A remarkably powerful dynamic programming language

Python (32-bit)

Python (32-bit)

  -  24.3 MB  -  Open Source
Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. It was created by Guido van Rossum and initially released in 1991. Since then, it has gained immense popularity among developers of all levels of experience.

Key Features

Readability: Python's syntax emphasizes code readability with its clean and straightforward structure. The use of indentation instead of braces makes the code visually appealing and easy to understand.

Easy to Learn: It has a gentle learning curve, making it an excellent choice for beginners. The simplicity of its syntax allows new programmers to grasp fundamental concepts quickly and start writing functional code.

Large Standard Library: It provides a vast standard library that offers ready-to-use modules for various purposes. It includes modules for string manipulation, file handling, networking, web development, data processing, and much more. Leveraging the standard library saves development time and effort.

Dynamically Typed: It is dynamically typed, meaning that variable types are inferred at runtime. This flexibility allows developers to write code more quickly without explicitly declaring variable types.

Versatility: It can be used for a wide range of applications, including web development, data analysis, scientific computing, artificial intelligence, machine learning, automation, and scripting. Its versatility stems from its extensive library ecosystem, which provides specialized tools and frameworks for different domains.

Cross-platform Compatibility: Python 32bit is available on major operating systems like Windows, macOS, and Linux, ensuring portability across different environments. The app code written on one platform can typically run on another platform without modification.

Strong Community Support: It has a vibrant and active community of developers worldwide. This community contributes to the language's growth and provides extensive documentation, tutorials, forums, and open-source libraries. The community-driven nature of Python fosters collaboration and continuous improvement.

Python is commonly used in various fields:

Web Development: The web frameworks like Django, Flask, and Pyramid are widely adopted for building scalable and secure web applications.

Data Analysis and Scientific Computing: Python's libraries, such as NumPy, Pandas, and Matplotlib, enable data manipulation, analysis, and visualization. It is often used in scientific research, data science, and machine learning.

Automation and Scripting: Python's simplicity and ease of use make it a popular choice for automating repetitive tasks, writing scripts, and system administration.

Artificial Intelligence and Machine Learning: It offers powerful libraries like TensorFlow, PyTorch, and sci-kit-learn, which are extensively used in developing AI and machine learning models.

Desktop Application Development: It can be used to create cross-platform desktop applications using frameworks like PyQt and Tkinter.

How to Use

Install Python
: Download the installer for your operating system from the official website (python.org) and run it. Follow the installation instructions provided.

Choose a Code Editor or IDE: Select a code editor or Integrated Development Environment (IDE) for writing your Python code. Some popular options include Visual Studio Code, PyCharm, Atom, and Sublime Text. Install your preferred editor/IDE.

Write Your First Python Program: Open your code editor/IDE and create a new file with a .py extension. This is where you'll write your code. Start by writing a simple program, such as the classic "Hello, World!" program:

print("Hello, World!")

Save and Run Your Program: Save the file with a meaningful name and the .py extension. Open the command prompt or terminal, navigate to the directory where the file is saved, and execute the program using the command:

python filename.py

Learn Python Syntax: Familiarize yourself with Python's syntax and basic concepts. Learn about variables, data types, control flow statements (if-else, loops), functions, and more. Online tutorials, courses, and documentation are excellent resources for learning Python syntax.

Utilize Python Libraries: Python's strength lies in its extensive libraries. Explore libraries relevant to your projects, such as NumPy for numerical computations, Pandas for data manipulation, Matplotlib for data visualization, and TensorFlow or PyTorch for machine learning. Install libraries using the pip package manager:

pip install library_name

Practice and Explore: It offers a wide range of possibilities, so practice coding regularly. Experiment with different functionalities and libraries to expand your knowledge and discover Python's capabilities. Online coding challenges and projects can help you strengthen your skills.

Refer to Documentation and Online Resources: It has comprehensive documentation available on the official website (docs.python.org). It covers the language's syntax, standard libraries, and various topics. Additionally, online communities, forums, and tutorial websites offer valuable resources for learning and problem-solving.

Collaborate and Contribute: Engage with the community by participating in forums, joining local user groups, and contributing to open-source projects. Collaborating with others can enhance your learning experience and allow you to contribute to the growth of the ecosystem.

