There are already a couple of question on Python's .pyc files but I have not read about 1) when (in what circumstances) are these files created and 2) whether there is a good reason to keep them.
More specifically:
About (1): In 200 or more PY files (new and old ones) that I have run, I have seen only a couple of .pyc files created. However, in Python's own (installation) directory there are currently 1160 .pyc files, about half of the .py files! I don't know how many of them existed already on Python's installation, but about 450 of them have been certainly created after that. So, it's a mystery to me in what circumstances these files are created. Or are they always automatically created and then they are supposed to be deleted after the PY program terminates?
About (2): I have compared the execution timing .py against .pyc files and I cannot see any (significant) difference. I don't have large .py files or .py files with heave computations, etc. to test. But as far as "regular" files (small to medium size and with regular computations) are concerned, the .pyc files seem to be certainly useless. So, why should one keep them?
... Later: I just realized that a .pyc file is created only when its corresp. .py file is used as a module, i.e. it is imported. OK, there's so meaning in this! :)
when (in what circumstances) are these files created and
whether there is a good reason to keep them.
When the python script is imported from somewhere else
Sure, as they'll be automatically created again the next time, otherwise. You'll save processing time if you keep them and the disk usage should be negligible.
In order to understand about the reason for these .pyc files, you should take a look at this question and the nice graphic in the last answer, too: If Python is interpreted, what are .pyc files?
Related
This answer tells me that a .pyc file gets created when a .py file is run, which I understand saves loading time when re-run. Which makes me wonder what the point of the .py file is after the .pyc is created.
When backing up my code, or sharing it, I don't want to include redundant or extraneous files. Which filetype should I focus on?
Side question: I have one script that calls another. After running them, the called script got a .pyc file written, but the master script that does the calling did not. Why would that be?
Python .pyc files are generated when a module is imported, not when a top level script is run. I'm not sure what you mean by calling, but if you ran your master script from the command line and it imported the other script, then only the imported one gets a .pyc.
As for distributing .pyc files, they are minor version sensitive. If you bundle your own python or distribute multiple python-version sensitive files, then maybe. But best practice is to distribute the .py files.
Python's script and module rules seem a bit odd until you consider its installation model. A common installation model is that executables are installed somewhere on the system's PATH and shared libraries are installed somewhere in a library path.
Python's setup.py does the same thing. Top level scripts go on the PATH but modules and packages go in an library path. For instance on my system, pdb3 (a top level script) is at /usr/bin/pdb3 and os (an imported module) is at /usr/lib/python3.4/os.py. Suppose python compiled pdb3 to pdb3.pyc. Well, I'd still call pdb3 and the .pyc is useless. So why clutter the path?
Its common for installs to run as root or administrator so you have write access on those paths. But you wouldn't have write access to them later as a regular user. You can have setup.py generate .pyc files during install. You get the right .pyc files for whatever python you happen to have, and since you are running as root/admin during install you still have acess to the directories. Trying to build .pyc files later is a problem because a regular user doesn't have access to the directories.
So, best practice is to distribute .py files and have setup.py build the .pyc during install.
If you simply want to run your Python script, all you really need is .pyc which is the bytecode generated from your source code. See here for details on running a .pyc file. I will warn that some of the detials are bit twisty.
However I recommend including your source code and leaving out your .pyc files as they are generated automatically by the Python Interpreter. Besides, if you, or another person would want to revise/revisit your source code at a later point, you would need the .py files. Furthermore, it is usually best practice to just include your source code.
After 3 intensive hour, I was testing my script on terminal. However, my editor messed up and it overwrote my script when it was being still executed on terminal. Well, I didn't terminate running script, so I was wondering that does python interpreter keep the currently running file in a temporary folder or somewhere else so that I can recover my script?
Python tries to cache your .pyc files. How that's done has changed over time (see PEP 3147 -- PYC Repository Directories. Top level scripts are not cached but imported ones are. So, you may not have one.
.pyc files are compiled byte codes so its not just a question of renaming it .py and you can't figure them out just by looking at them. There are decompilers out there like the one refenced here:
Decompiling .pyc files.
Personally, I create mercurial repos for my scripts and check them in frequently.... because I've made a similar mistake a time or two. git, svn, and etc... are other popular tools for maintaining repos.
Depending on your operating system and editor, you may have a copy in Trash or even saved by the editor. You may also be able to "roll back" the file system.
If you're running Linux, you may still be able to find a handle to open files in the /proc/ directory if the process is still running. This handle will keep the file from being deleted. Details see: https://superuser.com/questions/283102/how-to-recover-deleted-file-if-it-is-still-opened-by-some-process
Say I run a Python (2.7, though I'm not sure that makes a difference here) script. Instead of terminating the script, I tab out, or somehow switch back to my editing environment. I can then modify the script and save it, but this changes nothing in the still-running script.
Does Python load all source files into memory completely at launch? I am under the impression that this is how the Python interpreter works, but this contradicts my other views of the Python interpreter: I have heard that .pyc files serve as byte-code for Python's virtual machine, like .class files in Java. At the same time however, some (very few in my understanding) implementations of Python also use just-in-time compilation techniques.
So am I correct in thinking that if I make a change to a .py file while my script is running, I don't see that change until I re-run the script, because at launch all necessary .py files are compiled into .pyc files, and simply modifying the .py files does not remake the .pyc files?
