Programming FAQ¶
General Questions¶
Is there a source code level debugger with breakpoints, single-stepping, etc.?¶
Yes.
Several debuggers for Python are described below, and the built-in function
breakpoint()
allows you to drop into any of them.
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/idle3), 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 pywin32 project and as a part of the ActivePython distribution.
Eric is an IDE built on PyQt and the Scintilla editing component.
trepan3k is a gdb-like debugger.
Visual Studio Code is an IDE with debugging tools that integrates with version-control software.
There are a number of commercial Python IDEs that include graphical debuggers. They include:
Are there tools to help find bugs or perform static analysis?¶
Yes.
Pylint and Pyflakes do basic checking that will help you catch bugs sooner.
Static type checkers such as Mypy, Pyre, and Pytype can check type hints in Python source code.
How can I create a stand-alone binary from a Python script?¶
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; with 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.
The following packages can help with the creation of console and GUI executables:
Nuitka (Cross-platform)
PyInstaller (Cross-platform)
PyOxidizer (Cross-platform)
cx_Freeze (Cross-platform)
py2app (macOS only)
py2exe (Windows only)
Are there coding standards or a style guide for Python programs?¶
Yes. The coding style required for standard library modules is documented as PEP 8.
Core Language¶
Why am I getting an UnboundLocalError when the variable has a value?¶
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
What are the rules for local and global variables in Python?¶
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.
Why do lambdas defined in a loop with different values all return the same result?¶
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.
What are the “best practices” for using import in a module?¶
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:
third-party library modules (anything installed in Python’s site-packages directory) – e.g.
dateutil
,requests
,PIL.Image
locally developed modules
It 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
.
How can I pass optional or keyword parameters from one function to another?¶
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)
What is the difference between arguments and parameters?¶
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 kind 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.
Why did changing list ‘y’ also change list ‘x’?¶
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:
Variables are simply names that refer to objects. Doing
y = x
doesn’t create a copy of the list – it creates a new variabley
that refers to the same objectx
refers to. This means that there is only one object (the list), and bothx
andy
refer to it.Lists are mutable, which means that you can change their content.
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:
If we have a mutable object (
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.If we have an immutable object (
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()
.
How do I write a function with output parameters (call by reference)?¶
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 func1(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 >>> func1(x, y) ('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 func2(a): ... a[0] = 'new-value' # 'a' references a mutable list ... a[1] = a[1] + 1 # changes a shared object ... >>> args = ['old-value', 99] >>> func2(args) >>> args ['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) >>> args {'a': 'new-value', 'b': 100}
Or bundle up values in a class instance:
>>> class Namespace: ... 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 Namespace ... args.b = args.b + 1 # change object in-place ... >>> args = Namespace(a='old-value', b=99) >>> func4(args) >>> vars(args) {'a': 'new-value', 'b': 100}
There’s almost never a good reason to get this complicated.
Your best choice is to return a tuple containing the multiple results.
How do you make a higher order function in Python?¶
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.
How do I copy an object in Python?¶
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[:]
How can I find the methods or attributes of an object?¶
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.
How can my code discover the name of an object?¶
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!
What’s up with the comma operator’s precedence?¶
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.
Is there an equivalent of C’s “?:” ternary operator?¶
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.
Is it possible to write obfuscated one-liners in Python?¶
Yes. Usually this is done by nesting lambda
within
lambda
. See the following three examples, slightly adapted from 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+'\n'+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!
What does the slash(/) in the parameter list of a function mean?¶
A slash in the argument list of a function denotes that the parameters prior to
it are positional-only. Positional-only parameters are the ones without an
externally usable name. Upon calling a function that accepts positional-only
parameters, arguments are mapped to parameters based solely on their position.
For example, divmod()
is a function that accepts positional-only
parameters. Its documentation looks like this:
>>> help(divmod)
Help on built-in function divmod in module builtins:
divmod(x, y, /)
Return the tuple (x//y, x%y). Invariant: div*y + mod == x.
The slash at the end of the parameter list means that both parameters are
positional-only. Thus, calling divmod()
with keyword arguments would lead
to an error:
>>> divmod(x=3, y=4)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: divmod() takes no keyword arguments
Numbers and strings¶
How do I specify hexadecimal and octal integers?¶
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
Why does -22 // 10 return -3?¶
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.
How do I get int literal attribute instead of SyntaxError?¶
Trying to lookup an int
literal attribute in the normal manner gives
a SyntaxError
because the period is seen as a decimal point:
>>> 1.__class__
File "<stdin>", line 1
1.__class__
^
SyntaxError: invalid decimal literal
The solution is to separate the literal from the period with either a space or parentheses.
