As a special case, you can prefix names with a single underscore (e.g., _X) to prevent them from being copied out when a client imports a module’s names with a from * statement. This really is intended only to minimize namespace pollution; because from * copies out all names, the importer may get more than it’s bargained for (including names that overwrite names in the importer). Underscores aren’t “private” declarations: you can still see and change such names with other import forms, such as the import statement.

Alternatively, you can achieve a hiding effect similar to the _X naming convention by assigning a list of variable name strings to the variable _ _all_ _ at the top level of the module. For example:

_ _all_ _ = ["Error", "encode", "decode"]     # Export these only

When this feature is used, the from * statement will copy out only those names listed in the _ _all_ _ list. In effect, this is the converse of the _X convention: _ _all_ _ identifies names to be copied, while _X identifies names not to be copied. Python looks for an _ _all_ _ list in the module first; if one is not defined, from * copies all names without a single leading underscore.

Like the _X convention, the _ _all_ _ list has meaning only to the from * statement form, and does not amount to a privacy declaration. Module writers can use either trick to implement modules that are well behaved when used with from *. (See also the discussion of _ _all_ _ lists in package _ _init_ _.py files in Chapter 20; there, these lists declare submodules to be loaded for a from *.)

In fact, we’ve already seen a prime example in this book of an instance where the _ _name_ _ check could be useful. In the section on arguments in Chapter 16, we coded a script that computed the minimum value from the set of arguments sent in:

def minmax(test, *args):
    res = args[0]
    for arg in args[1:]:
        if test(arg, res):
            res = arg
    return res

def lessthan(x, y): return x < y
def grtrthan(x, y): return x > y

print minmax(lessthan, 4, 2, 1, 5, 6, 3)         # Self-test code
print minmax(grtrthan, 4, 2, 1, 5, 6, 3)

This script includes self-test code at the bottom, so we can test it without having to retype everything at the interactive command line each time we run it. The problem with the way it is currently coded, however, is that the output of the self-test call will appear every time this file is imported from another file to be used as a tool—not exactly a user-friendly feature! To improve it, we can wrap up the self-test call in a _ _name_ _ check, so that it will be launched only when the file is run as a top-level script, not when it is imported:

print 'I am:', _ _name_ _

def minmax(test, *args):
    res = args[0]
    for arg in args[1:]:
        if test(arg, res):
            res = arg
    return res

def lessthan(x, y): return x < y
def grtrthan(x, y): return x > y

if _ _name_ _ == '_ _main_ _':
    print minmax(lessthan, 4, 2, 1, 5, 6, 3)     # Self-test code
    print minmax(grtrthan, 4, 2, 1, 5, 6, 3)

We’re also printing the value of _ _name_ _ at the top here to trace its value. Python creates and assigns this usage-mode variable as soon as it starts loading a file. When we run this file as a top-level script, its name is set to _ _main_ _, so its self-test code kicks in automatically:

% python min.py
I am: _ _main_ _
1
6

But, if we import the file, its name is not _ _main_ _, so we must explicitly call the function to make it run:

>>> import min
I am: min
>>> min.minmax(min.lessthan, 's', 'p', 'a', 'm')
'a'

Again, regardless of whether this is used for testing, the net effect is that we get to use our code in two different roles—as a library module of tools, or as an executable program.

This feature is designed to allow scripts to resolve ambiguities that can arise when a same-named file appears in multiple places on the module search path. Consider the following package directory:

mypkg
    _ _init_ _.py
    main.py
    string.py

This defines a package named mypkg containing modules named mypkg.main and mypkg.string. Now, suppose that the main module tries to import a module named string. In Python 2.4 and earlier, Python will first look in the mypkg directory to perform a relative import. It will find and import the string.py file located there, assigning it to the name string in the mypkg.main module’s namespace.

It could be, though, that the intent of this import was to load the Python standard library’s string module instead. Unfortunately, in these versions of Python, there’s no straightforward way to ignore mypkg.string and look for the standard library’s string module located further to the right on the module search path. We cannot depend on any extra package directory structure above the standard library being present on every machine.

