Contents
id() appear to be not unique?Yes.
The pdb module is a simple but adequate console-mode debugger for Python. It is
part of the standard Python library, and is documented in the Library
Reference Manual. You can also write your own debugger by using the code
for pdb as an example.
The IDLE interactive development environment, which is part of the standard Python distribution (normally available as Tools/scripts/idle), includes a graphical debugger.
PythonWin is a Python IDE that includes a GUI debugger based on pdb. The Pythonwin debugger colors breakpoints and has quite a few cool features such as debugging non-Pythonwin programs. Pythonwin is available as part of the Python for Windows Extensions project and as a part of the ActivePython distribution (see https://www.activestate.com/activepython).
Boa Constructor is an IDE and GUI builder that uses wxWidgets. It offers visual frame creation and manipulation, an object inspector, many views on the source like object browsers, inheritance hierarchies, doc string generated html documentation, an advanced debugger, integrated help, and Zope support.
Eric is an IDE built on PyQt and the Scintilla editing component.
Pydb is a version of the standard Python debugger pdb, modified for use with DDD (Data Display Debugger), a popular graphical debugger front end. Pydb can be found at http://bashdb.sourceforge.net/pydb/ and DDD can be found at https://www.gnu.org/software/ddd.
There are a number of commercial Python IDEs that include graphical debuggers. They include:
Yes.
PyChecker is a static analysis tool that finds bugs in Python source code and warns about code complexity and style. You can get PyChecker from http://pychecker.sourceforge.net/.
Pylint is another tool that checks if a module satisfies a coding standard, and also makes it possible to write plug-ins to add a custom feature. In addition to the bug checking that PyChecker performs, Pylint offers some additional features such as checking line length, whether variable names are well-formed according to your coding standard, whether declared interfaces are fully implemented, and more. https://docs.pylint.org/ provides a full list of Pylint’s features.
You don’t need the ability to compile Python to C code if all you want is a stand-alone program that users can download and run without having to install the Python distribution first. There are a number of tools that determine the set of modules required by a program and bind these modules together with a Python binary to produce a single executable.
One is to use the freeze tool, which is included in the Python source tree as
Tools/freeze. It converts Python byte code to C arrays; a C compiler you can
embed all your modules into a new program, which is then linked with the
standard Python modules.
It works by scanning your source recursively for import statements (in both forms) and looking for the modules in the standard Python path as well as in the source directory (for built-in modules). It then turns the bytecode for modules written in Python into C code (array initializers that can be turned into code objects using the marshal module) and creates a custom-made config file that only contains those built-in modules which are actually used in the program. It then compiles the generated C code and links it with the rest of the Python interpreter to form a self-contained binary which acts exactly like your script.
Obviously, freeze requires a C compiler. There are several other utilities which don’t. One is Thomas Heller’s py2exe (Windows only) at
Another tool is Anthony Tuininga’s cx_Freeze.
Yes. The coding style required for standard library modules is documented as PEP 8.
It can be a surprise to get the UnboundLocalError in previously working code when it is modified by adding an assignment statement somewhere in the body of a function.
This code:
>>> x = 10
>>> def bar():
... print(x)
>>> bar()
10
works, but this code:
>>> x = 10
>>> def foo():
... print(x)
... x += 1
results in an UnboundLocalError:
>>> foo()
Traceback (most recent call last):
...
UnboundLocalError: local variable 'x' referenced before assignment
This is because when you make an assignment to a variable in a scope, that
variable becomes local to that scope and shadows any similarly named variable
in the outer scope. Since the last statement in foo assigns a new value to
x, the compiler recognizes it as a local variable. Consequently when the
earlier print(x) attempts to print the uninitialized local variable and
an error results.
In the example above you can access the outer scope variable by declaring it global:
>>> x = 10
>>> def foobar():
... global x
... print(x)
... x += 1
>>> foobar()
10
This explicit declaration is required in order to remind you that (unlike the superficially analogous situation with class and instance variables) you are actually modifying the value of the variable in the outer scope:
>>> print(x)
11
You can do a similar thing in a nested scope using the nonlocal
keyword:
>>> def foo():
... x = 10
... def bar():
... nonlocal x
... print(x)
... x += 1
... bar()
... print(x)
>>> foo()
10
11
In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a value anywhere within the function’s body, it’s assumed to be a local unless explicitly declared as global.
Though a bit surprising at first, a moment’s consideration explains this. On
one hand, requiring global for assigned variables provides a bar
against unintended side-effects. On the other hand, if global was required
for all global references, you’d be using global all the time. You’d have
to declare as global every reference to a built-in function or to a component of
an imported module. This clutter would defeat the usefulness of the global
declaration for identifying side-effects.
Assume you use a for loop to define a few different lambdas (or even plain functions), e.g.:
>>> squares = []
>>> for x in range(5):
... squares.append(lambda: x**2)
This gives you a list that contains 5 lambdas that calculate x**2. You
might expect that, when called, they would return, respectively, 0, 1,
4, 9, and 16. However, when you actually try you will see that
they all return 16:
>>> squares[2]()
16
>>> squares[4]()
16
This happens because x is not local to the lambdas, but is defined in
the outer scope, and it is accessed when the lambda is called — not when it
is defined. At the end of the loop, the value of x is 4, so all the
functions now return 4**2, i.e. 16. You can also verify this by
changing the value of x and see how the results of the lambdas change:
>>> x = 8
>>> squares[2]()
64
In order to avoid this, you need to save the values in variables local to the
lambdas, so that they don’t rely on the value of the global x:
>>> squares = []
>>> for x in range(5):
... squares.append(lambda n=x: n**2)
Here, n=x creates a new variable n local to the lambda and computed
when the lambda is defined so that it has the same value that x had at
that point in the loop. This means that the value of n will be 0
in the first lambda, 1 in the second, 2 in the third, and so on.
