Develop Reference

Why does the from ... import ... statement contain an implicit import?

Given a package:
package/
├── __init__.py
└── module.py
__init__.py:
from .module import function
module.py:
def function():
pass
One can import the package and print its namespace.
python -c 'import package; print(dir(package))'
['__builtins__', ..., 'function', 'module']
Question:
Why does the namespace of package contain module when only function was imported in the __init__.py?
I would have expected that the package's namespace would only contain function and not module. This mechanism is also mentioned in the Documentation,
"When a submodule is loaded using any mechanism (e.g. importlib APIs,
the import or import-from statements, or built-in __import__()) a
binding is placed in the parent module’s namespace to the submodule
object."
but is not really motivated. For me this choice seems odd, as I think of sub-modules as implementation detail to structure packages and do not expect them to be part of the API as the structure can change.
Also I know "Python is for consenting adults" and one cannot truly hide anything from a user. But I would argue, that binding the sub-modules names to the package's scopes makes it less obvious to a user what is actually part of the API and what can change.
Why no use a __sub_modules__ attribute or so to make sub-modules accessible to a user? What is the reason for this design decision?

You say you think of submodules as implementation details. This is not the design intent behind submodules; they can be, and extremely commonly are, part of the public interface of a package. The import system was designed to facilitate access to submodules, not to prevent access.
Loading a submodule places a binding into the parent's namespace because this is necessary for access to the module. For example, after the following code:
import package.submodule
the expression package.submodule must evaluate to the module object for the submodule. package evaluates to the module object for the package, so this module object must have a submodule attribute referring to the module object for the submodule.
At this point, you are almost certainly thinking, "hey, there's no reason from .submodule import function has to do the same thing!" It does the same thing because this attribute binding is part of submodule initialization, which only happens on the first import, and which needs to do the same setup regardless of what kind of import triggered it.
This is not an extremely strong reason. With enough changes and rejiggering, the import system definitely could have been designed the way you expect. It was not designed that way because the designers had different priorities than you. Python's design cares very little about hiding things or supporting any notion of privacy.

you have to understand that Python is a runtime language. def, class and import are all executable statements, that will, when executed, create (respectively) a function, class or module object and bind them in the current namespace.
wrt/ modules (packages being modules too - at least at runtime), the very first time a module is imported (directly or indirectly) for a given process, the matching .py (well, usually it's compiled .pyc version) is executed (all statements at the top level are executed in order), and the resulting namespace will be used to populate the module instance. Only once this has been done can any name defined in the module be accessed (you cannot access something that doesn't exist yet, can you ?). Then the module object is cached in sys.modules for subsequent imports. In this process, a when a sub-module is loaded, it's considered as an attribute of it's parent module.
For me this choice seems odd, as I think of sub-modules as implementation detail to structure packages and do not expect them to be part of the API as the structure can change
Actually, Python's designers considered things the other way round: a "package" (note that there's no 'package' type at runtime) is mostly a convenience to organize a collection of related modules - IOW, the ̀moduleis the real building block - and as a matter of fact, at runtime, when what you import is technically a "package", it still materializes as amodule` object.
Now wrt/ the "do not expect them to be part of the API as the structure can change", this has of course been taken into account. It's actually a quite common pattern to start out with a single module, and then turn it into a package as the code base grows - without impacting client code, of course. The key here is to make proper use of your package's initializer - the __init__.py file - which is actually what your package's module instance is built from. This lets the package act as a "facade", masking the "implementation details" of which submodule effectively defines which function, class or whatever.
So the solution here is plain simply to, in your package's __init__.py, 1/ import the names you want to make public (so the client code can import directly from your package instead of having to go thru the submodule) and 2/ define the __all__ attributes with the names that should be considered public so the interface is clearly documented.
FWIW, this last operation should be done for all your submodules too, and you can also use the _single_leading_underscore naming convention for things that are really really "implementation details".
None of this will of course prevent anyone to import even "private" names directly from your submodules, but then they are on their own when something breaks ("we are all consenting adults" etc).

