Python 3: from None to Machine Learning

author	Matt Harasymczuk
email	matt@astrotech.io
www	http://www.astrotech.io
github	https://github.com/astromatt
linkedin	https://linkedin.com/in/mattharasymczuk
facebook	https://facebook.com/matt.harasymczuk
slideshare	https://www.slideshare.net/astrotech/presentations

Tip

This book is free and open source at http://python.astrotech.io

For consulting or training course requests please email matt@astrotech.io

You might also be interested in other book (http://dev.astrotech.io) on Software Engineering covering topics of:

• DevOps and CI/CD with Docker

• Jira and Atlassian Ecosystem

• Git and Git Flow in CI/CD

• Agile Software Engineering: CI/CD with Scrum, Kanban, XP and Lean Startup

• Software Architecture with Cloud and Microservices

• Linux and Shell Programming with Bash

Warning

Before training course please setup your environment as described here Installing Python.

Copyright

MIT License

Copyright (c) 2019 Matt Harasymczuk

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

About

• Copyright
• References in the Book

Python Basics

Python Basics

• 1. About
  • 1.1. Survey
  • 1.2. Agenda
  • 1.3. Installing Python
  • 1.4. Python Language
  • 1.5. Development Environment
• 2. Syntax
  • 2.1. Python Syntax
  • 2.2. Operators
  • 2.3. Builtin Functions
• 3. Types
  • 3.1. Type int
  • 3.2. Type float
  • 3.3. Type bool
  • 3.4. Type None
  • 3.5. Type str
  • 3.6. Type str Methods
  • 3.7. Basic Type Annotations
• 4. Sequences
  • 4.1. Sequence list
  • 4.2. Sequence tuple
  • 4.3. Sequence set
  • 4.4. Sequence Nesting
  • 4.5. Sequence Indexing
  • 4.6. Sequence Slicing
  • 4.7. Sequence Unpacking
• 5. Mappings
  • 5.1. Mapping dict
  • 5.2. Mapping Generate
  • 5.3. Mapping Nested
• 6. Conditionals
  • 6.1. Operators
  • 6.2. Conditional Statements
  • 6.3. Conditional Expressions
• 7. Looping
  • 7.1. Loop while
  • 7.2. Loop for
  • 7.3. Loop Nested Sequences
  • 7.4. Loop Unpacking Sequences
  • 7.5. Loop over dict
  • 7.6. Comprehensions
• 8. Control Flow
  • 8.1. Exceptions
  • 8.2. Files
• 9. Functions
  • 9.1. Function Definition
  • 9.2. Function Return
  • 9.3. Function Arguments
  • 9.4. Function Variables Scope
  • 9.5. Function Type Annotation
  • 9.6. Function Recurrence
  • 9.7. Function Doctest
• 10. Object Oriented Programming
  • 10.1. Classes and Instances
  • 10.2. Attributes
  • 10.3. Initializer Method
  • 10.4. Methods
  • 10.5. Stringify Objects
  • 10.6. Inheritance
• 11. Recap
  • 11.1. Exit Exam
  • 11.2. Summary
  • 11.3. Certificate
• 12. References
  • 12.1. Bibliography