In other words, imports in packages can be ambiguous—within a package, it’s not clear whether an import spam statement refers to a module within or outside the package. More accurately, a local module or package can hide another hanging directly off of sys.path, whether intentionally or not.

In practice, Python users can avoid reusing the names of standard library modules they need for modules of their own (if you need the standard string, don’t name a new module string). But this doesn’t help if a package accidentally hides a standard module; moreover, Python might add a new standard library module in the future that has the same name as a module of your own. Code that relies on relative imports is also less easy to understand because the reader may be confused about which module is intended to be used. It’s better if the resolution can be made explicit in code.

In Python 2.5, we can control the behavior of imports, forcing them to be absolute by using the from _ _future_ _ import directive listed earlier. Again, bear in mind that this absolute-import behavior will become the default in a future version (planned currently for Python 2.7). When absolute imports are enabled, a statement of the following form in our example file mypkg/main.py will always find the standard library’s version of string, via an absolute import search:

import string                          # Imports standard lib string

You should get used to using absolute imports now, so you’re prepared when the change takes effect. That is, if you really want to import a module from your package, to make this explicit and absolute, you should begin writing statements like this in your code (mypkg will be found in an absolute directory on sys.path):

from mypkg import string               # Imports mypkg.string (absolute)

Relative imports are still possible by using the dot pattern in the from statement:

from . import string                   # Imports mypkg.string (relative)

This form imports the string module relative to the current package, and is the relative equivalent to the prior example’s absolute form (the package’s directory is automatically searched first).

We can also copy specific names from a module with relative syntax:

from .string import name1, name2       # Imports names from mypkg.string

This statement again refers to the string module relative to the current package. If this code appears in our mypkg.main module, for example, it will import name1 and name2 from mypkg.string. An additional leading dot performs the relative import starting from the parent of the current package. For example:

from .. import spam                    # Imports a sibling of mypkg

will load a sibling of mypkg—i.e., the spam module located in the parent directory, next to mypkg. More generally, code located in some module A.B.C can do any of these:

from . import D                        # Imports A.B.D
from .. import E                       # Imports A.E
from ..F import G                      # Imports A.F.G

Relative import syntax and the proposed absolute-by-default imports change are advanced concepts, and these features are still only partially present in Python 2.5. Because of that, we’ll omit further details here; see Python’s standard manual set for more information.

Because modules expose most of their interesting properties as built-in attributes, it’s easy to write programs that manage other programs. We usually call such manager programs metaprograms because they work on top of other systems. This is also referred to as introspection because programs can see and process object internals. Introspection is an advanced feature, but it can be useful for building programming tools.

For instance, to get to an attribute called name in a module called M, we can use qualification, or index the module’s attribute dictionary (exposed in the built-in _ _dict_ _ attribute). Python also exports the list of all loaded modules as the sys.modules dictionary (that is, the modules attribute of the sys module) and provides a built-in called getattr that lets us fetch attributes from their string names (it’s like saying object.attr, but attr is a runtime string). Because of that, all the following expressions reach the same attribute and object:

M.name                            # Qualify object
M._ _dict_ _['name']                 # Index namespace dictionary manually
sys.modules['M'].name            # Index loaded-modules table manually
getattr(M, 'name')               # Call built-in fetch function

By exposing module internals like this, Python helps you build programs about programs.^[52] For example, here is a module named mydir.py that puts these ideas to work to implement a customized version of the built-in dir function. It defines and exports a function called listing, which takes a module object as an argument, and prints a formatted listing of the module’s namespace:

