Python Language
- Getting started with Python Language
- Awesome Book
- Awesome Community
- Awesome Course
- Awesome Tutorial
- Awesome YouTube
- *args and **kwargs
- 2to3 tool
- Abstract Base Classes (abc)
- Abstract syntax tree
- Accessing Python source code and bytecode
- Alternatives to switch statement from other languages
- ArcPy
- Arrays
- Asyncio Module
- Attribute Access
- Audio
- Basic Curses with Python
- Basic Input and Output
- Binary Data
- Bitwise Operators
- Boolean Operators
- Call Python from C#
- Checking Path Existence and Permissions
- ChemPy - python package
- Classes
- CLI subcommands with precise help output
- Code blocks, execution frames, and namespaces
- Collections module
- Comments and Documentation
- Common Pitfalls
- Commonwealth Exceptions
- Comparisons
- Complex math
- Conditionals
- configparser
- Connecting Python to SQL Server
- Context Managers (“with” Statement)
- Copying data
- Counting
- Create virtual environment with virtualenvwrapper in windows
- Creating a Windows service using Python
- Creating Python packages
- ctypes
- Data Serialization
- Data Visualization with Python
- Database Access
- Date and Time
- Date Formatting
- Debugging
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- Defining functions with list arguments
- Deployment
- Deque Module
- Descriptor
- Design Patterns
- Dictionary
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- Distribution
- Django
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- Enum
- Exceptions
- Exponentiation
- Files & Folders I/O
- Filter
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- Functional Programming in Python
- Functions
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- Garbage Collection
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- getting start with GZip
- graph-tool
- groupby()
- hashlib
- Heapq
- Hidden Features
- HTML Parsing
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- ijson
- Immutable datatypes(int, float, str, tuple and frozensets)
- Importing modules
- Incompatibilities moving from Python 2 to Python 3
- Indentation
- Indexing and Slicing
- Input, Subset and Output External Data Files using Pandas
- Introduction to RabbitMQ using AMQPStorm
- IoT Programming with Python and Raspberry PI
- Iterables and Iterators
- Itertools Module
- JSON Module
- kivy - Cross-platform Python Framework for NUI Development
- Linked List Node
- Linked lists
- List
- List comprehensions
- List Comprehensions
- List destructuring (aka packing and unpacking)
- List slicing (selecting parts of lists)
- Logging
- Loops
- Manipulating XML
- Map Function
- Math Module
- Metaclasses
- Method Overriding
- Mixins
- Multidimensional arrays
- Multiprocessing
- Multithreading
- Mutable vs Immutable (and Hashable) in Python
- Neo4j and Cypher using Py2Neo
- Non-official Python implementations
- Operator module
- Operator Precedence
- Optical Character Recognition
- os.path
- Overloading
- Pandas Transform: Preform operations on groups and concatenate the results
- Parallel computation
- Parsing Command Line arguments
- Partial functions
- Performance optimization
- Pickle data serialisation
- Pillow
- pip: PyPI Package Manager
- Plotting with Matplotlib
- Plugin and Extension Classes
- Polymorphism
- PostgreSQL
- Processes and Threads
- Profiling
- Property Objects
- py.test
- pyaudio
- pyautogui module
- pygame
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- Python and Excel
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- Python concurrency
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- Python HTTP Server
- Python Lex-Yacc
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- Python Persistence
- Python Requests Post
- Python Serial Communication (pyserial)
- Python Server Sent Events
- Python speed of program
- Python Virtual Environment - virtualenv
- Queue Module
- Raise Custom Errors / Exceptions
- Random module
- Create cryptographically secure random numbers
- Creating a random user password
- Creating random integers and floats: randint, randrange, random, and uniform
- Random and sequences: shuffle, choice and sample
- Random Binary Decision
- Reproducible random numbers: Seed and State
- Reading and Writing CSV
- Recursion
- Reduce
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- Searching
- Secure Shell Connection in Python
- Security and Cryptography
- Set
- setup.py
- shelve
- Similarities in syntax, Differences in meaning: Python vs. JavaScript
- Simple Mathematical Operators
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- Sorting, Minimum and Maximum
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- Stack
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- String Methods
- String representations of class instances: __str__ and __repr__ methods
- Subprocess Library
- sys
- tempfile NamedTemporaryFile
- Templates in python
- The __name__ special variable
- The base64 Module
- The dis module
- The Interpreter (Command Line Console)
- The locale Module
- The os Module
- The pass statement
- The Print Function
- tkinter
- Tuple
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- Type Hints
- Unicode
- Unicode and bytes
- Unit Testing
- Unzipping Files
- urllib
- Usage of "pip" module: PyPI Package Manager
- User-Defined Methods
- Using loops within functions
- Variable Scope and Binding
- virtual environment with virtualenvwrapper
- Virtual environments
- Web scraping with Python
- Web Server Gateway Interface (WSGI)
- Webbrowser Module
- Websockets
- Working around the Global Interpreter Lock (GIL)
- Working with ZIP archives
- Writing extensions
- Writing to CSV from String or List
Python Language Random module Create cryptographically secure random numbers
Example
By default the Python random module use the Mersenne Twister PRNG to generate random numbers, which, although suitable in domains like simulations, fails to meet security requirements in more demanding environments.
In order to create a cryptographically secure pseudorandom number, one can use SystemRandom which, by using os.urandom, is able to act as a Cryptographically secure pseudorandom number generator, CPRNG.
The easiest way to use it simply involves initializing the SystemRandom class. The methods provided are similar to the ones exported by the random module.
from random import SystemRandom
secure_rand_gen = SystemRandom()
In order to create a random sequence of 10 ints in range [0, 20], one can simply call randrange():
print([secure_rand_gen.randrange(10) for i in range(10)])
# [9, 6, 9, 2, 2, 3, 8, 0, 9, 9]
To create a random integer in a given range, one can use randint:
print(secure_rand_gen.randint(0, 20))
# 5
and, accordingly for all other methods. The interface is exactly the same, the only change is the underlying number generator.
You can also use os.urandom directly to obtain cryptographically secure random bytes.