FAQ

Q: How do I install Python on my PC?

A: To install Python on your PC, visit the official website (python.org) and download the installer for your operating system. Run the installer and follow the instructions provided.

Q: Can I use Python on Windows, macOS, and Linux?
A: Yes, Python is compatible with all major operating systems, including Windows, macOS, and Linux. You can write and run Python code on any of these platforms.

Q: What code editor or IDE should I use for Python programming on PC?
A: There are several popular code editors and IDEs available for Python, such as Visual Studio Code, PyCharm, Atom, and Sublime Text. Choose the one that suits your preferences and workflow.

Q: How do I run a Python program on my PC?
A: Save your code with a .py extension. Open the command prompt or terminal, navigate to the directory where the Python file is saved, and run the command "python filename.py" to execute the program.

Q: What are Python packages and how do I install them?
A: Python packages are additional libraries or modules that extend Python's functionality. You can install packages using the pip package manager. For example, to install the NumPy package, run the command "pip install numpy" in the command prompt or terminal.

Q: Can I use Python to create graphical user interfaces (GUIs) for PC applications?
A: Yes, it provides various GUI frameworks like Tkinter, PyQt, and wxPython that allow you to create desktop applications with graphical interfaces.

Q: Is Python suitable for game development on PC?
A: Yes, it can be used for game development. Libraries like Pygame provide tools and functionality for creating 2D games, while engines like Panda3D and Pyglet offer more advanced capabilities.

Q: Can I use Python for web development on my PC?
A: Absolutely! It has powerful web frameworks like Django and Flask that enable you to build web applications, APIs, and websites using Python on your PC.

Q: Are there resources available for learning Python on PC?
A: Yes, there are numerous resources available for learning Python. You can find online tutorials, documentation, interactive courses, video tutorials, and books that cater to different learning styles and levels of expertise.

Q: Can I contribute to the Python community as a PC user?
A: Definitely! Python is an open-source language with an active community. You can contribute by reporting bugs, suggesting improvements, or even contributing code to the Python core or open-source projects.

Alternatives


JavaScript: Primarily used for web development, JavaScript is a versatile language with an extensive ecosystem of libraries and frameworks. It is particularly suitable for client-side scripting and interactive web applications.

R: A programming language specifically designed for statistical analysis and data visualization. It excels in the field of data science and is preferred by statisticians and researchers.

Java: A general-purpose language known for its robustness, scalability, and cross-platform compatibility. It is widely used for building enterprise-level applications, Android development, and large-scale systems.

C#: Developed by Microsoft, C# is a versatile language used for building Windows applications, web services, and game development using the Unity engine.

Ruby: A dynamic, object-oriented language known for its simplicity and elegant syntax. It is often used in web development frameworks like Ruby on Rails.

System Requirements
  • Operating System: Windows, macOS, Linux
  • Processor: 1 GHz or faster
  • RAM: 1 GB (minimum), 4 GB or more (recommended)
  • Disk Space: 200 MB for Python installation
PROS
  • Simplicity and readability
  • Vast library ecosystem
  • Cross-platform compatibility
  • Extensive community support
  • Integration capabilities
CONS
  • Global Interpreter Lock (GIL) can limit multi-threading performance
  • Relatively slower execution speed compared to low-level languages
  • Lack of a dedicated GUI (Graphical User Interface)
Conclusion

Overall, Python's versatility, simplicity, extensive library support, and thriving community make it a language that continues to thrive and evolve. Whether you are a beginner exploring programming or an experienced developer seeking a powerful toolset, Python is a language worth considering for its wide range of applications and its ability to facilitate efficient and effective development.

