cpython | longer updated!** CPython

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.

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:

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:

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.

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.

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

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.

I reinstalled reportlab with this command:

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.