cpython | The Python programming language

 by   python Python Version: v3.12.0b2 License: Non-SPDX

kandi X-RAY | cpython Summary

kandi X-RAY | cpython Summary

cpython is a Python library. cpython has no bugs and it has high support. However cpython has 1 vulnerabilities, it build file is not available and it has a Non-SPDX License. You can install using 'pip install cpython' or download it from GitHub, PyPI.

The Python programming language
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              cpython has a highly active ecosystem.
              It has 53983 star(s) with 27021 fork(s). There are 1490 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 6741 open issues and 56854 have been closed. On average issues are closed in 182 days. There are 1507 open pull requests and 0 closed requests.
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              It has a negative sentiment in the developer community.
              The latest version of cpython is v3.12.0b2

            kandi-Quality Quality

              cpython has 0 bugs and 0 code smells.

            kandi-Security Security

              cpython has 1 vulnerability issues reported (0 critical, 0 high, 1 medium, 0 low).
              cpython code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              cpython has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              cpython releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              cpython has no build file. You will be need to create the build yourself to build the component from source.
              It has 678976 lines of code, 60120 functions and 2143 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed cpython and discovered the below as its top functions. This is intended to give you an instant insight into cpython implemented functionality, and help decide if they suit your requirements.
            • Generate output templates .
            • Parse a parameter .
            • Generate the diff between two lines .
            • Builds a universal Python library .
            • Create theme highlighting .
            • Parse the input stream .
            • Instantiate a time string .
            • Make the Unicode database .
            • Parse known arguments .
            • Return the power of x and other .
            Get all kandi verified functions for this library.

            cpython Key Features

            No Key Features are available at this moment for cpython.

            cpython Examples and Code Snippets

            JavaScript to CPython calling convention adaptors
            Pythondot img1Lines of Code : 24dot img1License : Weak Copyleft (MPL-2.0)
            copy iconCopy
            try {
              // body of function here
            } catch (e) {
              // wrap e in a Python exception and set the Python error indicator
              // return error code
            }
            
            EM_JS_REF(JsRef, hiwire_call, (JsRef idfunc, JsRef idargs), {
              let jsfunc = Hiwire.get_value(idfunc);
              let  
            pyTelegramBotAPI - webhook cpython echo bot
            Pythondot img2Lines of Code : 54dot img2License : Non-SPDX (GNU General Public License v2.0)
            copy iconCopy
            #!/usr/bin/env python
            # -*- coding: utf-8 -*-
            
            # This is a simple echo bot using decorators and webhook with BaseHTTPServer
            # It echoes any incoming text messages and does not use the polling method.
            
            try:
                # Python 2
                from BaseHTTPServer impor  
            Initialize the TFLite plugin .
            pythondot img3Lines of Code : 51dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def __init__(self, library, options=None):
                """Loads delegate from the shared library.
            
                Args:
                  library: Shared library name.
                  options: Dictionary of options that are required to load the delegate. All
                    keys and values in the d  

            Community Discussions

            QUESTION

            How does CPython implement os.environ?
            Asked 2022-Mar-30 at 18:51

            I was looking through source and noticed that it references a variable environ in methods before its defined:

            ...

            ANSWER

            Answered 2022-Mar-30 at 18:51

            TLDR search for from posix import * in os module content.

            The os module imports all public symbols from posix (Unix) or nt (Windows) low-level module at the beginning of os.py.

            posix exposes environ as a plain Python dict. os wraps it with _Environ dict-like object that updates environment variables on _Environ items changing.

            Source https://stackoverflow.com/questions/71641609

            QUESTION

            cannot import name '_registerMatType' from 'cv2.cv2'
            Asked 2022-Mar-17 at 14:47

            I got below error message when I run model_main_tf2.py on Object Detection API:

            ...