If that is correct, then why don't huge programs, like the one I'm working on with ~6,550 kilobytes of source code distributed over 20+ .py files, take forever to compile at startup? How is the program itself so fast?
Additional Info:
I am not using third-party modules. All of the files have been written locally. The main source file is relatively small (10 kB), but the source file I primarily work on is 65 kB. It was also written locally and changes every time before launch.
Python loads the main script into memory, compiles it into bytecode and runs that. If you modify the source file in the meantime, you're not affecting the bytecode.
If you're running the script as the main script (i. e. by calling it like python myfile.py, then the bytecode will be discarded when the script exits.
If you're importing the script, however, then the bytecode will be written to disk as a .pyc file which won't be recompiled when imported again, unless you modify the corresponding .py file.
Your big 6.5 MB program consists of many modules which are imported by the (probably small) main script, so only that will have to be compiled at each run. All the other files will have their .pyc file ready to run.
First of all, you are indeed correct in your understanding that changes to a Python source file aren't seen by the interpreter until the next run. There are some debugging systems, usually built for proprietary purposes, that allow you to reload modules, but this bring attendant complexities such as existing objects retaining references to code from the old module, for example. It can get really ugly, though.
The reason huge programs start up so quickly is the the interpreter tries to create a .pyc file for every .py file it imports if either no corresponding .pyc file exists or if the .py is newer. The .pyc is indeed the program compiled into byte code, so it's relatively quick to load.
As far as JIT compilation goes you may be thinking of the PyPy implementation, which is written in Python and has backends in several different languages. It's increasingly being used in Python 2 shops where execution speed is important, but it's along way from the CPython that we all know and love.
I know the difference between a .py and a .pyc file. My question is not about how, but about why According to the docs:
A program doesn’t run any faster when it is read from a .pyc or .pyo
file than when it is read from a .py file; the only thing that’s
faster about .pyc or .pyo files is the speed with which they are
loaded.
.pyc files load imports faster. But after loading the 'running' part of .pyc files takes the same time as the 'running' part in .py files? Why is is this? I would expected that
bit code (.pyc) is closer to the Python Virtual Machine and thus runs faster
.py files are being compiled to .pyc before they are being executed. This takes an extra step and thus costs time.
My question: After the import part, Why does the running part of .pyc files doesn't speed up execution compared to .py files?
When you run a .py file, it is first compiled to bytecode, then executed. The loading of such a file is slower because for a .pyc, the compilation step has already been performed, but after loading, the same bytecode interpretation is done.
In pseudocode, the Python interpreter executes the following algorithm:
code = load(path)
if path.endswith(".py"):
code = compile(code)
run(code)
The way the programs are run is always the same. The compiled code is interpreted.
The way the programs are loaded differs. If there is a current pyc file, this is taken as the compiled version, so no compile step has to be taken before running the command. Otherwise the py file is read, the compiler has to compile it (which takes a little time) but then the compiled version in memory is interpreted just like in the other way.
I understand that ".pyc" files are compiled versions of the plain-text ".py" files, created at runtime to make programs run faster. However I have observed a few things:
Upon modification of "py" files, program behavior changes. This indicates that the "py" files are compiled or at least go though some sort of hashing process or compare time stamps in order to tell whether or not they should be re-compiled.
Upon deleting all ".pyc" files (rm *.pyc) sometimes program behavior will change. Which would indicate that they are not being compiled on update of ".py"s.
Questions:
How do they decide when to be compiled?
Is there a way to ensure that they have stricter checking during development?
The .pyc files are created (and possibly overwritten) only when that python file is imported by some other script. If the import is called, Python checks to see if the .pyc file's internal timestamp is not older than the corresponding .py file. If it is, it loads the .pyc; if it isn't or if the .pyc does not yet exist, Python compiles the .py file into a .pyc and loads it.
What do you mean by "stricter checking"?
.pyc files generated whenever the corresponding code elements are imported, and updated if the corresponding code files have been updated. If the .pyc files are deleted, they will be automatically regenerated. However, they are not automatically deleted when the corresponding code files are deleted.
This can cause some really fun bugs during file-level refactors.
First of all, you can end up pushing code that only works on your machine and on no one else's. If you have dangling references to files you deleted, these will still work locally if you don't manually delete the relevant .pyc files because .pyc files can be used in imports. This is compounded with the fact that a properly configured version control system will only push .py files to the central repository, not .pyc files, meaning that your code can pass the "import test" (does everything import okay) just fine and not work on anyone else's computer.
Second, you can have some pretty terrible bugs if you turn packages into modules. When you convert a package (a folder with an __init__.py file) into a module (a .py file), the .pyc files that once represented that package remain. In particular, the __init__.pyc remains. So, if you have the package foo with some code that doesn't matter, then later delete that package and create a file foo.py with some function def bar(): pass and run:
from foo import bar
you get:
ImportError: cannot import name bar
because python is still using the old .pyc files from the foo package, none of which define bar. This can be especially problematic on a web server, where totally functioning code can break because of .pyc files.
As a result of both of these reasons (and possibly others), your deployment code and testing code should delete .pyc files, such as with the following line of bash:
find . -name '*.pyc' -delete
Also, as of python 2.6, you can run python with the -B flag to not use .pyc files. See How to avoid .pyc files? for more details.
See also: How do I remove all .pyc files from a project?