# A module that lists the namespaces of other modules

verbose = 1

def listing(module):
    if verbose:
        print "-"*30
        print "name:", module.__name__, "file:", module._ _file_ _
        print "-"*30

    count = 0
    for attr in module._ _dict_ _.keys(  ):       # Scan namespace
        print "%02d) %s" % (count, attr),
        if attr[0:2] == "_ _":
            print "<built-in name>"           # Skip _ _file_ _, etc.
        else:
            print getattr(module, attr)       # Same as ._ _dict_ _[attr]
        count = count+1

    if verbose:
        print "-"*30
        print module._ _name_ _, "has %d names" % count
        print "-"*30

if __name__ == "_ _main_ _":
    import mydir
    listing(mydir)                            # Self-test code: list myself

We’ve also provided self-test logic at the bottom of this module, which narcissistically imports and lists itself. Here’s the sort of output produced:

C:python> python mydir.py
------------------------------
name: mydir file: mydir.py
------------------------------
00) _ _file_ _ <built-in name>
01) _ _name_ _ <built-in name>
02) listing <function listing at 885450>
03) _ _doc_ _ <built-in name>
04) _ _builtins_ _ <built-in name>
05) verbose 1
------------------------------
mydir has 6 names
------------------------------

We’ll meet getattr and its relatives again later. The point to notice here is that mydir is a program that lets you browse other programs. Because Python exposes its internals, you can process objects generically.^[53]

When a module is first imported (or reloaded), Python executes its statements one by one, from the top of the file to the bottom. This has a few subtle implications regarding forward references that are worth underscoring here:

Generally, forward references are only a concern in top-level module code that executes immediately; functions can reference names arbitrarily. Here’s an example that illustrates forward reference:

func1(  )                           # Error: "func1" not yet assigned

def func1(  ):
    print func2(  )                  # OK:  "func2" looked up later

func1(  )                           # Error: "func2" not yet assigned

def func2(  ):
    return "Hello"

func1(  )                           # Okay:  "func1" and "func2" assigned

When this file is imported (or run as a standalone program), Python executes its statements from top to bottom. The first call to func1 fails because the func1 def hasn’t run yet. The call to func2 inside func1 works as long as func2’s def has been reached by the time func1 is called (it hasn’t when the second top-level func1 call is run). The last call to func1 at the bottom of the file works because func1 and func2 have both been assigned.

Mixing defs with top-level code is not only hard to read, it’s dependent on statement ordering. As a rule of thumb, if you need to mix immediate code with defs, put your defs at the top of the file, and top-level code at the bottom. That way, your functions are guaranteed to be defined and assigned by the time code that uses them runs.

The module name in an import or from statement is a hardcoded variable name. Sometimes, though, your program will get the name of a module to be imported as a string at runtime (e.g., if a user selects a module name from within a GUI). Unfortunately, you can’t use import statements directly to load a module given its name as a string—Python expects a variable name here, not a string. For instance:

>>> import "string"
  File "<stdin>", line 1
    import "string"
                  ^
SyntaxError: invalid syntax

It also won’t work to simply assign the string to a variable name:

x = "string"
import x

Here, Python will try to import a file x.py, not the string module.

To get around this, you need to use special tools to load a module dynamically from a string that is generated at runtime. The most general approach is to construct an import statement as a string of Python code, and pass it to the exec statement to run:

>>> modname = "string"
>>> exec "import " + modname# Run a string of code
>>> string# Imported in this namespace
<module 'string'>

The exec statement (and its cousin for expressions, the eval function) compiles a string of code, and passes it to the Python interpreter to be executed. In Python, the byte code compiler is available at runtime, so you can write programs that construct and run other programs like this. By default, exec runs the code in the current scope, but you can get more specific by passing in optional namespace dictionaries.