            ANSWER

            Answered 2021-Dec-31 at 03:38

            The same thing occurred to me yesterday when I used Colab. A possible reason may be that the version of opencv-python(4.1.2.30) does not match opencv-python-headless(4.5.5.62). Or the latest version 4.5.5 may have something wrong...

            I uninstalled opencv-python-headless==4.5.5.62 and installed 4.1.2.30 and it fixed.

            Source https://stackoverflow.com/questions/70537488

            QUESTION

            How is CPython implemented?
            Asked 2022-Feb-10 at 02:37

            So I lately came across an explanation for Python's interpreter and compiler (CPython specifically).

            Please correct me if I'm wrong. I just want to be sure I understand these specific concepts.

            So CPython gets both compiled (to bytecode) and then interpreted (in the PVM)? And what does the PVM do exactly? Does it read the bytecode line by line, and translate each one to binary instructions that can be executed on a specific computer? Does this mean that a computer based on an Intel processor needs a different PVM from an AMD-based computer?

            ...

            ANSWER

            Answered 2022-Feb-09 at 13:49
            1. Yes, CPython is compiled to bytecode which is then executed by the virtual machine.
            2. The virtual machine executes instructions one-by-one. It's written in C (but you can write it in another language) and looks like a huge if/else statement like "if the current instruction is this, do this; if the instruction is this, do another thing", and so on. Instructions aren't translated to binary - that's why it's called an interpreter.
              1. You can find the list of instructions here: https://docs.python.org/3.10/library/dis.html#python-bytecode-instructions
              2. The implementation of the VM is available here: https://github.com/python/cpython/blob/f71a69aa9209cf67cc1060051b147d6afa379bba/Python/ceval.c#L1718
            3. Bytecode doesn't have a concept of "line": it's just a stream of bytes. The interpreter can read one byte at a time and use another if/else statement to decide what instruction it's looking at. For example:

            Source https://stackoverflow.com/questions/71050098

            QUESTION

            Why does the float object behave differently with the "is" operator?
            Asked 2022-Jan-25 at 18:49

            As far as I know cpython implementation keeps the same object for some same values in order to save memory. For example when I create 2 strings with the value hello, cpython does not create 2 different PyObject:

            ...

            ANSWER

            Answered 2022-Jan-25 at 18:49

            Mutable objects always create a new object, otherwise the data would be shared. There's not much to explain here, as if you append an item to an empty list, you don't want all of the empty lists to have that item.

            Immutable objects behave in a completely different manner:

            • Strings get interned. If they are smaller than 20 alphanumeric characters, and are static (consts in the code, function names, etc), they get cached and are accessed from a special mapping reserved for these. It is to save memory but more importantly used to have a faster comparison. Python uses a lot of dictionary access operations under the hood which require string comparison. Being able to compare 2 strings like attribute or function names by comparing their memory address instead of the actual value, is a significant runtime improvement.

            • Booleans simply return the same object. Considering there are only 2 available, it makes no sense creating them again and again.

            • Small integers (from -5 to 256) by default, are also cached. These are used quite often, just about everywhere. Every time an integer is in that range, CPython simply returns the same object.

            Floats however are not cached. Unlike integers, where the numbers 0-10 are extremely common, 1.0 isn't guaranteed to be more used than 2.0 or 0.1. That's why float() simply returns a new float. We could have optimized the empty float(), and we can check for speed benefits but it might not have made such a difference.

            The confusion starts to arise when float(0.0) is float(0.0). Python has numerous optimizations built in:

            • First of all, consts are saved in each function's code object. 0.0 is 0.0 simply refers to the same object. It is a compile-time optimization.

            • Second of all, float(0.0) takes the 0.0 object, and since it's a float (which is immutable), it simply returns it. No need to create a new object if it's already a float.

            • Lastly, 1.0 + 1.0 is 2.0 will also work. The reason is that 1.0 + 1.0 is calculated on compile time and then references the same 2.0 object:

            Source https://stackoverflow.com/questions/70452989

            QUESTION

            Are generators with context managers an anti-pattern?
            Asked 2022-Jan-17 at 17:17

            I'm wondering about code like this:

            ...