Python Intermediate

Python Intermediate

• 1. Entry Test
  • 1.1. Pre-Intermediate
• 2. Type Annotations
  • 2.1. Type Annotation
  • 2.2. Introspection
  • 2.3. Type Checking
  • 2.4. Annotating existing code
• 3. Builtins
  • 3.1. Builtin Functions
  • 3.2. Builtin Keywords
  • 3.3. Builtin Printing
• 4. Functions
  • 4.1. Defining function with arbitrary number of arguments
  • 4.2. Passing many arguments
• 5. Looping
  • 5.1. Comprehensions Nesting
  • 5.2. Loop else
• 6. Object Oriented Programming
  • 6.1. Access Modifiers
  • 6.2. OOP Enum
  • 6.3. Operator Overload
  • 6.4. OOP Dataclass
• 7. Modules and Packages
  • 7.1. Module venv
  • 7.2. Module Install
  • 7.3. Module Import
  • 7.4. Tools
  • 7.5. Standalone Applications
  • 7.6. Distributing Packages
• 8. Datetime and Timezones
  • 8.1. Datetime Dates and Time
  • 8.2. Datetime Parsing and Formatting
  • 8.3. Datetime Time Deltas
  • 8.4. Datetime Timestamp
  • 8.5. Datetime Time Utils
  • 8.6. Datetime Timezones
• 9. Serialization
  • 9.1. Serialization CSV
  • 9.2. Serialization JSON
  • 9.3. Serialization Pickle
  • 9.4. Serialization XML, XSLT, XPath
• 10. Database
  • 10.1. Database API
  • 10.2. Database SQLite
  • 10.3. Database MySQL
  • 10.4. Database PostgreSQL
  • 10.5. Database MongoDB
  • 10.6. Database ORM
  • 10.7. Database Schema Migration
  • 10.8. Database Exploration
  • 10.9. Database SQL Injection
  • 10.10. Database SQL
• 11. Regular Expressions
  • 11.1. Regexp Syntax
  • 11.2. Regexp Match
  • 11.3. Regexp Search
  • 11.4. Regexp Findall, Finditer
  • 11.5. Regexp Compare
  • 11.6. Regexp Compile
  • 11.7. Regexp Groups
  • 11.8. Regexp Multiline
  • 11.9. Regexp Substitute
  • 11.10. Regexp Split
  • 11.11. Regexp Non-Greedy
  • 11.12. Regexp Standards
  • 11.13. References
• 12. Mathematics
  • 12.1. Math Stdlib
  • 12.2. Math Statistics
  • 12.3. Math Random
  • 12.4. Math Fractions
  • 12.5. Math Precision
  • 12.6. Math Plotting
  • 12.7. Math Complex Numbers
• 13. Operating System
  • 13.1. Commandline argument parsing
  • 13.2. Operating System
  • 13.3. pathlib
• 14. GUI
  • 14.1. GUI
• 15. Exit Test
  • 15.1. Stdlib Exam

Python Advanced

Python Advanced

• 1. Entry Test
  • 1.1. Entry Exam
  • 1.2. Dragon
• 2. Performance Optimization
  • 2.1. Performance Optimization
  • 2.2. Micro-benchmarking
  • 2.3. Profiling
  • 2.4. Compilers and interpreters
  • 2.5. Collections
  • 2.6. C Types
  • 2.7. C Modules
• 3. Functions
  • 3.1. Generators
  • 3.2. Callable
  • 3.3. Closures
  • 3.4. Decorator
• 4. OOP Paradigm
  • 4.1. Static and Dynamic Fields
  • 4.2. Inheritance vs. Composition
  • 4.3. Method Resolution Order
  • 4.4. Relations
  • 4.5. Polymorphism
  • 4.6. Dynamic Typing
  • 4.7. New and Old style classes
  • 4.8. Interface
  • 4.9. Abstract classes
  • 4.10. Good Engineering Practises
  • 4.11. Exam
• 5. OOP Protocols
  • 5.1. Iterators
  • 5.2. Context Managers
  • 5.3. staticmethod
  • 5.4. classmethod
  • 5.5. Properties
  • 5.6. Reflection
  • 5.7. Descriptor
• 6. Object Oriented Programming
  • 6.1. Object initialization
  • 6.2. Placeholder class
  • 6.3. Object Identity
  • 6.4. Slots
  • 6.5. Metaclass
  • 6.6. OOP Advanced Exam
• 7. Concurrency
  • 7.1. Queues
  • 7.2. Threads
  • 7.3. Multiprocessing
  • 7.4. Concurrency
• 8. Exit Test
  • 8.1. Exit Exam

Design Patterns

Design Patterns

• 1. Entry Test
  • 1.1. Entry Exam
  • 1.2. Dragon
  • 1.3. Classification
• 2. Creational
  • 2.1. Singleton
  • 2.2. Factory
• 3. Structural
  • 3.1. Dependency injection
• 4. Behavioral
  • 4.1. Gateway
  • 4.2. State Machine
• 5. Paradigms
  • 5.1. Callback
  • 5.2. Functional Programming
  • 5.3. Event Programming
• 6. Exit Test
  • 6.1. Exit Exam