Therefore each lambda will now return the correct result:
>>> squares[2]()
4
>>> squares[4]()
16
Note that this behaviour is not peculiar to lambdas, but applies to regular functions too.
In general, don’t use from modulename import *. Doing so clutters the
importer’s namespace, and makes it much harder for linters to detect undefined
names.
Import modules at the top of a file. Doing so makes it clear what other modules your code requires and avoids questions of whether the module name is in scope. Using one import per line makes it easy to add and delete module imports, but using multiple imports per line uses less screen space.
It’s good practice if you import modules in the following order:
sys, os, getopt, reIt is sometimes necessary to move imports to a function or class to avoid problems with circular imports. Gordon McMillan says:
Circular imports are fine where both modules use the “import <module>” form of import. They fail when the 2nd module wants to grab a name out of the first (“from module import name”) and the import is at the top level. That’s because names in the 1st are not yet available, because the first module is busy importing the 2nd.
In this case, if the second module is only used in one function, then the import can easily be moved into that function. By the time the import is called, the first module will have finished initializing, and the second module can do its import.
It may also be necessary to move imports out of the top level of code if some of the modules are platform-specific. In that case, it may not even be possible to import all of the modules at the top of the file. In this case, importing the correct modules in the corresponding platform-specific code is a good option.
Only move imports into a local scope, such as inside a function definition, if
it’s necessary to solve a problem such as avoiding a circular import or are
trying to reduce the initialization time of a module. This technique is
especially helpful if many of the imports are unnecessary depending on how the
program executes. You may also want to move imports into a function if the
modules are only ever used in that function. Note that loading a module the
first time may be expensive because of the one time initialization of the
module, but loading a module multiple times is virtually free, costing only a
couple of dictionary lookups. Even if the module name has gone out of scope,
the module is probably available in sys.modules.
Collect the arguments using the * and ** specifiers in the function’s
parameter list; this gives you the positional arguments as a tuple and the
keyword arguments as a dictionary. You can then pass these arguments when
calling another function by using * and **:
def f(x, *args, **kwargs):
...
kwargs['width'] = '14.3c'
...
g(x, *args, **kwargs)
Parameters are defined by the names that appear in a function definition, whereas arguments are the values actually passed to a function when calling it. Parameters define what types of arguments a function can accept. For example, given the function definition:
def func(foo, bar=None, **kwargs):
pass
foo, bar and kwargs are parameters of func. However, when calling
func, for example:
func(42, bar=314, extra=somevar)
the values 42, 314, and somevar are arguments.
If you wrote code like:
>>> x = []
>>> y = x
>>> y.append(10)
>>> y
[10]
>>> x
[10]
you might be wondering why appending an element to y changed x too.
There are two factors that produce this result:
y = x doesn’t
create a copy of the list – it creates a new variable y that refers to
the same object x refers to. This means that there is only one object
(the list), and both x and y refer to it.After the call to append(), the content of the mutable object has
changed from [] to [10]. Since both the variables refer to the same
object, using either name accesses the modified value [10].
If we instead assign an immutable object to x:
>>> x = 5 # ints are immutable
>>> y = x
>>> x = x + 1 # 5 can't be mutated, we are creating a new object here
>>> x
6
>>> y
5
we can see that in this case x and y are not equal anymore. This is
because integers are immutable, and when we do x = x + 1 we are not
mutating the int 5 by incrementing its value; instead, we are creating a
new object (the int 6) and assigning it to x (that is, changing which
object x refers to). After this assignment we have two objects (the ints
6 and 5) and two variables that refer to them (x now refers to
6 but y still refers to 5).
Some operations (for example y.append(10) and y.sort()) mutate the
object, whereas superficially similar operations (for example y = y + [10]
and sorted(y)) create a new object. In general in Python (and in all cases
in the standard library) a method that mutates an object will return None
to help avoid getting the two types of operations confused. So if you
mistakenly write y.sort() thinking it will give you a sorted copy of y,
you’ll instead end up with None, which will likely cause your program to
generate an easily diagnosed error.
However, there is one class of operations where the same operation sometimes
has different behaviors with different types: the augmented assignment
operators. For example, += mutates lists but not tuples or ints (a_list
+= [1, 2, 3] is equivalent to a_list.extend([1, 2, 3]) and mutates
a_list, whereas some_tuple += (1, 2, 3) and some_int += 1 create
new objects).
In other words:
list, dict, set,
etc.), we can use some specific operations to mutate it and all the variables
that refer to it will see the change.str, int, tuple,
etc.), all the variables that refer to it will always see the same value,
but operations that transform that value into a new value always return a new
object.If you want to know if two variables refer to the same object or not, you can
use the is operator, or the built-in function id().