python workaround for circular import

Ok so it is like this.
I'd rather not give away my code but if you really need it I will. I have two modules that need a bit from each other. the modules are called webhandler and datahandler.
In webhandler I have a line:
import datahandler
and in datahandler I have another line:
import webhandler
Now I know this is terrible code and a circular import like this causes the code to run twice (which is what im trying to avoid).
However the datahandler module needs to access several functions from the webhandler module, and the webhandler module needs access to several variables that are generated in the datahandler module. I dont see any workaround other than moving functions to different modules but that would ruin the organisation of my program and make no logical sense with the module naming.
Any help?

Circular dependencies are a form of code smell. If you have two modules that depend on each other, then that’s a very bad sign, and you should restructure your code.
There are a few different ways to do this; which one is best depends on what you are doing, and what parts of each module are actually used by another.
A very simple solution would be to just merge both modules, so you only have a single module that only depends on itself, or rather on its own contents. This is simple, but since you had separated modules before, it’s likely that you are introducing new problems that way because you no longer have a separation of concerns.
Another solution would be to make sure that the dependencies are actually required. If there are only a few parts of a module that depend on the other, maybe you could move those bits around in a way that the circular dependency is no longer required, or utilize the way imports work to make the circular dependencies no longer a problem.
The better solution would probably be to move the dependencies into a separate new module. If naming is really the hardest problem about that, then you’re probably doing it right. It might “ruin the organisation of [your] program” but since you have circular dependencies, there is something inherently wrong with your setup anyway.

What others have said about not doing circular imports is the best solution, but if you end up absolutely needing them (possibly for backwards compatibility or clarity of code), it's usually within just one method or function of one of the modules. Thus you can safely do this:
# modA.py
import modB
# modB.py
def functionDependingOnA():
import modA
...
There's a slight overhead to doing the import each time the function is called, but it is rather low unless it's called all the time. (about 400ns in my testing).
You could also do like this to avoid even that lookup:
# modA -- same as above.
# modB.py
_imports = {}
def _importA():
import modA
_imports['modA'] = modA
return modA
def functionDependingOnA():
modA = _imports.get('modA') or _importA()
This version only added 40ns of time on the second and subsequent calls, or about the same amount of time as an empty local function call.

SqlAlchemy uses the Dependency Injection pattern, where the required module is passed to a function by a decorator:
#util.dependencies("sqlalchemy.orm.util")
def identity_key(cls, orm_util, *args, **kwargs):
return orm_util.identity_key(*args, **kwargs)
This approach is basically the same as doing an import inside a function, but has slightly better performance.

the webhandler module needs access to several variables that are generated in the datahandler module
It might make sense to push any "generated" data to a third location. So datahandler functions call config.setvar( name, value ) when appropriate and webhandler functions call config.getvar( name ) when they need to. config would be a third sub-module, containing simple setvar and getvar functions that you write (wrappers around setting/getting elements of a global dictionary would be the simplest approach).
Then the datahandler code would import webhandler, config but webhandler would only need to import config.
I agree with poke however, that the need for such a question betrays the fact that you probably haven't yet got the design finalized as neatly and logically as you thought. If it were me, I would re-think the way modules are divided up.

What happens when you import a package?