Quality and CI/CD

Quality and CI/CD

• 1. Software Engineering Conventions
  • 1.1. PEP 8 - Style Guide for Python Code
  • 1.2. Static Code Analysis
  • 1.3. Assignment
• 2. The Zen of Python
  • 2.1. Most important rules
  • 2.2. Full Text
• 3. Code Smells
  • 3.1. Bad practice
• 4. ReST and Sphinx documentation
  • 4.1. reStructuredText vs. Markdown
  • 4.2. Format reStructuredText
  • 4.3. Sphinx
  • 4.4. Read the docs
  • 4.5. Assignments
• 5. Unit Testing
  • 5.1. Glossary
  • 5.2. Running tests
  • 5.3. Example
  • 5.4. Mock
  • 5.5. Stub
  • 5.6. Assignments
• 6. Pytest
  • 6.1. Why?
  • 6.2. Running
  • 6.3. Skip
  • 6.4. pytest.raises
  • 6.5. Fixtures
• 7. Python WAT?!
• 8. Logging
  • 8.1. Korzystanie z logging
  • 8.2. Konfiguracja formatowania logów
  • 8.3. Rotate
  • 8.4. Further Reading
• 9. Warnings
  • 9.1. Usage
  • 9.2. Running
  • 9.3. Example
• 10. Basic Debugging
  • 10.1. Run in the console
  • 10.2. print
  • 10.3. pprint
  • 10.4. locals()
  • 10.5. Using debugger in IDE
• 11. Advanced Debugging
  • 11.1. json.tool
  • 11.2. Using pdb
  • 11.3. breakpoint()
  • 11.4. code.interact()
  • 11.5. Using debugger in IDE
  • 11.6. Debugging i Wątki
  • 11.7. Debugging i Procesy
  • 11.8. Debugging aplikacji sieciowych
  • 11.9. Assignment
• 12. Introspection
  • 12.1. Introspecting Types
  • 12.2. Introspecting Objects
  • 12.3. Introspecting Docstrings
  • 12.4. Example
• 13. Type Annotation
  • 13.1. What are Type Annotations?
  • 13.2. Data types
  • 13.3. Sequences
  • 13.4. Nested sequences
  • 13.5. Union
  • 13.6. Any
  • 13.7. Optional
  • 13.8. The NoReturn type
  • 13.9. Type aliases
  • 13.10. Iterator
  • 13.11. Overload
  • 13.12. Final
  • 13.13. Literal
  • 13.14. TypedDict
  • 13.15. TypeVar, Iterable, Tuple
  • 13.16. Callable
• 14. Introspection
• 15. Type Checking
  • 15.1. MyPy
  • 15.2. PyType
  • 15.3. pyre-check
• 16. Annotating existing code
  • 16.1. PyAnnotate
  • 16.2. monkeytype
• 17. CI/CD
  • 17.1. CI/CD Tools
  • 17.2. CI/CD Pipelines
  • 17.3. Linters
  • 17.4. Behavioral Testing
  • 17.5. Code Style
  • 17.6. Code Coverage
  • 17.7. Internationalization
  • 17.8. DB Schema Migration
  • 17.9. Distributing
  • 17.10. Security
  • 17.11. Smoke Tests
  • 17.12. Static Code Analysis
  • 17.13. Test Automation
  • 17.14. Type Checking
  • 17.15. UI testing
  • 17.16. Mutation Testing

Network and HTTP

Network and HTTP

• 1. Protocols
  • 1.1. FTP
  • 1.2. IMAP
  • 1.3. SMTP
  • 1.4. POP3
  • 1.5. LDAP
  • 1.6. SSH
  • 1.7. DNS
• 2. Transport
  • 2.1. OSI Model
  • 2.2. Interfaces
  • 2.3. Routing
  • 2.4. Socket
  • 2.5. SSL
  • 2.6. IPv4/IPv6
  • 2.7. TCP/UDP
  • 2.8. scapy
  • 2.9. 3rd Party Libraries
  • 2.10. Mini Botnet
• 3. HTTP and Web
  • 3.1. HTTP Protocol
  • 3.2. HTTP using stdlib
  • 3.3. HTTP using requests
  • 3.4. HTML Scrapping
  • 3.5. HTTP Frameworks
  • 3.6. Templates with Jinja2
  • 3.7. PaaS - Platform as a Service
  • 3.8. SOAP and WSDL
  • 3.9. Asynchronous processing