Remember that arguments are passed by assignment in Python. Since assignment just creates references to objects, there’s no alias between an argument name in the caller and callee, and so no call-by-reference per se. You can achieve the desired effect in a number of ways.
By returning a tuple of the results:
def func2(a, b):
a = 'new-value' # a and b are local names
b = b + 1 # assigned to new objects
return a, b # return new values
x, y = 'old-value', 99
x, y = func2(x, y)
print(x, y) # output: new-value 100
This is almost always the clearest solution.
By using global variables. This isn’t thread-safe, and is not recommended.
By passing a mutable (changeable in-place) object:
def func1(a):
a[0] = 'new-value' # 'a' references a mutable list
a[1] = a[1] + 1 # changes a shared object
args = ['old-value', 99]
func1(args)
print(args[0], args[1]) # output: new-value 100
By passing in a dictionary that gets mutated:
def func3(args):
args['a'] = 'new-value' # args is a mutable dictionary
args['b'] = args['b'] + 1 # change it in-place
args = {'a': 'old-value', 'b': 99}
func3(args)
print(args['a'], args['b'])
Or bundle up values in a class instance:
class callByRef:
def __init__(self, **args):
for (key, value) in args.items():
setattr(self, key, value)
def func4(args):
args.a = 'new-value' # args is a mutable callByRef
args.b = args.b + 1 # change object in-place
args = callByRef(a='old-value', b=99)
func4(args)
print(args.a, args.b)
There’s almost never a good reason to get this complicated.
Your best choice is to return a tuple containing the multiple results.
You have two choices: you can use nested scopes or you can use callable objects.
For example, suppose you wanted to define linear(a,b) which returns a
function f(x) that computes the value a*x+b. Using nested scopes:
def linear(a, b):
def result(x):
return a * x + b
return result
Or using a callable object:
class linear:
def __init__(self, a, b):
self.a, self.b = a, b
def __call__(self, x):
return self.a * x + self.b
In both cases,
taxes = linear(0.3, 2)
gives a callable object where taxes(10e6) == 0.3 * 10e6 + 2.
The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance:
class exponential(linear):
# __init__ inherited
def __call__(self, x):
return self.a * (x ** self.b)
Object can encapsulate state for several methods:
class counter:
value = 0
def set(self, x):
self.value = x
def up(self):
self.value = self.value + 1
def down(self):
self.value = self.value - 1
count = counter()
inc, dec, reset = count.up, count.down, count.set
Here inc(), dec() and reset() act like functions which share the
same counting variable.
In general, try copy.copy() or copy.deepcopy() for the general case.
Not all objects can be copied, but most can.
Some objects can be copied more easily. Dictionaries have a copy()
method:
newdict = olddict.copy()
Sequences can be copied by slicing:
new_l = l[:]
For an instance x of a user-defined class, dir(x) returns an alphabetized
list of the names containing the instance attributes and methods and attributes
defined by its class.
Generally speaking, it can’t, because objects don’t really have names.
Essentially, assignment always binds a name to a value; The same is true of
def and class statements, but in that case the value is a
callable. Consider the following code:
>>> class A:
... pass
...
>>> B = A
>>> a = B()
>>> b = a
>>> print(b)
<__main__.A object at 0x16D07CC>
>>> print(a)
<__main__.A object at 0x16D07CC>
Arguably the class has a name: even though it is bound to two names and invoked through the name B the created instance is still reported as an instance of class A. However, it is impossible to say whether the instance’s name is a or b, since both names are bound to the same value.
Generally speaking it should not be necessary for your code to “know the names” of particular values. Unless you are deliberately writing introspective programs, this is usually an indication that a change of approach might be beneficial.
In comp.lang.python, Fredrik Lundh once gave an excellent analogy in answer to this question:
The same way as you get the name of that cat you found on your porch: the cat (object) itself cannot tell you its name, and it doesn’t really care – so the only way to find out what it’s called is to ask all your neighbours (namespaces) if it’s their cat (object)...
....and don’t be surprised if you’ll find that it’s known by many names, or no name at all!
Comma is not an operator in Python. Consider this session:
>>> "a" in "b", "a"
(False, 'a')
Since the comma is not an operator, but a separator between expressions the above is evaluated as if you had entered:
("a" in "b"), "a"
not:
"a" in ("b", "a")
The same is true of the various assignment operators (=, += etc). They
are not truly operators but syntactic delimiters in assignment statements.
Yes, there is. The syntax is as follows:
[on_true] if [expression] else [on_false]
x, y = 50, 25
small = x if x < y else y
Before this syntax was introduced in Python 2.5, a common idiom was to use logical operators:
[expression] and [on_true] or [on_false]
However, this idiom is unsafe, as it can give wrong results when on_true
has a false boolean value. Therefore, it is always better to use
the ... if ... else ... form.