For efficiency's sake I am trying to figure out how python works with its heap of objects (and system of namespaces, but it is more or less clear). So, basically, I am trying to understand when objects are loaded into the heap, how many of them are there, how long they live etc.
And my question is when I work with a package and import something from it:
from pypackage import pymodule
what objects get loaded into the memory (into the object heap of the python interpreter)? And more generally: what happens? :)
I guess the above example does something like:
some object of the package pypackage was created in the memory (which contains some information about the package but not too much), the module pymodule was loaded into the memory and its reference was created in the local name space. The important thing here is: no other modules of the pypackage (or other objects) were created in the memory, unless it is stated explicitly (in the module itself, or somewhere in the package initialization tricks and hooks, which I am not familiar with). At the end the only one big thing in the memory is pymodule (i.e. all the objects that were created when the module was imported). Is it so? I would appreciate if someone clarified this matter a little bit. Maybe you could advice some useful article about it? (documentation covers more particular things)
I have found the following to the same question about the modules import:
When Python imports a module, it first checks the module registry (sys.modules) to see if the module is already imported. If that’s the case, Python uses the existing module object as is.
Otherwise, Python does something like this:
Create a new, empty module object (this is essentially a dictionary)
Insert that module object in the sys.modules dictionary
Load the module code object (if necessary, compile the module first)
Execute the module code object in the new module’s namespace. All variables assigned by the code will be available via the module object.
And would be grateful for the same kind of explanation about packages.
By the way, with packages a module name is added into the sys.modules oddly:
>>> import sys
>>> from pypacket import pymodule
>>> "pymodule" in sys.modules.keys()
False
>>> "pypacket" in sys.modules.keys()
True
And also there is a practical question concerning the same matter.
When I build a set of tools, which might be used in different processes and programs. And I put them in modules. I have no choice but to load a full module even when all I want is to use only one function declared there. As I see one can make this problem less painful by making small modules and putting them into a package (if a package doesn't load all of its modules when you import only one of them).
Is there a better way to make such libraries in Python? (With the mere functions, which don't have any dependencies within their module.) Is it possible with C-extensions?
PS sorry for such a long question.

You have a few different questions here. . .
About importing packages
When you import a package, the sequence of steps is the same as when you import a module. The only difference is that the packages's code (i.e., the code that creates the "module code object") is the code of the package's __init__.py.
So yes, the sub-modules of the package are not loaded unless the __init__.py does so explicitly. If you do from package import module, only module is loaded, unless of course it imports other modules from the package.
sys.modules names of modules loaded from packages
When you import a module from a package, the name is that is added to sys.modules is the "qualified name" that specifies the module name together with the dot-separated names of any packages you imported it from. So if you do from package.subpackage import mod, what is added to sys.modules is "package.subpackage.mod".
Importing only part of a module
It is usually not a big concern to have to import the whole module instead of just one function. You say it is "painful" but in practice it almost never is.
If, as you say, the functions have no external dependencies, then they are just pure Python and loading them will not take much time. Usually, if importing a module takes a long time, it's because it loads other modules, which means it does have external dependencies and you have to load the whole thing.
If your module has expensive operations that happen on module import (i.e., they are global module-level code and not inside a function), but aren't essential for use of all functions in the module, then you could, if you like, redesign your module to defer that loading until later. That is, if your module does something like:
def simpleFunction():
pass
# open files, read huge amounts of data, do slow stuff here
you can change it to
def simpleFunction():
pass
def loadData():
# open files, read huge amounts of data, do slow stuff here
and then tell people "call someModule.loadData() when you want to load the data". Or, as you suggested, you could put the expensive parts of the module into their own separate module within a package.
I've never found it to be the case that importing a module caused a meaningful performance impact unless the module was already large enough that it could reasonably be broken down into smaller modules. Making tons of tiny modules that each contain one function is unlikely to gain you anything except maintenance headaches from having to keep track of all those files. Do you actually have a specific situation where this makes a difference for you?
Also, regarding your last point, as far as I'm aware, the same all-or-nothing load strategy applies to C extension modules as for pure Python modules. Obviously, just like with Python modules, you could split things up into smaller extension modules, but you can't do from someExtensionModule import someFunction without also running the rest of the code that was packaged as part of that extension module.