Django

Django

• 1. HTTP
  • 1.1. Narzędzia
  • 1.2. DNS
  • 1.3. HTTP Protocol
  • 1.4. HTTP Methods
  • 1.5. HTML + JS + CSS
• 2. Introduction to Django
  • 2.1. Co to jest Django?
  • 2.2. Dokumentacja
  • 2.3. Wersjonowanie
  • 2.4. setup.cfg
  • 2.5. IDE Support
• 3. Django Framework Architecture
  • 3.1. Framework Architecture
  • 3.2. Request-Response flow
• 4. Installation and Running
  • 4.1. Instalacja
  • 4.2. Starting new project
  • 4.3. IDE Run configuration
• 5. Settings
• 6. Apps
  • 6.1. Reusability
  • 6.2. Location
  • 6.3. Create
  • 6.4. Structure
  • 6.5. Configuration
• 7. Models
  • 7.1. Model Fields
  • 7.2. Model field arguments
  • 7.3. Abstract Models
  • 7.4. Architecture
  • 7.5. Single File vs. Models per file
  • 7.6. Reverse engineering database
  • 7.7. Database schema migration
  • 7.8. Example Model
  • 7.9. Assignments
• 8. URL Router
  • 8.1. Global URLs
  • 8.2. In app URLs
  • 8.3. urlpatterns
  • 8.4. path
  • 8.5. url
  • 8.6. include
  • 8.7. URLs reversing
  • 8.8. URLs in templates
• 9. ORM
  • 9.1. QuerySet
  • 9.2. Field lookups
  • 9.3. Filters can reference fields on the model
  • 9.4. Complex lookups with Q objects
  • 9.5. Comparing objects
  • 9.6. Q() expressions
  • 9.7. F() expressions
  • 9.8. Aggregations
• 10. Views
  • 10.1. Class Based Views
  • 10.2. Generic Views
  • 10.3. Responses
  • 10.4. Permissions in Views
• 11. Forms
  • 11.1. Forms
  • 11.2. Model Forms
  • 11.3. Widgets
• 12. REST
  • 12.1. RESTful Views
  • 12.2. python -m json.tool
  • 12.3. django-rest-framework
  • 12.4. django-rest-swagger
• 13. Staticfiles
  • 13.1. Settings
  • 13.2. Serving staticfiles
• 14. Templates
• 15. Templatetags
  • 15.1. Filters
  • 15.2. Simple Tags
  • 15.3. Assignment tags
  • 15.4. Block tags
• 16. Admin panel
  • 16.1. Customizong
  • 16.2. Permissions
  • 16.3. Model Admin
  • 16.4. Model Inlines
  • 16.5. Extending Admin
  • 16.6. ModelAdmin Example
  • 16.7. django-import-export
  • 16.8. Grapelli
• 17. Management Commands
  • 17.1. Built-in
  • 17.2. Writing own management commands
• 18. i18n and l10n
  • 18.1. Timezone
  • 18.2. i18n - internationalization
• 19. Database
  • 19.1. Database schema migration
  • 19.2. Management commands
  • 19.3. Database settings
  • 19.4. Fixtures
  • 19.5. Multiple DB and db routing
• 20. Cache
• 21. Signals
  • 21.1. Available signals
  • 21.2. Examples
• 22. Auth and Permissions
  • 22.1. Basic Auth
  • 22.2. Require Permissions
• 23. API CORS
• 24. Standalone scripts
• 25. Tests and quality
  • 25.1. Tests
  • 25.2. Test URLs
  • 25.3. SonarQube
  • 25.4. Debug Toolbar
• 26. Tests
• 27. CI/CD - Continuous Integration and Deployment
  • 27.1. Bitbucket Pipelines
  • 27.2. Travis
• 28. Deployment
  • 28.1. Deployment platforms
  • 28.2. Packaging
  • 28.3. Staticfils serving