Yes. Usually this is done by nesting lambda within
lambda. See the following three examples, due to Ulf Bartelt:
from functools import reduce
# Primes < 1000
print(list(filter(None,map(lambda y:y*reduce(lambda x,y:x*y!=0,
map(lambda x,y=y:y%x,range(2,int(pow(y,0.5)+1))),1),range(2,1000)))))
# First 10 Fibonacci numbers
print(list(map(lambda x,f=lambda x,f:(f(x-1,f)+f(x-2,f)) if x>1 else 1:
f(x,f), range(10))))
# Mandelbrot set
print((lambda Ru,Ro,Iu,Io,IM,Sx,Sy:reduce(lambda x,y:x+y,map(lambda y,
Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,Sy=Sy,L=lambda yc,Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,i=IM,
Sx=Sx,Sy=Sy:reduce(lambda x,y:x+y,map(lambda x,xc=Ru,yc=yc,Ru=Ru,Ro=Ro,
i=i,Sx=Sx,F=lambda xc,yc,x,y,k,f=lambda xc,yc,x,y,k,f:(k<=0)or (x*x+y*y
>=4.0) or 1+f(xc,yc,x*x-y*y+xc,2.0*x*y+yc,k-1,f):f(xc,yc,x,y,k,f):chr(
64+F(Ru+x*(Ro-Ru)/Sx,yc,0,0,i)),range(Sx))):L(Iu+y*(Io-Iu)/Sy),range(Sy
))))(-2.1, 0.7, -1.2, 1.2, 30, 80, 24))
# \___ ___/ \___ ___/ | | |__ lines on screen
# V V | |______ columns on screen
# | | |__________ maximum of "iterations"
# | |_________________ range on y axis
# |____________________________ range on x axis
Don’t try this at home, kids!
To specify an octal digit, precede the octal value with a zero, and then a lower or uppercase “o”. For example, to set the variable “a” to the octal value “10” (8 in decimal), type:
>>> a = 0o10
>>> a
8
Hexadecimal is just as easy. Simply precede the hexadecimal number with a zero, and then a lower or uppercase “x”. Hexadecimal digits can be specified in lower or uppercase. For example, in the Python interpreter:
>>> a = 0xa5
>>> a
165
>>> b = 0XB2
>>> b
178
It’s primarily driven by the desire that i % j have the same sign as j.
If you want that, and also want:
i == (i // j) * j + (i % j)
then integer division has to return the floor. C also requires that identity to
hold, and then compilers that truncate i // j need to make i % j have
the same sign as i.
There are few real use cases for i % j when j is negative. When j
is positive, there are many, and in virtually all of them it’s more useful for
i % j to be >= 0. If the clock says 10 now, what did it say 200 hours
ago? -190 % 12 == 2 is useful; -190 % 12 == -10 is a bug waiting to
bite.
For integers, use the built-in int() type constructor, e.g. int('144')
== 144. Similarly, float() converts to floating-point,
e.g. float('144') == 144.0.
By default, these interpret the number as decimal, so that int('0144') ==
144 and int('0x144') raises ValueError. int(string, base) takes
the base to convert from as a second optional argument, so int('0x144', 16) ==
324. If the base is specified as 0, the number is interpreted using Python’s
rules: a leading ‘0o’ indicates octal, and ‘0x’ indicates a hex number.
Do not use the built-in function eval() if all you need is to convert
strings to numbers. eval() will be significantly slower and it presents a
security risk: someone could pass you a Python expression that might have
unwanted side effects. For example, someone could pass
__import__('os').system("rm -rf $HOME") which would erase your home
directory.
eval() also has the effect of interpreting numbers as Python expressions,
so that e.g. eval('09') gives a syntax error because Python does not allow
leading ‘0’ in a decimal number (except ‘0’).
To convert, e.g., the number 144 to the string ‘144’, use the built-in type
constructor str(). If you want a hexadecimal or octal representation, use
the built-in functions hex() or oct(). For fancy formatting, see
the Formatted string literals and Format String Syntax sections,
e.g. "{:04d}".format(144) yields
'0144' and "{:.3f}".format(1.0/3.0) yields '0.333'.
You can’t, because strings are immutable. In most situations, you should
simply construct a new string from the various parts you want to assemble
it from. However, if you need an object with the ability to modify in-place
unicode data, try using an io.StringIO object or the array
module:
>>> import io
>>> s = "Hello, world"
>>> sio = io.StringIO(s)
>>> sio.getvalue()
'Hello, world'
>>> sio.seek(7)
7
>>> sio.write("there!")
6
>>> sio.getvalue()
'Hello, there!'
>>> import array
>>> a = array.array('u', s)
>>> print(a)
array('u', 'Hello, world')
>>> a[0] = 'y'
>>> print(a)
array('u', 'yello, world')
>>> a.tounicode()
'yello, world'
There are various techniques.
The best is to use a dictionary that maps strings to functions. The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct:
def a():
pass
def b():
pass
dispatch = {'go': a, 'stop': b} # Note lack of parens for funcs
dispatch[get_input()]() # Note trailing parens to call function
Use the built-in function getattr():
import foo
getattr(foo, 'bar')()
Note that getattr() works on any object, including classes, class
instances, modules, and so on.
This is used in several places in the standard library, like this:
class Foo:
def do_foo(self):
...
def do_bar(self):
...
f = getattr(foo_instance, 'do_' + opname)
f()
Use locals() or eval() to resolve the function name:
def myFunc():
print("hello")
fname = "myFunc"
f = locals()[fname]
f()
f = eval(fname)
f()
Note: Using eval() is slow and dangerous. If you don’t have absolute
control over the contents of the string, someone could pass a string that
resulted in an arbitrary function being executed.