The approximate sequence of steps that occurs when a module is imported is as follows:
Python tries to locate the module in sys.modules and does nothing else if it is found. Packages are keyed by their full name, so while pymodule is missing from sys.modules, pypacket.pymodule will be there (and can be obtained as sys.modules["pypacket.pymodule"].
Python locates the file that implements the module. If the module is part of the package, as determined by the x.y syntax, it will look for directories named x that contain both an __init__.py and y.py (or further subpackages). The bottom-most file located will be either a .py file, a .pyc file, or a .so/.pyd file. If no file that fits the module is found, an ImportError will be raised.
An empty module object is created, and the code in the module is executed with the module's __dict__ as the execution namespace.1
The module object is placed in sys.modules, and injected into the importer's namespace.
Step 3 is the point at which "objects get loaded into memory": the objects in question are the module object, and the contents of the namespace contained in its __dict__. This dict typically contains top-level functions and classes created as a side effect of executing all the def, class, and other top-level statements normally contained in each module.
Note that the above only desribes the default implementation of import. There is a number of ways one can customize import behavior, for example by overriding the __import__ built-in or by implementing import hooks.
1 If the module file is a .py source file, it will be compiled into memory first, and the code objects resulting from the compilation will be executed. If it is a .pyc, the code objects will be obtained by deserializing the file contents. If the module is a .so or a .pyd shared library, it will be loaded using the operating system's shared-library loading facility, and the init<module> C function will be invoked to initialize the module.

Automatically import to all Python files in the given folder?

I am relatively quite new to Python and I try to learn the "Pythonic" way of doing things to build a solid foundation in terms of Python development. Perhaps what I want to achieve is not Python at all, but I am nonetheless seeking to find out the "right" way to solve this issue.
I am building an application, for which I am creating modules. I just noticed that a module of mine has 7 different .py Python files, all importing 3 different same things. So all these files share these imports.
I tried removing them, and inserting these import to the empty init.py in the folder, but it did not do the trick.
If possible, since these imports are needed by all these module files, I would not like them to be imported in each file one by one.
What can I do to perform the common import?
Thank you very much, I really appreciate your kind help.

As the Zen of Python states, "Explicit is better than implicit", and this is a good example.
It's very useful to have the dependencies of a module listed explicitly in the imports and it means that every symbol in a file can be traced to its origin with a simple text search. E.g. if you search for some_identifier in your file, you'll either find a definition in the file, or from some_module import some_identifier. It's even more obvious with direct references to some_module.some_identifier. (This is also one reason why you should not do from module import *.)
One thing you could do, without losing the above property, is to import your three shared modules into a fourth module:
#fourth.py
import first
import second
import third
then...
#another.py
import fourth
fourth.first.some_function()
#etc.
If you can't stomach that (it does make calls more verbose, after all) then the duplication of three imports is fine, really.

I agree with DrewV, it is perfectly pythonic to do
File1:
import xyz
import abc
...
File2:
import xyz
An almost identical question has also been addressed in the following link:
python multiple imports for a common module
As it explains, Python does the job of optimising the module load, so you can write multiple import statements and not worry about performance losses, because the module is only loaded once. In fact, listing out all the imports in each file makes it explicitly clear what each file depends on.
And for a discussion of how imports interact with namespaces, see:
Python imports across modules and global variables

What are the advantages and disadvantages of the require vs. import methods of loading code?

Ruby uses require, Python uses import. They're substantially different models, and while I'm more used to the require model, I can see a few places where I think I like import more. I'm curious what things people find particularly easy — or more interestingly, harder than they should be — with each of these models.
In particular, if you were writing a new programming language, how would you design a code-loading mechanism? Which "pros" and "cons" would weigh most heavily on your design choice?