Numerical Analysis, Data Science

Numerical Analysis, Data Science

• 1. Introduction
  • 1.1. Python Numerical Analysis Program
• 2. Jupyter
  • 2.1. Jupyter
• 3. Python
  • 3.1. Tools and libs
  • 3.2. Python Syntax
  • 3.3. Types
  • 3.4. Sequences
  • 3.5. Mappings
  • 3.6. Conditionals
  • 3.7. Looping
  • 3.8. Control Flow
  • 3.9. Functions
  • 3.10. Object Oriented Programming
  • 3.11. Packages
  • 3.12. Math
  • 3.13. Serialization
  • 3.14. Performance
• 4. Numpy
  • 4.1. Numpy
  • 4.2. Built-ins
  • 4.3. Data Import and Export
  • 4.4. Array Create
  • 4.5. Array Random
  • 4.6. Array Data Types
  • 4.7. Array Attributes
  • 4.8. Array Shape
  • 4.9. Array Axis
  • 4.10. Array Indexing
  • 4.11. Array Slicing
  • 4.12. Array Iteration
  • 4.13. Array Rounding
  • 4.14. Array Methods
  • 4.15. Array Arithmetic
  • 4.16. Array Concatenation
  • 4.17. Array Serialize
  • 4.18. Array Statistics
  • 4.19. Array Logic
  • 4.20. Array Select
  • 4.21. Statistics
  • 4.22. Trigonometry
  • 4.23. Polynomials
  • 4.24. Linear Algebra
• 5. Pandas
  • 5.1. Pandas
  • 5.2. Types Basics
  • 5.3. Types Advanced
  • 5.4. Series Create
  • 5.5. Series Attributes
  • 5.6. Series Index
  • 5.7. Series Indexing
  • 5.8. Series Slicing
  • 5.9. Series Update
  • 5.10. Series Alter
  • 5.11. Series Arithmetic
  • 5.12. Series Statistics
  • 5.13. Series Mapping
  • 5.14. DataFrame Cleaning
  • 5.15. DataFrame Create
  • 5.16. DataFrame Import
  • 5.17. DataFrame Index
  • 5.18. DataFrame Attributes
  • 5.19. DataFrame Select
  • 5.20. DataFrame Indexing
  • 5.21. DataFrame At
  • 5.22. DataFrame Slicing
  • 5.23. DataFrame Loc
  • 5.24. DataFrame Alter
  • 5.25. DataFrame NaN
  • 5.26. DataFrame Sample
  • 5.27. DataFrame Plotting
  • 5.28. DataFrame Sort
  • 5.29. DataFrame Statistics
  • 5.30. DataFrame Mapping
  • 5.31. DataFrame Cleaning
  • 5.32. DataFrame Group By
  • 5.33. DataFrame Aggregations
  • 5.34. DataFrame Join
  • 5.35. DataFrame Export Data
  • 5.36. Date and Time Types
  • 5.37. Date and Time Timezones
  • 5.38. Date and Time Shifts
  • 5.39. Date and Time Frequency
  • 5.40. Date and Time Calendar
  • 5.41. Configuration
  • 5.42. Example Workflow
  • 5.43. References
• 6. Matplotlib
  • 6.1. Matplotlib
  • 6.2. Import/Export
  • 6.3. How to understand charts?
  • 6.4. Plot Styles
  • 6.5. Line Chart
  • 6.6. Bar Chart
  • 6.7. Box Chart
  • 6.8. Histogram
  • 6.9. Examples
  • 6.10. Scatter Plot
  • 6.11. Stacked Plot
  • 6.12. Pie Chart
  • 6.13. 3D Charts
  • 6.14. Advanced Styling
  • 6.15. Basic customizations
  • 6.16. Multiple figures and plots
  • 6.17. Logarithmic and other nonlinear axes
  • 6.18. Matplotlib Examples
  • 6.19. Matplotlib Plot Lifecycle
  • 6.20. Assignments
• 7. Data Visualization
  • 7.1. Plotly
  • 7.2. Bokeh
  • 7.3. Seaborn
  • 7.4. Generowanie grafów
• 8. Scipy
  • 8.1. Zastosowanie naukowe

Machine Learning

Machine Learning

• 1. Introduction
  • 1.1. What is Data?
  • 1.2. Basic Statistics
  • 1.3. Machine Learning Introduction
  • 1.4. Glossary PL
  • 1.5. Glossary
  • 1.6. Datasets
  • 1.7. Machine Learning Algorithms
  • 1.8. Features
  • 1.9. Classifiers
• 2. Sklearn
  • 2.1. scikit-learn
• 3. Model Quality
  • 3.1. Model Quality
  • 3.2. Principal Component Analysis
• 4. Decision Trees
  • 4.1. Decision Tree
• 5. Regressions
  • 5.1. Linear Regression
  • 5.2. Logistic Regression
• 6. K-Nearest Neighbors
  • 6.1. K Nearest Neighbors
• 7. Bayes
  • 7.1. Naive Bayes
• 8. Support Vector Machines
  • 8.1. Support Vector Machines
• 9. Clustering
  • 9.1. K-Means Clustering
• 10. Neural Networks
  • 10.1. Deep Neural Network
  • 10.2. Convolutional Neural Network
• 11. References
  • 11.1. References
  • 11.2. Notes

