • Overriding and super
      • Multiple inheritance
        • The diamond problem
        • Different sets of arguments
      • Polymorphism
      • Abstract base classes
        • Using an abstract base class
        • Creating an abstract base class
        • Demystifying the magic
      • Case study
      • Exercises
      • Summary
    • 4. Expecting the Unexpected
      • Raising exceptions
        • Raising an exception
        • The effects of an exception
        • Handling exceptions
        • The exception hierarchy
        • Defining our own exceptions
      • Case study
      • Exercises
      • Summary
    • 5. When to Use Object-oriented Programming
      • Treat objects as objects
      • Adding behavior to class data with properties
        • Properties in detail
        • Decorators another way to create properties
        • Deciding when to use properties
      • Manager objects
        • Removing duplicate code
        • In practice
      • Case study
      • Exercises
      • Summary
    • 6. Python Data Structures
      • Empty objects
      • Tuples and named tuples
        • Named tuples
      • Dictionaries
        • Dictionary use cases
        • Using defaultdict
          • Counter
      • Lists
        • Sorting lists
      • Sets
      • Extending built-ins
      • Queues
        • FIFO queues
        • LIFO queues
        • Priority queues
      • Case study
      • Exercises
      • Summary
    • 7. Python Object-oriented Shortcuts
      • Python built-in functions
        • The len() function
        • Reversed
        • Enumerate
        • File I/O
        • Placing it in context
      • An alternative to method overloading
        • Default arguments
        • Variable argument lists
        • Unpacking arguments
      • Functions are objects too
        • Using functions as attributes
        • Callable objects
      • Case study
      • Exercises
      • Summary
    • 8. Strings and Serialization
      • Strings
        • String manipulation
        • String formatting
          • Escaping braces
          • Keyword arguments
          • Container lookups
          • Object lookups
          • Making it look right
        • Strings are Unicode
          • Converting bytes to text
          • Converting text to bytes
        • Mutable byte strings
      • Regular expressions
        • Matching patterns
          • Matching a selection of characters
          • Escaping characters
          • Matching multiple characters
          • Grouping patterns together
        • Getting information from regular expressions
          • Making repeated regular expressions efficient
      • Serializing objects
        • Customizing pickles
        • Serializing web objects
      • Case study
      • Exercises
      • Summary
    • 9. The Iterator Pattern
      • Design patterns in brief
      • Iterators
        • The iterator protocol
      • Comprehensions
        • List comprehensions
        • Set and dictionary comprehensions
        • Generator expressions
      • Generators
        • Yield items from another iterable
      • Coroutines
        • Back to log parsing
        • Closing coroutines and throwing exceptions
        • The relationship between coroutines, generators, and functions
      • Case study
      • Exercises
      • Summary
    • 10. Python Design Patterns I
      • The decorator pattern
        • A decorator example
        • Decorators in Python
      • The observer pattern
        • An observer example
      • The strategy pattern
        • A strategy example
        • Strategy in Python
      • The state pattern
        • A state example
        • State versus strategy
        • State transition as coroutines
      • The singleton pattern
        • Singleton implementation
      • The template pattern
        • A template example
      • Exercises
      • Summary
    • 11. Python Design Patterns II
      • The adapter pattern
      • The facade pattern
      • The flyweight pattern
      • The command pattern
      • The abstract factory pattern
      • The composite pattern
      • Exercises
      • Summary
    • 12. Testing Object-oriented Programs
      • Why test?
        • Test-driven development
      • Unit testing
        • Assertion methods
        • Reducing boilerplate and cleaning up
        • Organizing and running tests
        • Ignoring broken tests
      • Testing with py.test
        • One way to do setup and cleanup
        • A completely different way to set up variables
        • Skipping tests with py.test
      • Imitating expensive objects
      • How much testing is enough?
      • Case study
        • Implementing it
      • Exercises
      • Summary
    • 13. Concurrency
      • Threads
        • The many problems with threads
          • Shared memory
          • The global interpreter lock
        • Thread overhead
      • Multiprocessing
        • Multiprocessing pools
        • Queues
        • The problems with multiprocessing
      • Futures
      • AsyncIO
        • AsyncIO in action
        • Reading an AsyncIO future
        • AsyncIO for networking
        • Using executors to wrap blocking code
        • Streams
          • Executors
      • Case study
      • Exercises
      • Summary
  • 3. Module 3
    • 1. Getting Started One Environment per Project
      • Creating a virtual Python environment using venv
        • Creating your first venv
        • venv arguments
        • Differences between virtualenv and venv
      • Bootstrapping pip using ensurepip
        • ensurepip usage
        • Manual pip install
      • Installing C/C++ packages
        • Debian and Ubuntu
        • Red Hat, CentOS, and Fedora
        • OS X
        • Windows
      • Summary
    • 2. Pythonic Syntax, Common Pitfalls, and Style Guide
      • Code style or what is Pythonic code?