You can use S.rstrip("\r\n") to remove all occurrences of any line
terminator from the end of the string S without removing other trailing
whitespace. If the string S represents more than one line, with several
empty lines at the end, the line terminators for all the blank lines will
be removed:
>>> lines = ("line 1 \r\n"
... "\r\n"
... "\r\n")
>>> lines.rstrip("\n\r")
'line 1 '
Since this is typically only desired when reading text one line at a time, using
S.rstrip() this way works well.
Not as such.
For simple input parsing, the easiest approach is usually to split the line into
whitespace-delimited words using the split() method of string objects
and then convert decimal strings to numeric values using int() or
float(). split() supports an optional “sep” parameter which is useful
if the line uses something other than whitespace as a separator.
For more complicated input parsing, regular expressions are more powerful
than C’s sscanf() and better suited for the task.
That’s a tough one, in general. First, here are a list of things to remember before diving further:
profile module).timeit module).That being said, there are many tricks to speed up Python code. Here are some general principles which go a long way towards reaching acceptable performance levels:
collections module.list.sort() built-in method or the related sorted()
function to do sorting (and see the Sorting HOW TO for examples
of moderately advanced usage).If you have reached the limit of what pure Python can allow, there are tools to take you further away. For example, Cython can compile a slightly modified version of Python code into a C extension, and can be used on many different platforms. Cython can take advantage of compilation (and optional type annotations) to make your code significantly faster than when interpreted. If you are confident in your C programming skills, you can also write a C extension module yourself.
See also
The wiki page devoted to performance tips.
str and bytes objects are immutable, therefore concatenating
many strings together is inefficient as each concatenation creates a new
object. In the general case, the total runtime cost is quadratic in the
total string length.
To accumulate many str objects, the recommended idiom is to place
them into a list and call str.join() at the end:
chunks = []
for s in my_strings:
chunks.append(s)
result = ''.join(chunks)
(another reasonably efficient idiom is to use io.StringIO)
To accumulate many bytes objects, the recommended idiom is to extend
a bytearray object using in-place concatenation (the += operator):
result = bytearray()
for b in my_bytes_objects:
result += b
The type constructor tuple(seq) converts any sequence (actually, any
iterable) into a tuple with the same items in the same order.
For example, tuple([1, 2, 3]) yields (1, 2, 3) and tuple('abc')
yields ('a', 'b', 'c'). If the argument is a tuple, it does not make a copy
but returns the same object, so it is cheap to call tuple() when you
aren’t sure that an object is already a tuple.
The type constructor list(seq) converts any sequence or iterable into a list
with the same items in the same order. For example, list((1, 2, 3)) yields
[1, 2, 3] and list('abc') yields ['a', 'b', 'c']. If the argument
is a list, it makes a copy just like seq[:] would.
Python sequences are indexed with positive numbers and negative numbers. For
positive numbers 0 is the first index 1 is the second index and so forth. For
negative indices -1 is the last index and -2 is the penultimate (next to last)
index and so forth. Think of seq[-n] as the same as seq[len(seq)-n].
Using negative indices can be very convenient. For example S[:-1] is all of
the string except for its last character, which is useful for removing the
trailing newline from a string.
Use the reversed() built-in function, which is new in Python 2.4:
for x in reversed(sequence):
... # do something with x ...
This won’t touch your original sequence, but build a new copy with reversed order to iterate over.
With Python 2.3, you can use an extended slice syntax:
for x in sequence[::-1]:
... # do something with x ...
See the Python Cookbook for a long discussion of many ways to do this:
If you don’t mind reordering the list, sort it and then scan from the end of the list, deleting duplicates as you go:
if mylist:
mylist.sort()
last = mylist[-1]
for i in range(len(mylist)-2, -1, -1):
if last == mylist[i]:
del mylist[i]
else:
last = mylist[i]
If all elements of the list may be used as set keys (i.e. they are all hashable) this is often faster
mylist = list(set(mylist))
This converts the list into a set, thereby removing duplicates, and then back into a list.
Use a list:
["this", 1, "is", "an", "array"]
Lists are equivalent to C or Pascal arrays in their time complexity; the primary difference is that a Python list can contain objects of many different types.
The array module also provides methods for creating arrays of fixed types
with compact representations, but they are slower to index than lists. Also
note that the Numeric extensions and others define array-like structures with
various characteristics as well.
To get Lisp-style linked lists, you can emulate cons cells using tuples:
lisp_list = ("like", ("this", ("example", None) ) )
If mutability is desired, you could use lists instead of tuples. Here the
analogue of lisp car is lisp_list[0] and the analogue of cdr is
lisp_list[1]. Only do this if you’re sure you really need to, because it’s
usually a lot slower than using Python lists.
You probably tried to make a multidimensional array like this:
>>> A = [[None] * 2] * 3
This looks correct if you print it:
>>> A
[[None, None], [None, None], [None, None]]
But when you assign a value, it shows up in multiple places:
>>> A[0][0] = 5
>>> A
[[5, None], [5, None], [5, None]]
The reason is that replicating a list with * doesn’t create copies, it only
creates references to the existing objects. The *3 creates a list
containing 3 references to the same list of length two. Changes to one row will
show in all rows, which is almost certainly not what you want.