Blogposts

Blogposts

• 1. Wprowadzenie do Machine Learning w Pythonie

Appendixes

Appendixes

• 1. History
  • 1.1. BDFL - Guido van Rossum
  • 1.2. Name
  • 1.3. PSF - Python Software Foundation
• 2. Further reading
  • 2.1. My favourite sources
  • 2.2. Documentation
  • 2.3. Working with Excel
  • 2.4. IDE - Integrated Development Environment
  • 2.5. Others
  • 2.6. IT Fun
  • 2.7. Wyzwania i potyczki
• 3. Books
  • 3.1. Algorithms
  • 3.2. Databases
  • 3.3. Software Engineering Practises
  • 3.4. Design patterns
  • 3.5. Refactoring
  • 3.6. Python
• 4. Video
  • 4.1. Collections of videos
  • 4.2. Python
  • 4.3. Machine Learning and Data Science
  • 4.4. Filmy z ostatnich konferencji
• 5. Python 2 vs. 3
  • 5.1. six
  • 5.2. Lista kompatybilności Python 2 i Python 3
  • 5.3. Unicode i kodowanie znaków
  • 5.4. Biblioteki standardowe w 2 i 3
• 6. Python Intermediate
  • 6.1. Description
  • 6.2. Agenda
  • 6.3. Survey before workshop
  • 6.4. Certificate
• 7. Python Advanced
  • 7.1. Description
  • 7.2. Agenda
  • 7.3. Survey before workshop
  • 7.4. Certificate
• 8. Python in Networking
  • 8.1. Description
  • 8.2. Agenda
  • 8.3. Survey before workshop
  • 8.4. Certificate
• 9. Django Framework
  • 9.1. Description
  • 9.2. Agenda
  • 9.3. Survey before workshop
  • 9.4. Certificate
• 10. Python in Testing
  • 10.1. Description
  • 10.2. Agenda
  • 10.3. Survey before workshop
  • 10.4. Certificate
• 11. Python in Science and Engineering
  • 11.1. Description
  • 11.2. Agenda
  • 11.3. Survey before workshop
  • 11.4. Certificate
• 12. Python in Machine Learning
  • 12.1. Description
  • 12.2. Agenda
  • 12.3. Survey before workshop
  • 12.4. Certificate
• 13. References
  • 13.1. Bibliography
  • 13.2. Images
  • 13.3. Video

Bootcamp

Bootcamp

• 1. CSV
  • 1.1. Dialects
  • 1.2. Read data from CSV file
  • 1.3. Write data to CSV file
  • 1.4. Parsing non-CSV files
  • 1.5. Assignments
• 2. JSON
  • 2.1. JSON syntax
  • 2.2. JSON Serialization of simple objects
  • 2.3. Serializing datetime and date
  • 2.4. Serializing objects
  • 2.5. Pretty Printing JSON
  • 2.6. Assignments
• 3. XML
  • 3.1. xml module from standard library
  • 3.2. lxml module
  • 3.3. Working with HTML
• 4. XSLT
  • 4.1. Example 1
  • 4.2. Example 2
  • 4.3. Example 3
  • 4.4. XSLT Transformation
• 5. SQL
  • 5.1. ACID
  • 5.2. CREATE
  • 5.3. INSERT
  • 5.4. AUTOINCREMENT
  • 5.5. COMMIT and ROLLBACK
  • 5.6. SELECT
  • 5.7. UPDATE
  • 5.8. GROUP BY
  • 5.9. HAVING
  • 5.10. ALTER
  • 5.11. DROP
  • 5.12. DELETE
  • 5.13. JOIN
  • 5.14. TRUNCATE
• 6. Database SQL Injection
  • 6.1. Prepare query
  • 6.2. Get user input
  • 6.3. Execute query
• 7. Database SQLite
  • 7.1. SQL Syntax
  • 7.2. Connection
  • 7.3. Execute
  • 7.4. Executemany
  • 7.5. Results
  • 7.6. Cursor
  • 7.7. Use cases
  • 7.8. Assignments
• 8. Requests
  • 8.1. Basic API
  • 8.2. Response
  • 8.3. GET Requests
  • 8.4. POST Requests
  • 8.5. DELETE Requests
  • 8.6. Custom Headers
  • 8.7. Timeout
  • 8.8. Basic Auth
  • 8.9. Requests OAuth
  • 8.10. Assignments
• 9. Beautifulsoup
  • 9.1. BeautifulSoup
  • 9.2. Assignments