The only real drawback to exec is that it must compile the import statement each time it runs; if it runs many times, your code may run quicker if it uses the built-in _ _import_ _ function to load from a name string instead. The effect is similar, but _ _import_ _ returns the module object, so assign it to a name here to keep it:

>>> modname = "string"
>>> string = _ _import_ _(modname)
>>> string
<module 'string'>

Although it’s commonly used, the from statement is the source of a variety of potential gotchas in Python. The from statement is really an assignment to names in the importer’s scope—a name-copy operation, not a name aliasing. The implications of this are the same as for all assignments in Python, but subtle, especially given that the code that shares the objects lives in different files. For instance, suppose we define the following module (nested1.py):

X = 99
def printer(  ): print X

If we import its two names using from in another module (nested2.py), we get copies of those names, not links to them. Changing a name in the importer resets only the binding of the local version of that name, not the name in nested1.py:

from nested1 import X, printer    # Copy names out
X = 88                            # Changes my "X" only!
printer(  )                             # nested1's X is still 99
% python nested2.py
99

If we use import to get the whole module, and then assign to a qualified name, however, we change the name in nested1.py. Qualification directs Python to a name in the module object, rather than a name in the importer (nested3.py):

import nested1                    # Get module as a whole
nested1.X = 88                    # OK: change nested1's X
nested1.printer(  )

% python nested3.py
88

I mentioned this in Chapter 19, but saved the details for here. Because you don’t list the variables you want when using the from module import * statement form, it can accidentally overwrite names you’re already using in your scope. Worse, it can make it difficult to determine where a variable comes from. This is especially true if the from * form is used on more than one imported file.

For example, if you use from * on three modules, you’ll have no way of knowing what a raw function call really means, short of searching all three external module files (all of which may be in other directories):

>>> from module1 import *# Bad: may overwrite my names silently
>>> from module2 import *# Worse: no way to tell what we get!
>>> from module3 import *
>>> . . .

>>> func(  )# Huh???

The solution again is not to do this: try to explicitly list the attributes you want in your from statements, and restrict the from * form to at most one imported module per file. That way, any undefined names must by deduction be in the module named in the single from *. You can avoid the issue altogether if you always use import instead of from, but that advice is too harsh; like much else in programming, from is a convenient tool if used wisely.

Here’s another from-related gotcha: as discussed previously, because from copies (assigns) names when run, there’s no link back to the module where the names came from. Names imported with from simply become references to objects, which happen to have been referenced by the same names in the importee when the from ran.

Because of this behavior, reloading the importee has no effect on clients that import its names using from. That is, the client’s names will still reference the original objects fetched with from, even if the names in the original module are later reset:

from module import X          # X may not reflect any module reloads!
 . . .
reload(module)                # Changes module, but not my names
X                             # Still references old object

To make reloads more effective, use import and name qualification instead of from. Because qualifications always go back to the module, they will find the new bindings of module names after reloading:

import module                 # Get module, not names
 . . .
reload(module)                # Changes module in-place
module.X                      # Get current X: reflects module reloads

Chapter 3 warned that it’s usually better not to launch programs with imports and reloads because of the complexities involved. Things get even worse when from is brought into the mix. Python beginners often encounter the gotcha described here. After opening a module file in a text edit window, say you launch an interactive session to load and test your module with from:

from module import function
function(1, 2, 3)

Finding a bug, you jump back to the edit window, make a change, and try to reload the module this way:

reload(module)

But this doesn’t work—the from statement assigned the name function, not module. To refer to the module in a reload, you have to first load it with an import statement at least once:

import module
reload(module)
function(1, 2, 3)

However, this doesn’t quite work either—reload updates the module object, but as discussed in the preceding section, names like function that were copied out of the module in the past still refer to the old objects (in this instance, the original version of the function). To really get the new function, you must call it module.function after the reload, or rerun the from:

import module
reload(module)
from module import function
function(1, 2, 3)

Now, the new version of the function will finally run.

As you can see, there are problems inherent in using reload with from: not only do you have to remember to reload after imports, but you also have to remember to rerun your from statements after reloads. This is complex enough to trip up even an expert once in a while.

You should not expect reload and from to play together nicely. The best policy is not to combine them at all—use reload with import, or launch your programs other ways, as suggested in Chapter 3 (e.g., using the Run → Run Module menu option in IDLE, file icon clicks, or system command lines).