The suggested approach is to create a list of the desired length first and then fill in each element with a newly created list:
A = [None] * 3
for i in range(3):
A[i] = [None] * 2
This generates a list containing 3 different lists of length two. You can also use a list comprehension:
w, h = 2, 3
A = [[None] * w for i in range(h)]
Or, you can use an extension that provides a matrix datatype; NumPy is the best known.
Use a list comprehension:
result = [obj.method() for obj in mylist]
This is because of a combination of the fact that augmented assignment operators are assignment operators, and the difference between mutable and immutable objects in Python.
This discussion applies in general when augmented assignment operators are
applied to elements of a tuple that point to mutable objects, but we’ll use
a list and += as our exemplar.
If you wrote:
>>> a_tuple = (1, 2)
>>> a_tuple[0] += 1
Traceback (most recent call last):
...
TypeError: 'tuple' object does not support item assignment
The reason for the exception should be immediately clear: 1 is added to the
object a_tuple[0] points to (1), producing the result object, 2,
but when we attempt to assign the result of the computation, 2, to element
0 of the tuple, we get an error because we can’t change what an element of
a tuple points to.
Under the covers, what this augmented assignment statement is doing is approximately this:
>>> result = a_tuple[0] + 1
>>> a_tuple[0] = result
Traceback (most recent call last):
...
TypeError: 'tuple' object does not support item assignment
It is the assignment part of the operation that produces the error, since a tuple is immutable.
When you write something like:
>>> a_tuple = (['foo'], 'bar')
>>> a_tuple[0] += ['item']
Traceback (most recent call last):
...
TypeError: 'tuple' object does not support item assignment
The exception is a bit more surprising, and even more surprising is the fact that even though there was an error, the append worked:
>>> a_tuple[0]
['foo', 'item']
To see why this happens, you need to know that (a) if an object implements an
__iadd__ magic method, it gets called when the += augmented assignment
is executed, and its return value is what gets used in the assignment statement;
and (b) for lists, __iadd__ is equivalent to calling extend on the list
and returning the list. That’s why we say that for lists, += is a
“shorthand” for list.extend:
>>> a_list = []
>>> a_list += [1]
>>> a_list
[1]
This is equivalent to:
>>> result = a_list.__iadd__([1])
>>> a_list = result
The object pointed to by a_list has been mutated, and the pointer to the
mutated object is assigned back to a_list. The end result of the
assignment is a no-op, since it is a pointer to the same object that a_list
was previously pointing to, but the assignment still happens.
Thus, in our tuple example what is happening is equivalent to:
>>> result = a_tuple[0].__iadd__(['item'])
>>> a_tuple[0] = result
Traceback (most recent call last):
...
TypeError: 'tuple' object does not support item assignment
The __iadd__ succeeds, and thus the list is extended, but even though
result points to the same object that a_tuple[0] already points to,
that final assignment still results in an error, because tuples are immutable.
The technique, attributed to Randal Schwartz of the Perl community, sorts the
elements of a list by a metric which maps each element to its “sort value”. In
Python, use the key argument for the list.sort() method:
Isorted = L[:]
Isorted.sort(key=lambda s: int(s[10:15]))
Merge them into an iterator of tuples, sort the resulting list, and then pick out the element you want.
>>> list1 = ["what", "I'm", "sorting", "by"]
>>> list2 = ["something", "else", "to", "sort"]
>>> pairs = zip(list1, list2)
>>> pairs = sorted(pairs)
>>> pairs
[("I'm", 'else'), ('by', 'sort'), ('sorting', 'to'), ('what', 'something')]
>>> result = [x[1] for x in pairs]
>>> result
['else', 'sort', 'to', 'something']
An alternative for the last step is:
>>> result = []
>>> for p in pairs: result.append(p[1])
If you find this more legible, you might prefer to use this instead of the final
list comprehension. However, it is almost twice as slow for long lists. Why?
First, the append() operation has to reallocate memory, and while it uses
some tricks to avoid doing that each time, it still has to do it occasionally,
and that costs quite a bit. Second, the expression “result.append” requires an
extra attribute lookup, and third, there’s a speed reduction from having to make
all those function calls.
A class is the particular object type created by executing a class statement. Class objects are used as templates to create instance objects, which embody both the data (attributes) and code (methods) specific to a datatype.
A class can be based on one or more other classes, called its base class(es). It
then inherits the attributes and methods of its base classes. This allows an
object model to be successively refined by inheritance. You might have a
generic Mailbox class that provides basic accessor methods for a mailbox,
and subclasses such as MboxMailbox, MaildirMailbox, OutlookMailbox
that handle various specific mailbox formats.
A method is a function on some object x that you normally call as
x.name(arguments...). Methods are defined as functions inside the class
definition:
class C:
def meth(self, arg):
return arg * 2 + self.attribute
Self is merely a conventional name for the first argument of a method. A method
defined as meth(self, a, b, c) should be called as x.meth(a, b, c) for
some instance x of the class in which the definition occurs; the called
method will think it is called as meth(x, a, b, c).
See also Why must ‘self’ be used explicitly in method definitions and calls?.
Use the built-in function isinstance(obj, cls). You can check if an object
is an instance of any of a number of classes by providing a tuple instead of a
single class, e.g. isinstance(obj, (class1, class2, ...)), and can also
check whether an object is one of Python’s built-in types, e.g.
isinstance(obj, str) or isinstance(obj, (int, float, complex)).