The Python import has a major feature in that it ties two things together -- how to find the import and under what namespace to include it.
This creates very explicit code:
import xml.sax
This specifies where to find the code we want to use, by the rules of the Python search path.
At the same time, all objects that we want to access live under this exact namespace, for example xml.sax.ContentHandler.
I regard this as an advantage to Ruby's require. require 'xml' might in fact make objects inside the namespace XML or any other namespace available in the module, without this being directly evident from the require line.
If xml.sax.ContentHandler is too long, you may specify a different name when importing:
import xml.sax as X
And it is now avalable under X.ContentHandler.
This way Python requires you to explicitly build the namespace of each module. Python namespaces are thus very "physical", and I'll explain what I mean:
By default, only names directly defined in the module are available in its namespace: functions, classes and so.
To add to a module's namespace, you explicitly import the names you wish to add, placing them (by reference) "physically" in the current module.
For example, if we have the small Python package "process" with internal submodules machine and interface, and we wish to present this as one convenient namespace directly under the package name, this is and example of what we could write in the "package definition" file process/__init__.py:
from process.interface import *
from process.machine import Machine, HelperMachine
Thus we lift up what would normally be accessible as process.machine.Machine up to process.Machine. And we add all names from process.interface to process namespace, in a very explicit fashion.
The advantages of Python's import that I wrote about were simply two:
Clear what you include when using import
Explicit how you modify your own module's namespace (for the program or for others to import)

A nice property of require is that it is actually a method defined in Kernel. Thus you can override it and implement your own packaging system for Ruby, which is what e.g. Rubygems does!
PS: I am not selling monkey patching here, but the fact that Ruby's package system can be rewritten by the user (even to work like python's system). When you write a new programming language, you cannot get everything right. Thus if your import mechanism is fully extensible (into totally all directions) from within the language, you do your future users the best service. A language that is not fully extensible from within itself is an evolutionary dead-end. I'd say this is one of the things Matz got right with Ruby.

Python's import provides a very explicit kind of namespace: the namespace is the path, you don't have to look into files to know what namespace they do their definitions in, and your file is not cluttered with namespace definitions. This makes the namespace scheme of an application simple and fast to understand (just look at the source tree), and avoids simple mistakes like mistyping a namespace declaration.
A nice side effect is every file has its own private namespace, so you don't have to worry about conflicts when naming things.
Sometimes namespaces can get annoying too, having things like some.module.far.far.away.TheClass() everywhere can quickly make your code very long and boring to type. In these cases you can import ... from ... and inject bits of another namespace in the current one. If the injection causes a conflict with the module you are importing in, you can simply rename the thing you imported: from some.other.module import Bar as BarFromOtherModule.
Python is still vulnerable to problems like circular imports, but it's the application design more than the language that has to be blamed in these cases.
So python took C++ namespace and #include and largely extended on it. On the other hand I don't see in which way ruby's module and require add anything new to these, and you have the exact same horrible problems like global namespace cluttering.

Disclaimer, I am by no means a Python expert.
The biggest advantage I see to require over import is simply that you don't have to worry about understanding the mapping between namespaces and file paths. It's obvious: it's just a standard file path.
I really like the emphasis on namespacing that import has, but can't help but wonder if this particular approach isn't too inflexible. As far as I can tell, the only means of controlling a module's naming in Python is by altering the filename of the module being imported or using an as rename. Additionally, with explicit namespacing, you have a means by which you can refer to something by its fully-qualified identifier, but with implicit namespacing, you have no means to do this inside the module itself, and that can lead to potential ambiguities that are difficult to resolve without renaming.
i.e., in foo.py:
class Bar:
def myself(self):
return foo.Bar
This fails with:
Traceback (most recent call last):
File "", line 1, in ?
File "foo.py", line 3, in myself
return foo.Bar
NameError: global name 'foo' is not defined
Both implementations use a list of locations to search from, which strikes me as a critically important component, regardless of the model you choose.
What if a code-loading mechanism like require was used, but the language simply didn't have a global namespace? i.e., everything, everywhere must be namespaced, but the developer has full control over which namespace the class is defined in, and that namespace declaration occurs explicitly in the code rather than via the filename. Alternatively, defining something in the global namespace generates a warning. Is that a best-of-both-worlds approach, or is there an obvious downside to it that I'm missing?