        • Formatting strings printf-style or str.format?
        • PEP20, the Zen of Python
          • Beautiful is better than ugly
          • Explicit is better than implicit
          • Simple is better than complex
          • Flat is better than nested
          • Sparse is better than dense
          • Readability counts
          • Practicality beats purity
          • Errors should never pass silently
          • In the face of ambiguity, refuse the temptation to guess
          • One obvious way to do it
          • Now is better than never
          • Hard to explain, easy to explain
          • Namespaces are one honking great idea
          • Conclusion
        • Explaining PEP8
          • Duck typing
          • Differences between value and identity comparisons
          • Loops
          • Maximum line length
        • Verifying code quality, pep8, pyflakes, and more
          • flake8
            • Pep8
            • pyflakes
            • McCabe
            • flake8
          • Pylint
      • Common pitfalls
        • Scope matters!
          • Function arguments
          • Class properties
          • Modifying variables in the global scope
        • Overwriting and/or creating extra built-ins
        • Modifying while iterating
        • Catching exceptions differences between Python 2 and 3
        • Late binding be careful with closures
        • Circular imports
        • Import collisions
      • Summary
    • 3. Containers and Collections Storing Data the Right Way
      • Time complexity the big O notation
      • Core collections
        • list a mutable list of items
        • dict unsorted but a fast map of items
        • set like a dict without values
        • tuple the immutable list
      • Advanced collections
        • ChainMap the list of dictionaries
        • counter keeping track of the most occurring elements
        • deque the double ended queue
        • defaultdict dictionary with a default value
        • namedtuple tuples with field names
        • enum a group of constants
        • OrderedDict a dictionary where the insertion order matters
        • heapq the ordered list
        • bisect the sorted list
      • Summary
    • 4. Functional Programming Readability Versus Brevity
      • Functional programming
      • list comprehensions
      • dict comprehensions
      • set comprehensions
      • lambda functions
        • The Y combinator
      • functools
        • partial no need to repeat all arguments every time
        • reduce combining pairs into a single result
          • Implementing a factorial function
          • Processing trees
      • itertools
        • accumulate reduce with intermediate results
        • chain combining multiple results
        • combinations combinatorics in Python
        • permutations combinations where the order matters
        • compress selecting items using a list of Booleans
        • dropwhile/takewhile selecting items using a function
        • count infinite range with decimal steps
        • groupby grouping your sorted iterable
        • islice slicing any iterable
      • Summary
    • 5. Decorators Enabling Code Reuse by Decorating
      • Decorating functions
        • Why functools.wraps is important
        • How are decorators useful?
        • Memoization using decorators
        • Decorators with (optional) arguments
        • Creating decorators using classes
      • Decorating class functions
        • Skipping the instance classmethod and staticmethod
        • Properties smart descriptor usage
      • Decorating classes
        • Singletons classes with a single instance
        • Total ordering sortable classes the easy way
      • Useful decorators
        • Single dispatch polymorphism in Python
        • Contextmanager, with statements made easy
        • Validation, type checks, and conversions
        • Useless warnings how to ignore them
      • Summary
    • 6. Generators and Coroutines Infinity, One Step at a Time
      • What are generators?
        • Advantages and disadvantages of generators
        • Pipelines an effective use of generators
        • tee using an output multiple times
        • Generating from generators
        • Context managers
      • Coroutines
        • A basic example
        • Priming
        • Closing and throwing exceptions
        • Bidirectional pipelines
        • Using the state
      • Summary
    • 7. Async IO Multithreading without Threads
      • Introducing the asyncio library
        • The async and await statements
          • Python 3.4
          • Python 3.5
          • Choosing between the 3.4 and 3.5 syntax
        • A simple example of single-threaded parallel processing
        • Concepts of asyncio
          • Futures and tasks
          • Event loops
            • Event loop implementations
            • Event loop policies
            • Event loop usage
          • Processes
        • Asynchronous servers and clients
          • Basic echo server
      • Summary
    • 8. Metaclasses Making Classes (Not Instances) Smarter
      • Dynamically creating classes
        • A basic metaclass
        • Arguments to metaclasses
        • Accessing metaclass attributes through classes
      • Abstract classes using collections.abc
        • Internal workings of the abstract classes
        • Custom type checks
        • Using abc.ABC before Python 3.4
      • Automatically registering a plugin system
        • Importing plugins on-demand
        • Importing plugins through configuration
        • Importing plugins through the file system
      • Order of operations when instantiating classes
        • Finding the metaclass
        • Preparing the namespace
        • Executing the class body
        • Creating the class object (not instance)
        • Executing the class decorators
        • Creating the class instance
        • Example
      • Storing class attributes in definition order
        • The classic solution without metaclasses
        • Using metaclasses to get a sorted namespace