Note that most programs do not use isinstance() on user-defined classes
very often. If you are developing the classes yourself, a more proper
object-oriented style is to define methods on the classes that encapsulate a
particular behaviour, instead of checking the object’s class and doing a
different thing based on what class it is. For example, if you have a function
that does something:
def search(obj):
if isinstance(obj, Mailbox):
... # code to search a mailbox
elif isinstance(obj, Document):
... # code to search a document
elif ...
A better approach is to define a search() method on all the classes and just
call it:
class Mailbox:
def search(self):
... # code to search a mailbox
class Document:
def search(self):
... # code to search a document
obj.search()
Delegation is an object oriented technique (also called a design pattern).
Let’s say you have an object x and want to change the behaviour of just one
of its methods. You can create a new class that provides a new implementation
of the method you’re interested in changing and delegates all other methods to
the corresponding method of x.
Python programmers can easily implement delegation. For example, the following class implements a class that behaves like a file but converts all written data to uppercase:
class UpperOut:
def __init__(self, outfile):
self._outfile = outfile
def write(self, s):
self._outfile.write(s.upper())
def __getattr__(self, name):
return getattr(self._outfile, name)
Here the UpperOut class redefines the write() method to convert the
argument string to uppercase before calling the underlying
self.__outfile.write() method. All other methods are delegated to the
underlying self.__outfile object. The delegation is accomplished via the
__getattr__ method; consult the language reference
for more information about controlling attribute access.
Note that for more general cases delegation can get trickier. When attributes
must be set as well as retrieved, the class must define a __setattr__()
method too, and it must do so carefully. The basic implementation of
__setattr__() is roughly equivalent to the following:
class X:
...
def __setattr__(self, name, value):
self.__dict__[name] = value
...
Most __setattr__() implementations must modify self.__dict__ to store
local state for self without causing an infinite recursion.
Use the built-in super() function:
class Derived(Base):
def meth(self):
super(Derived, self).meth()
For version prior to 3.0, you may be using classic classes: For a class
definition such as class Derived(Base): ... you can call method meth()
defined in Base (or one of Base‘s base classes) as Base.meth(self,
arguments...). Here, Base.meth is an unbound method, so you need to
provide the self argument.
You could define an alias for the base class, assign the real base class to it before your class definition, and use the alias throughout your class. Then all you have to change is the value assigned to the alias. Incidentally, this trick is also handy if you want to decide dynamically (e.g. depending on availability of resources) which base class to use. Example:
BaseAlias = <real base class>
class Derived(BaseAlias):
def meth(self):
BaseAlias.meth(self)
...
Both static data and static methods (in the sense of C++ or Java) are supported in Python.
For static data, simply define a class attribute. To assign a new value to the attribute, you have to explicitly use the class name in the assignment:
class C:
count = 0 # number of times C.__init__ called
def __init__(self):
C.count = C.count + 1
def getcount(self):
return C.count # or return self.count
c.count also refers to C.count for any c such that isinstance(c,
C) holds, unless overridden by c itself or by some class on the base-class
search path from c.__class__ back to C.
Caution: within a method of C, an assignment like self.count = 42 creates a
new and unrelated instance named “count” in self‘s own dict. Rebinding of a
class-static data name must always specify the class whether inside a method or
not:
C.count = 314
Static methods are possible:
class C:
@staticmethod
def static(arg1, arg2, arg3):
# No 'self' parameter!
...
However, a far more straightforward way to get the effect of a static method is via a simple module-level function:
def getcount():
return C.count
If your code is structured so as to define one class (or tightly related class hierarchy) per module, this supplies the desired encapsulation.
This answer actually applies to all methods, but the question usually comes up first in the context of constructors.
In C++ you’d write
class C {
C() { cout << "No arguments\n"; }
C(int i) { cout << "Argument is " << i << "\n"; }
}
In Python you have to write a single constructor that catches all cases using default arguments. For example:
class C:
def __init__(self, i=None):
if i is None:
print("No arguments")
else:
print("Argument is", i)
This is not entirely equivalent, but close enough in practice.
You could also try a variable-length argument list, e.g.
def __init__(self, *args):
...
The same approach works for all method definitions.
Variable names with double leading underscores are “mangled” to provide a simple
but effective way to define class private variables. Any identifier of the form
__spam (at least two leading underscores, at most one trailing underscore)
is textually replaced with _classname__spam, where classname is the
current class name with any leading underscores stripped.
This doesn’t guarantee privacy: an outside user can still deliberately access
the “_classname__spam” attribute, and private values are visible in the object’s
__dict__. Many Python programmers never bother to use private variable
names at all.
There are several possible reasons for this.
The del statement does not necessarily call __del__() – it simply
decrements the object’s reference count, and if this reaches zero
__del__() is called.
If your data structures contain circular links (e.g. a tree where each child has
a parent reference and each parent has a list of children) the reference counts
will never go back to zero. Once in a while Python runs an algorithm to detect
such cycles, but the garbage collector might run some time after the last
reference to your data structure vanishes, so your __del__() method may be
called at an inconvenient and random time. This is inconvenient if you’re trying
to reproduce a problem. Worse, the order in which object’s __del__()
methods are executed is arbitrary. You can run gc.collect() to force a
collection, but there are pathological cases where objects will never be
collected.