            ANSWER

            Answered 2022-Jan-17 at 14:48

            There are two answers to your question :

            • the absolutist : indeed, the context managers will not serve their role, the GC will have to clean the mess that should not have happened
            • the pragmatic : true, but is it actually a problem ? Your file handle will get released a few milliseconds later, what's the bother ? Does it have a measurable impact on production, or is it just bikeshedding ?

            I'm not an expert to Python alt implementations' differences (see this page for PyPy's example), but I posit that this lifetime problem will not occur in 99% of cases. If you happen to hit in prod, then yes, you should address it (either with your proposal, or a mix of generator with context manager) otherwise, why bother ? I mean it in a kind way : your point is strictly valid, but irrelevant to most cases.

            Source https://stackoverflow.com/questions/70729329

            QUESTION

            How to use pandas on M1 mac? (without rosetta or changing to x86 environment in any other way)
            Asked 2022-Jan-09 at 14:10

            Last I wrote a python project was less than 2 months ago and everything worked fine. I'm not sure if while working on other project I messed something up on my mac but now when trying to run python files which used to run perfectly, the following error appears:

            ...

            ANSWER

            Answered 2022-Jan-09 at 14:10

            You should try using miniforge.

            its definition from its GitHub repository:

            This repository holds a minimal installer for Conda specific to conda-forge. Miniforge allows you to install the conda package manager with the following features pre-configured:

            Its main feature that will be useful for us

            An emphasis on supporting various CPU architectures (x86_64, ppc64le, and aarch64 including Apple M1).

            The Process I use:

            1. Create a conda environment and usually go with "python3.9".
            2. Install the packages from the conda, most of them are available but some are not.
            3. After trying and installing all the packages possible with miniforge, I use PIP for the remaining packages.

            This workflow has worked pretty well for me and hope it helps you. I want to utilize the native m1 performance and I think you will be able to see the difference.

            By default, miniforge only downloads arm compatible builds of python packages. till now I have not faced any major issue working with most data science libraries, except with PyTorch.

            Source https://stackoverflow.com/questions/70628759

            QUESTION

            I am unable to create a new virtualenv in ubuntu?
            Asked 2022-Jan-05 at 05:54

            So, I installed virtualenv in ubuntu terminal. I installed using the following commands:

            ...

            ANSWER

            Answered 2022-Jan-04 at 19:21

            There is no need to use virtualenv anymore. Since Python3.3, you can use venv to create virtual environments.

            Source https://stackoverflow.com/questions/70583980

            QUESTION

            Brownie not working: Cython undefined symbol _PyGen_Send
            Asked 2021-Dec-22 at 20:40

            I set up my development environment on Fedora 35 and when I run any brownie command such as $ brownie console or even brownie --version I get the following error:

            ...

            ANSWER

            Answered 2021-Dec-22 at 20:40

            The problem here seems to be Python 3.10.1!

            I used anaconda to create a new virtual environment with Python 3.8.12, installed brownie using pipx install --python python3.8 eth-brownie and it worked!

            The trick here was, to also tell pipx to use another python version, otherwise it would create a dependency to the global python version, which is python 3.10 in my case.

            Source https://stackoverflow.com/questions/70453104

            QUESTION

            Symbol not found in flat namespace '_FT_Done_Face' from reportlab with Python@3.9 on macOS 12
            Asked 2021-Dec-20 at 13:45

            I have a django project using easy-thumbnail as a dependency.

            Installing all packages with pip is working as expected, but when I try to run my app I get this error:

            ...

            ANSWER

            Answered 2021-Nov-15 at 14:19

            I reinstalled reportlab with this command:

            Source https://stackoverflow.com/questions/69973873

            QUESTION

            on what systems does Python not use IEEE-754 double precision floats
            Asked 2021-Dec-02 at 17:25

            Python makes various references to IEEE 754 floating point operations, but doesn't guarantee 1 2 that it'll be used at runtime. I'm therefore wondering where this isn't the case.