Also Available: Python (64-bit) and Python for Mac


  • Python 3.12.4 (32-bit) Screenshots

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    Python 3.12.4 (32-bit) Screenshot 1
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  • Python 3.12.4 (32-bit) Screenshot 4
  • Python 3.12.4 (32-bit) Screenshot 5

What's new in this version:

Security:
- os.mkdir() on Windows now accepts mode of 0o700 to restrict the new directory to the current user. This fixes CVE-2024-4030 affecting tempfile.mkdtemp() in scenarios where the base temporary directory is more permissive than the default.
- Update bundled libexpat to 2.6
- Detect BLAKE2, SHA3, Shake, & truncated SHA512 support in the OpenSSL-ish libcrypto library at build time. This allows hashlib to be used with libraries that do not to support every algorithm that upstream OpenSSL does

Core and Builtins:
- Fix execution of annotation scopes within classes when globals is set to a non-dict
- Speed up os.path.normpath() with a direct C call
- Fix bug where names are unexpectedly mangled in the bases of generic classes
- Fix bug where names appearing after a generic class are mangled as if they are in the generic class
- Fix os.path.isfile() on Windows for pipes
- Non-builtin modules built with argument clinic were crashing if used in a subinterpreter before the main interpreter. The objects that were causing the problem by leaking between interpreters carelessly have been fixed
- Fixes type.__type_params__ to return an empty tuple instead of a descriptor
- Fix _Py_ClearImmortal() assertion: use _Py_IsImmortal() to tolerate reference count lower than _Py_IMMORTAL_REFCNT. Fix the assertion for the stable ABI, when a C extension is built with Python 3.11 or lower
- Fix incorrect UnboundLocalError when two comprehensions in the same function both reference the same name, and in one comprehension the name is bound while in the other it’s an implicit global
- Break a loop between the Python implementation of the decimal module and the Python code for integer to string conversion. Also optimize integer to string conversion for values in the range from 9_000 to 135_000 decimal digits
- Fix bug where generator.close does not free the generator frame’s locals
- Fix crash in compiler on ‘async with’ that has many context managers
- Prevent agen.aclose() objects being re-used after .throw()
- prevent concurrent access to an async generator via athrow().throw() or asend().throw(
- Fixed a possible segfault during garbage collection of _asyncio.FutureIter object

Library:
- Fix regression to allow logging configuration with multiprocessing queue types
- Fix issue with shutil.rmtree() where a RecursionError is raised on deep directory trees
- Partially fix issue with shutil.rmtree() where a RecursionError is raised on deep directory trees. A recursion error is no longer raised when rmtree.avoids_symlink_attacks is false
- Fix performance regression in the tokenize module by caching the line token attribute and calculating the column offset more efficiently
- Fix issue with os.fwalk() where a RecursionError was raised on deep directory trees by adjusting the implementation to be iterative instead of recursive
- Now, the method sock_connect of asyncio.ProactorEventLoop raises a ValueError if given socket is not in non-blocking mode, as well as in other loop implementations
- Fix high DPI causes turtledemo(turtle-graphics examples) windows blurr
- Fix an AttributeError in the email module when re-fold a long address list. Also fix more cases of incorrect encoding of the address separator in the address list
- Make pdb return to caller frame correctly when f_trace of the caller frame is not se
- Fixed issue where kwargs were no longer passed to the logging handler QueueHandle
- The Python implementation of the decimal module could appear to hang in relatively small power cases (like 2**117) if context precision was set to a very high value. A different method to check for exactly representable results is used now that doesn’t rely on computing 10**precision (which could be effectively too large to compute)
- Fix inspect.signature() for non-comparable callables
- Fix an edge case in binascii.a2b_base64() strict mode, where excessive padding is not detected when no padding is necessary
- Fix an unraisable exception in telnetlib.Telnet.__del__() when the __init__() method was not called
- Fix a bug where sqlite3.iterdump() could fail if a custom row factory was used
- Fix regression introduced in gh-103193 that meant that calling inspect.getattr_static() on an instance would cause a strong reference to that instance’s class to persist in an internal cache in the inspect module. This caused unexpected memory consumption if the class was dynamically created, the class held strong references to other objects which took up a significant amount of memory, and the cache contained the sole strong reference to the class. The fix for the regression leads to a slowdown in getattr_static(), but the function should still be significantly faster than it was in Python 3.11
- Fixed unittest.mock.create_autospec() to configure parent mock with keyword arguments
- Fix incorrect argument substitution when typing.Unpack is used with the builtin tuple. typing.Unpack now raises TypeError when used with certain invalid types
- Fix dataclasses.dataclass() not creating a __weakref__ slot when subclassing typing.Generic
- Do not try to get the source line for made up file name “sys” in warnings
- Fix erroneous NameError when calling typing.get_type_hints() on a class that made use of PEP 695 type parameters in a module that had from __future__ import annotations at the top of the file
- Don’t raise DeprecationWarning when a sequence of parameters is used to bind indexed, nameless placeholders. See also gh-100668
- Fix TypeError in email.message.Message.get_payload() when the charset is RFC 2231 encoded
- Fix IndexError when parse some emails with invalid Message-ID (including one-off addresses generated by Microsoft Outlook)
- Improve the error messages emitted by tarfile deprecation warnings relating to PEP 706. If a filter argument is not provided to extract() or extractall, the deprecation warning now points to the line in the user’s code where the relevant function was called
- site module now parses .pth file with UTF-8 first, and locale encoding if UnicodeDecodeError happened. It supported only locale encoding before
- Fixes a bug when doctest.DocTestFinder was failing on wrapped builtin_function_or_method
- ipaddress.IPv6Address.is_loopback() will now return True for IPv4-mapped loopback addresses, i.e. addresses in the ::ffff:127.0.0.0/104 address space
- Fix support of non-ASCII user names in bytes paths in os.path.expanduser() on Posix
- Only treat '\n', '\r' and '\r\n' as line separators in re-folding the email messages. Preserve control characters '\v', '\f', '\x1c', '\x1d' and '\x1e' and Unicode line separators '\x85', '\u2028' and '\u2029' as is
-
Fixed various false positives and false negatives i:
- ipaddress.IPv4Address.is_private (see these docs for details)
- ipaddress.IPv4Address.is_global
- ipaddress.IPv6Address.is_private
- ipaddress.IPv6Address.is_global