Despite the cycle collector, it’s still a good idea to define an explicit
close() method on objects to be called whenever you’re done with them. The
close() method can then remove attributes that refer to subobjecs. Don’t
call __del__() directly – __del__() should call close() and
close() should make sure that it can be called more than once for the same
object.
Another way to avoid cyclical references is to use the weakref module,
which allows you to point to objects without incrementing their reference count.
Tree data structures, for instance, should use weak references for their parent
and sibling references (if they need them!).
Finally, if your __del__() method raises an exception, a warning message
is printed to sys.stderr.
Python does not keep track of all instances of a class (or of a built-in type). You can program the class’s constructor to keep track of all instances by keeping a list of weak references to each instance.
id() appear to be not unique?¶The id() builtin returns an integer that is guaranteed to be unique during
the lifetime of the object. Since in CPython, this is the object’s memory
address, it happens frequently that after an object is deleted from memory, the
next freshly created object is allocated at the same position in memory. This
is illustrated by this example:
>>> id(1000)
13901272
>>> id(2000)
13901272
The two ids belong to different integer objects that are created before, and
deleted immediately after execution of the id() call. To be sure that
objects whose id you want to examine are still alive, create another reference
to the object:
>>> a = 1000; b = 2000
>>> id(a)
13901272
>>> id(b)
13891296
When a module is imported for the first time (or when the source file has
changed since the current compiled file was created) a .pyc file containing
the compiled code should be created in a __pycache__ subdirectory of the
directory containing the .py file. The .pyc file will have a
filename that starts with the same name as the .py file, and ends with
.pyc, with a middle component that depends on the particular python
binary that created it. (See PEP 3147 for details.)
One reason that a .pyc file may not be created is a permissions problem
with the directory containing the source file, meaning that the __pycache__
subdirectory cannot be created. This can happen, for example, if you develop as
one user but run as another, such as if you are testing with a web server.
Unless the PYTHONDONTWRITEBYTECODE environment variable is set,
creation of a .pyc file is automatic if you’re importing a module and Python
has the ability (permissions, free space, etc...) to create a __pycache__
subdirectory and write the compiled module to that subdirectory.
Running Python on a top level script is not considered an import and no
.pyc will be created. For example, if you have a top-level module
foo.py that imports another module xyz.py, when you run foo (by
typing python foo.py as a shell command), a .pyc will be created for
xyz because xyz is imported, but no .pyc file will be created for
foo since foo.py isn’t being imported.
If you need to create a .pyc file for foo – that is, to create a
.pyc file for a module that is not imported – you can, using the
py_compile and compileall modules.
The py_compile module can manually compile any module. One way is to use
the compile() function in that module interactively:
>>> import py_compile
>>> py_compile.compile('foo.py')
This will write the .pyc to a __pycache__ subdirectory in the same
location as foo.py (or you can override that with the optional parameter
cfile).
You can also automatically compile all files in a directory or directories using
the compileall module. You can do it from the shell prompt by running
compileall.py and providing the path of a directory containing Python files
to compile:
python -m compileall .
A module can find out its own module name by looking at the predefined global
variable __name__. If this has the value '__main__', the program is
running as a script. Many modules that are usually used by importing them also
provide a command-line interface or a self-test, and only execute this code
after checking __name__:
def main():
print('Running test...')
...
if __name__ == '__main__':
main()
Suppose you have the following modules:
foo.py:
from bar import bar_var
foo_var = 1
bar.py:
from foo import foo_var
bar_var = 2
The problem is that the interpreter will perform the following steps:
The last step fails, because Python isn’t done with interpreting foo yet and
the global symbol dictionary for foo is still empty.
The same thing happens when you use import foo, and then try to access
foo.foo_var in global code.
There are (at least) three possible workarounds for this problem.
Guido van Rossum recommends avoiding all uses of from <module> import ...,
and placing all code inside functions. Initializations of global variables and
class variables should use constants or built-in functions only. This means
everything from an imported module is referenced as <module>.<name>.
Jim Roskind suggests performing steps in the following order in each module:
import statementsvan Rossum doesn’t like this approach much because the imports appear in a strange place, but it does work.
Matthias Urlichs recommends restructuring your code so that the recursive import is not necessary in the first place.
These solutions are not mutually exclusive.
Consider using the convenience function import_module() from
importlib instead:
z = importlib.import_module('x.y.z')
For reasons of efficiency as well as consistency, Python only reads the module file on the first time a module is imported. If it didn’t, in a program consisting of many modules where each one imports the same basic module, the basic module would be parsed and re-parsed many times. To force re-reading of a changed module, do this:
import importlib
import modname
importlib.reload(modname)
Warning: this technique is not 100% fool-proof. In particular, modules containing statements like
from modname import some_objects
will continue to work with the old version of the imported objects. If the module contains class definitions, existing class instances will not be updated to use the new class definition. This can result in the following paradoxical behaviour:
>>> import importlib
>>> import cls
>>> c = cls.C() # Create an instance of C
>>> importlib.reload(cls)
<module 'cls' from 'cls.py'>
>>> isinstance(c, cls.C) # isinstance is false?!?
False
The nature of the problem is made clear if you print out the “identity” of the class objects:
>>> hex(id(c.__class__))
'0x7352a0'
>>> hex(id(cls.C))
'0x4198d0'