      • Summary
    • 9. Documentation How to Use Sphinx and reStructuredText
      • The reStructuredText syntax
        • Getting started with reStructuredText
        • Inline markup
        • Headers
        • Lists
          • Enumerated list
          • Bulleted list
          • Option list
          • Definition list
          • Nested lists
        • Links, references, and labels
        • Images
        • Substitutions
        • Blocks, code, math, comments, and quotes
        • Conclusion
      • The Sphinx documentation generator
        • Getting started with Sphinx
          • Using sphinx-quickstart
          • Using sphinx-apidoc
        • Sphinx directives
          • The table of contents tree directive (toctree)
          • Autodoc, documenting Python modules, classes, and functions
        • Sphinx roles
      • Documenting code
        • Documenting a class with the Sphinx style
        • Documenting a class with the Google style
        • Documenting a class with the NumPy style
        • Which style to choose
      • Summary
    • 10. Testing and Logging Preparing for Bugs
      • Using examples as tests with doctest
        • A simple doctest example
        • Writing doctests
        • Testing with pure documentation
        • The doctest flags
          • True and False versus 1 and 0
          • Normalizing whitespace
          • Ellipsis
        • Doctest quirks
          • Testing dictionaries
          • Testing floating-point numbers
          • Times and durations
      • Testing with py.test
        • The difference between the unittest and py.test output
        • The difference between unittest and py.test tests
          • Simplifying assertions
          • Parameterizing tests
          • Automatic arguments using fixtures
            • Cache
            • Custom fixtures
          • Print statements and logging
          • Plugins
            • pytest-cov
            • pytest-pep8 and pytest-flakes
            • Configuring plugins
      • Mock objects
        • Using unittest.mock
        • Using py.test monkeypatch
      • Logging
        • Configuration
          • Basic logging configuration
          • Dictionary configuration
          • JSON configuration
          • Ini file configuration
          • The network configuration
        • Logger
          • Usage
      • Summary
    • 11. Debugging Solving the Bugs
      • Non-interactive debugging
        • Inspecting your script using trace
        • Debugging using logging
        • Showing call stack without exceptions
        • Debugging asyncio
        • Handling crashes using faulthandler
      • Interactive debugging
        • Console on demand
        • Debugging using pdb
          • Breakpoints
          • Catching exceptions
          • Commands
        • Debugging using ipdb
        • Other debuggers
        • Debugging services
      • Summary
    • 12. Performance Tracking and Reducing Your Memory and CPU Usage
      • What is performance?
      • Timeit comparing code snippet performance
      • cProfile finding the slowest components
        • First profiling run
        • Calibrating your profiler
        • Selective profiling using decorators
        • Using profile statistics
      • Line profiler
      • Improving performance
        • Using the right algorithm
        • Global interpreter lock
        • Try versus if
        • Lists versus generators
        • String concatenation
        • Addition versus generators
        • Map versus generators and list comprehensions
        • Caching
        • Lazy imports
        • Using optimized libraries
        • Just-in-time compiling
        • Converting parts of your code to C
      • Memory usage
        • Tracemalloc
        • Memory profiler
        • Memory leaks
        • Reducing memory usage
          • Generators versus lists
          • Recreating collections versus removing items
          • Using slots
      • Performance monitoring
      • Summary
    • 13. Multiprocessing When a Single CPU Core Is Not Enough
      • Multithreading versus multiprocessing
      • Hyper-threading versus physical CPU cores
      • Creating a pool of workers
      • Sharing data between processes
      • Remote processes
        • Distributed processing using multiprocessing
        • Distributed processing using IPyparallel
          • ipython_config.py
          • ipython_kernel_config.py
          • ipcontroller_config.py
          • ipengine_config.py
          • ipcluster_config.py
      • Summary
    • 14. Extensions in C/C++, System Calls, and C/C++ Libraries
      • Introduction
        • Do you need C/C++ modules?
        • Windows
        • OS X
        • Linux/Unix
      • Calling C/C++ with ctypes
        • Platform-specific libraries
          • Windows
          • Linux/Unix
          • OS X
          • Making it easy
        • Calling functions and native types
        • Complex data structures
        • Arrays
        • Gotchas with memory management
      • CFFI
        • Complex data structures
        • Arrays
        • ABI or API?
        • CFFI or ctypes?
      • Native C/C++ extensions
        • A basic example
        • C is not Python size matters
        • The example explained
          • static
          • PyObject*
          • Parsing arguments
        • C is not Python errors are silent or lethal
        • Calling Python from C handling complex types
      • Summary
    • 15. Packaging Creating Your Own Libraries or Applications
      • Installing packages
      • Setup parameters
      • Packages
      • Entry points
        • Creating global commands
        • Custom setup.py commands
      • Package data
      • Testing packages
        • Unittest
        • py.test
        • Nosetests
      • C/C++ extensions
        • Regular extensions
        • Cython extensions
      • Wheels the new eggs
        • Distributing to the Python Package Index
      • Summary
  • A. Bibliography
  • Index

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