- Also in the corresponding ipaddress.IPv4Network and ipaddress.IPv6Network attributes.
- Fix lack of newline characters in trace module output when line tracing is enabled but source code line for current frame is not available
- Fix missing spaces in email headers when the spaces are mixed with encoded 8-bit characters
- Prepare Tkinter for C API changes in Tcl 8.7/9.0 to avoid _tkinter.Tcl_Obj being unexpectedly returned instead of bool, str, bytearray, or int
- Fixed handling in inspect.Signature.bind() of keyword arguments having the same name as positional-only arguments when a variadic keyword argument (e.g. **kwargs) is present
- bpo-45767: Fix integer conversion in os.major(), os.minor(), and os.makedev(). Support device numbers larger than 2**63-1. Support non-existent device number (NODEV).
- bpo-40943: Fix several IndexError when parse emails with truncated Message-ID, address, routes, etc, e.g. example@.
- bpo-30988: Fix parsing of emails with invalid address headers having a leading or trailing dot
- Fix urllib.parse.urlunparse() and urllib.parse.urlunsplit() for URIs with path starting with multiple slashes and no authority
- bpo-15010: unittest.TestLoader.discover() now saves the original value of unittest.TestLoader._top_level_dir and restores it at the end of the call.

Documentation:
- The minimum Sphinx version required for the documentation is now 6.2.1
- Changes to documentation files and config outputs to reflect the new location for reporting bugs - i.e. GitHub rather than bugs.python.org

Tests:
- regrtest test runner: Add XML support to the refleak checker (-R option)

Windows:
- Adds Unicode support and fixes audit events for _winapi.CreateNamedPipe
- Fixes py.exe handling of shebangs like /usr/bin/env python3.12, which were previously interpreted as python3.exe instead of python3.12.exe
- Fixes launcher updates not being installed
- Update Windows installer to use SQLite 3.45.3
- Suppress the warning displayed on virtual environment creation when the requested and created paths differ only by a short (8.3 style) name. Warnings will continue to be shown if a junction or symlink in the path caused the venv to be created in a different location than originally requested

macOS:
- Update macOS installer to use SQLite 3.45.3
- Update macOS installer to Tcl/Tk 8.6.14

IDLE:
- bpo-34774: Use user-selected color theme for Help => IDLE Doc.

C API:
- Fix crash when a thread state that was created by PyGILState_Ensure() calls a destructor that during PyThreadState_Clear() that calls back into PyGILState_Ensure() and PyGILState_Release(). This might occur when in the free-threaded build or when using thread-local variables whose destructors call PyGILState_Ensure()
- Improve validation logic in the C implementation of datetime.fromisoformat() to better handle invalid years