Complete_Python_Notes

Python_eBook

Features of Python

1. Simple Reading a good Python program feels almost like reading English( very strict English). This pseudo-code nature of Python is one of its greatest strengths. It allows you to concentrate on the solution to the problem rather than the language itself.
2. Easy to Learn Python is extremely easy to learn because Python has an extraordinarily simple syntax. Elegant syntax, making the programs you write easier to read.
3. Free and Open Source Python is an example of a FLOSS (Free/Libré and Open Source Software). In simple terms, you can freely distribute copies of this software, read it's source code, make changes to it, use pieces of it in new free programs, and that you know you can do these things. This is one of the reasons why Python is so good.
4. High-level Language When you write programs in Python, you never need to bother about the low-level details such as managing the memory used by your program, etc.
5. Portable Due to its open-source nature, Python has been ported (i.e. changed to make it work on) to many platforms. All your Python programs can work on any of these platforms without requiring any changes at all. You can use Python on Linux, Windows, FreeBSD, Macintosh, Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows CE and even PocketPC !
6. Interpreted A program written in a compiled language like C or C++ is converted from the source language i.e. C or C++ into a language that is spoken by your computer (binary code i.e. 0s and 1s) using a compiler with various flags and options. When you run the program, the linker/loader software copies the program from hard disk to memory and starts running it. Python, on the other hand, does not need compilation to binary. You just run the program directly from the source code. Internally, Python converts the source code into an intermediate form called bytecodes and then translates this into the native language of your computer and then runs it. All this, actually, makes using Python much easier since you don't have to worry about compiling the program, making sure that the proper libraries are linked and loaded, etc, etc. This also makes your Python programs much more portable, since you can just copy your Python program onto another computer and it just works.
7. Procedure Oriented and Object Oriented Python supports procedure-oriented programming as well as object-oriented programming. In procedure-oriented languages, the program is built around procedures or functions which are nothing but reusable pieces of programs. In object-oriented languages, the program is built around objects which combine data and functionality. Python has a very powerful but simplistic way of doing OOP, especially when compared to big languages like C++ or Java.
8. Extensible If you need a critical piece of code to run very fast or want to have some piece of algorithm not to be open, you can code that part of your program in C or C++ and then use them from your Python program.
9. Embeddable You can embed Python within your C/C++ programs to give 'scripting' capabilities for your program's users.
10. Extensive Libraries The Python Standard Library is huge indeed. It can help you do various things involving regular expressions, documentation generation, unit testing, threading, databases, web browsers, CGI, ftp, email, XML, XML-RPC, HTML, WAV files, cryptography, GUI (graphical user interfaces), Tk, and other system-dependent stuff. Remember, all this is always available wherever Python is installed. This is called the 'Batteries Included' philosophy of Python.

The Python Tutorial

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.

The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, https://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.

The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.

This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.

For a description of standard objects and modules, see The Python Standard Library. The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual. There are also several books covering Python in depth.

This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library.

The Glossary is also worth going through.

1. Whetting Your Appetite
2. Using the Python Interpreter 2.1. Invoking the Interpreter 2.1.1. Argument Passing 2.1.2.8 Interactive Mode 2.2. The Interpreter and Its Environment 2.2.1. Source Code Encoding
3. An Informal Introduction to Python 3.1. Using Python as a Calculator 3.1.1. Numbers 3.1.2. Strings 3.1.3. Lists 3.2. First Steps Towards Programming
4. More Control Flow Tools 4.1. if Statements 4.2. for Statements 4.3. The range() Function 4.4. break and continue Statements, and else Clauses on Loops 4.5. pass Statements 4.6. Defining Functions 4.7. More on Defining Functions 4.7.1. Default Argument Values 4.7.2. Keyword Arguments 4.7.3. Arbitrary Argument Lists 4.7.4. Unpacking Argument Lists 4.7.5. Lambda Expressions 4.7.6. Documentation Strings 4.7.7. Function Annotations 4.8. Intermezzo: Coding Style
5. Data Structures 5.1. More on Lists 5.1.1. Using Lists as Stacks 5.1.2. Using Lists as Queues 5.1.3. List Comprehensions 5.1.4. Nested List Comprehensions 5.2. The del statement 5.3. Tuples and Sequences 5.4. Sets 5.5. Dictionaries 5.6. Looping Techniques 5.7. More on Conditions 5.8. Comparing Sequences and Other Types
6. Modules 6.1. More on Modules 6.1.1. Executing modules as scripts 6.1.2. The Module Search Path 6.1.3. “Compiled” Python files 6.2. Standard Modules 6.3. The dir() Function 6.4. Packages 6.4.1. Importing * From a Package 6.4.2. Intra-package References 6.4.3. Packages in Multiple Directories
7. Input and Output 7.1. Fancier Output Formatting 7.1.1. Formatted String Literals 7.1.2. The String format() Method 7.1.3. Manual String Formatting 7.1.4. Old string formatting 7.2. Reading and Writing Files 7.2.1. Methods of File Objects 7.2.2. Saving structured data with json
8. Errors and Exceptions 8.1. Syntax Errors 8.2. Exceptions 8.3. Handling Exceptions 8.4. Raising Exceptions 8.5. User-defined Exceptions 8.6. Defining Clean-up Actions 8.7. Predefined Clean-up Actions
9. Classes 9.1. A Word About Names and Objects 9.2. Python Scopes and Namespaces 9.2.1. Scopes and Namespaces Example 9.3. A First Look at Classes 9.3.1. Class Definition Syntax 9.3.2. Class Objects 9.3.3. Instance Objects 9.3.4. Method Objects 9.3.5. Class and Instance Variables 9.4. Random Remarks 9.5. Inheritance 9.5.1. Multiple Inheritance 9.6. Private Variables 9.7. Odds and Ends 9.8. Iterators 9.9. Generators 9.10. Generator Expressions
10. Brief Tour of the Standard Library 10.1. Operating System Interface 10.2. File Wildcards 10.3. Command Line Arguments 10.4. Error Output Redirection and Program Termination 10.5. String Pattern Matching 10.6. Mathematics 10.7. Internet Access 10.8. Dates and Times 10.9. Data Compression 10.10. Performance Measurement 10.11. Quality Control 10.12. Batteries Included
11. Brief Tour of the Standard Library — Part II 11.1. Output Formatting 11.2. Templating 11.3. Working with Binary Data Record Layouts 11.4. Multi-threading 11.5. Logging 11.6. Weak References 11.7. Tools for Working with Lists 11.8. Decimal Floating Point Arithmetic
12. Virtual Environments and Packages 12.1. Introduction 12.2. Creating Virtual Environments 12.3. Managing Packages with pip
13. What Now?
14. Interactive Input Editing and History Substitution 14.1. Tab Completion and History Editing 14.2. Alternatives to the Interactive Interpreter
15. Floating Point Arithmetic: Issues and Limitations 15.1. Representation Error
16. Appendix 16.1. Interactive Mode 16.1.1. Error Handling 16.1.2. Executable Python Scripts 16.1.3. The Interactive Startup File 16.1.4. The Customization Modules.

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