When you reload a module, Python only reloads that particular module’s file; it doesn’t automatically reload modules that the file being reloaded happens to import. For example, if you reload some module A, and A imports modules B and C, the reload applies only to A, not to B and C. The statements inside A that import B and C are rerun during the reload, but they just fetch the already loaded B and C module objects (assuming they’ve been imported before). In actual code, here’s the file A.py:

import B                   # Not reloaded when A is
import C                   # Just an import of an already loaded module

% python
>>> . . .
>>> reload(A)

Don’t depend on transitive module reloads—instead, use multiple reload calls to update subcomponents independently. If desired, you can design your systems to reload their subcomponents automatically by adding reload calls in parent modules like A.

Better still, you could write a general tool to do transitive reloads automatically by scanning modules’ _ _dict_ _ attributes (see "Modules Are Objects: Metaprograms" earlier in this chapter) and checking each item’s type (see Chapter 9) to find nested modules to reload recursively. Such a utility function could call itself recursively to navigate arbitrarily shaped import dependency chains.

For example, the module reloadall.py listed below has a reload_all function that automatically reloads a module, every module that the module imports, and so on, all the way to the bottom of each import chain. It uses a dictionary to keep track of already reloaded modules; recursion to walk the import chains; and the standard library’s types module (introduced at the end of Chapter 9), which simply predefines type results for built-in types.

To use this utility, import its reload_all function, and pass it the name of an already loaded module (like you would the built-in reload function). When the file runs standalone, its self-test code will test itself—it has to import itself because its own name is not defined in the file without an import. I encourage you to study and experiment with this example on your own:

import types

def status(module):
    print 'reloading', module._ _name_ _

def transitive_reload(module, visited):
    if not visited.has_key(module):                   # Trap cycles, dups
        status(module)                                # Reload this module
        reload(module)                               # And visit children
        visited[module] = None
        for attrobj in module._ _dict_ _.values(  ):     # For all attrs
            if type(attrobj) == types.ModuleType:    # Recur if module
                transitive_reload(attrobj, visited)

def reload_all(*args):
    visited = {  }
    for arg in args:
        if type(arg) == types.ModuleType:
            transitive_reload(arg, visited)

if __name__ == '_ _main_ _':
    import reloadall                      # Test code: reload myself
    reload_all(reloadall)                 # Should reload this, types

I saved the most bizarre (and, thankfully, obscure) gotcha for last. Because imports execute a file’s statements from top to bottom, you need to be careful when using modules that import each other (known as recursive imports). Because the statements in a module may not all have been run when it imports another module, some of its names may not yet exist.

If you use import to fetch the module as a whole, this may or may not matter; the module’s names won’t be accessed until you later use qualification to fetch their values. But, if you use from to fetch specific names, you must bear in mind that you will only have access to names in that module that have already been assigned.

For instance, take the following modules, recur1 and recur2. recur1 assigns a name X, and then imports recur2 before assigning the name Y. At this point, recur2 can fetch recur1 as a whole with an import (it already exists in Python’s internal modules table), but if it uses from, it will be able to see only the name X; the name Y, which is assigned below the import in recur1, doesn’t yet exist, so you get an error:

# File: recur1.py
X = 1
import recur2                             # Run recur2 now if it doesn't exist
Y = 2

# File: recur2.py
from recur1 import X                      # OK: "X" already assigned
from recur1 import Y                      # Error: "Y" not yet assigned
>>> import recur1
Traceback (innermost last):
  File "<stdin>", line 1, in ?
  File "recur1.py", line 2, in ?
    import recur2
  File "recur2.py", line 2, in ?
    from recur1 import Y                  # Error: "Y" not yet assigned
ImportError: cannot import name Y

Python avoids rerunning recur1’s statements when they are imported recursively from recur2 (or else the imports would send the script into an infinite loop), but recur1’s namespace is incomplete when imported by recur2.

The solution? Don’t use from in recursive imports (no, really!). Python won’t get stuck in a cycle if you do, but your programs will once again be dependent on the order of the statements in the modules.

There are two ways out of this gotcha:

See "Part V, Modules" in Appendix B for the solutions.