            CPython source code defers to whatever the C compiler is using for a double, which in practice is an IEEE 754-2008 binary64 on all common systems I'm aware of, e.g.:

            • Linux and BSD distros (e.g. FreeBSD, OpenBSD, NetBSD)
              • Intel i386/x86 and x86-64
              • ARM: AArch64
              • Power: PPC64
            • MacOS all architectures supported are 754 compatible
            • Windows x86 and x86-64 systems

            I'm aware there are other platforms it's known to build on but don't know how these work out in practice.

            ...

            ANSWER

            Answered 2021-Dec-02 at 17:25

            In theory, as you say, CPython is designed to be buildable and usable on any platform without caring about what floating-point format their C double is using.

            In practice, two things are true:

            • To the best of my knowledge, CPython has not met a system that's not using IEEE 754 binary64 format for its C double within the last 15 years (though I'd love to hear stories to the contrary; I've been asking about this at conferences and the like for a while). My knowledge is a long way from perfect, but I've been involved with mathematical and floating-point-related aspects of CPython core development for at least 13 of those 15 years, and paying close attention to floating-point related issues in that time. I haven't seen any indications on the bug tracker or elsewhere that anyone has been trying to run CPython on systems using a floating-point format other than IEEE 754 binary64.

            • I strongly suspect that the first time modern CPython does meet such a system, there will be a significant number of test failures, and so the core developers are likely to find out about it fairly quickly. While we've made an effort to make things format-agnostic, it's currently close to impossible to do any testing of CPython on other formats, and it's highly likely that there are some places that implicitly assume IEEE 754 format or semantics, and that will break for something more exotic. We have yet to see any reports of such breakage.

            There's one exception to the "no bug reports" report above. It's this issue: https://bugs.python.org/issue27444. There, Greg Stark reported that there were indeed failures using VAX floating-point. It's not clear to me whether the original bug report came from a system that emulated VAX floating-point.

            I joined the CPython core development team in 2008. Back then, while I was working on floating-point-related issues I tried to keep in mind 5 different floating-point formats: IEEE 754 binary64, IBM's hex floating-point format as used in their zSeries mainframes, the Cray floating-point format used in the SV1 and earlier machines, and the VAX D-float and G-float formats; anything else was too ancient to be worth worrying about. Since then, the VAX formats are no longer worth caring about. Cray machines now use IEEE 754 floating-point. The IBM hex floating-point format is very much still in existence, but in practice the relevant IBM hardware also has support for IEEE 754, and the IBM machines that Python meets all seem to be using IEEE 754 floating-point.

            Rather than exotic floating-point formats, the modern challenges seem to be more to do with variations in adherence to the rest of the IEEE 754 standard: systems that don't support NaNs, or treat subnormals differently, or allow use of higher precision for intermediate operations, or where compilers make behaviour-changing optimizations.

            The above is all about CPython-the-implementation, not Python-the-language. But the story for the Python language is largely similar. In theory, it makes no assumptions about the floating-point format. In practice, I don't know of any alternative Python implementations that don't end up using an IEEE 754 binary format (if not semantics) for the float type. IronPython and Jython both target runtimes that are explicit that floating-point will be IEEE 754 binary64. JavaScript-based versions of Python will similarly presumably be using JavaScript's Number type, which is required to be IEEE 754 binary64 by the ECMAScript standard. PyPy runs on more-or-less the same platforms that CPython does, with the same floating-point formats. MicroPython uses single-precision for its float type, but as far as I know that's still IEEE 754 binary32 in practice.

            Source https://stackoverflow.com/questions/70184494

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            Vulnerabilities

            No vulnerabilities reported

            Install cpython

            You can install using 'pip install cpython' or download it from GitHub, PyPI.
            You can use cpython like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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