>>> 1 .__class__
<class 'int'>
>>> (1).__class__
<class 'int'>
How do I convert a string to a number?¶
For integers, use the built-in int()
type constructor, e.g. int('144')
== 144
. Similarly, float()
converts to a floating-point number,
e.g. float('144') == 144.0
.
By default, these interpret the number as decimal, so that int('0144') ==
144
holds true, 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’).
How do I convert a number to a string?¶
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 f-strings and Format String Syntax sections,
e.g. "{:04d}".format(144)
yields
'0144'
and "{:.3f}".format(1.0/3.0)
yields '0.333'
.
How do I modify a string in place?¶
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('w', s)
>>> print(a)
array('w', 'Hello, world')
>>> a[0] = 'y'
>>> print(a)
array('w', 'yello, world')
>>> a.tounicode()
'yello, world'
How do I use strings to call functions/methods?¶
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()
to resolve the function name:def myFunc(): print("hello") fname = "myFunc" f = locals()[fname] f()
Is there an equivalent to Perl’s chomp()
for removing trailing newlines from strings?¶
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.
Is there a scanf()
or sscanf()
equivalent?¶
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.
What does UnicodeDecodeError
or UnicodeEncodeError
error mean?¶
See the Unicode HOWTO.
Can I end a raw string with an odd number of backslashes?¶
A raw string ending with an odd number of backslashes will escape the string’s quote:
>>> r'C:\this\will\not\work\'
File "<stdin>", line 1
r'C:\this\will\not\work\'
^
SyntaxError: unterminated string literal (detected at line 1)
There are several workarounds for this. One is to use regular strings and double the backslashes:
>>> 'C:\\this\\will\\work\\'
'C:\\this\\will\\work\\'
Another is to concatenate a regular string containing an escaped backslash to the raw string:
>>> r'C:\this\will\work' '\\'
'C:\\this\\will\\work\\'
It is also possible to use os.path.join()
to append a backslash on Windows:
>>> os.path.join(r'C:\this\will\work', '')
'C:\\this\\will\\work\\'
Note that while a backslash will “escape” a quote for the purposes of determining where the raw string ends, no escaping occurs when interpreting the value of the raw string. That is, the backslash remains present in the value of the raw string:
>>> r'backslash\'preserved'
"backslash\\'preserved"
Also see the specification in the language reference.
Performance¶
My program is too slow. How do I speed it up?¶
That’s a tough one, in general. First, here are a list of things to remember before diving further:
Performance characteristics vary across Python implementations. This FAQ focuses on CPython.
Behaviour can vary across operating systems, especially when talking about I/O or multi-threading.
You should always find the hot spots in your program before attempting to optimize any code (see the
profile
module).Writing benchmark scripts will allow you to iterate quickly when searching for improvements (see the
timeit
module).It is highly recommended to have good code coverage (through unit testing or any other technique) before potentially introducing regressions hidden in sophisticated optimizations.
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:
Making your algorithms faster (or changing to faster ones) can yield much larger benefits than trying to sprinkle micro-optimization tricks all over your code.
Use the right data structures. Study documentation for the Built-in Types and the
collections
module.When the standard library provides a primitive for doing something, it is likely (although not guaranteed) to be faster than any alternative you may come up with. This is doubly true for primitives written in C, such as builtins and some extension types. For example, be sure to use either the
list.sort()
built-in method or the relatedsorted()
function to do sorting (and see the Sorting Techniques for examples of moderately advanced usage).Abstractions tend to create indirections and force the interpreter to work more. If the levels of indirection outweigh the amount of useful work done, your program will be slower. You should avoid excessive abstraction, especially under the form of tiny functions or methods (which are also often detrimental to readability).
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.
What is the most efficient way to concatenate many strings together?¶
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
Sequences (Tuples/Lists)¶
How do I convert between tuples and lists?¶
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.
What’s a negative index?¶
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.
How do I iterate over a sequence in reverse order?¶
Use the reversed()
built-in function:
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.
How do you remove duplicates from a list?¶
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.
How do you remove multiple items from a list?¶
As with removing duplicates, explicitly iterating in reverse with a delete condition is one possibility. However, it is easier and faster to use slice replacement with an implicit or explicit forward iteration. Here are three variations:
mylist[:] = filter(keep_function, mylist)
mylist[:] = (x for x in mylist if keep_condition)
mylist[:] = [x for x in mylist if keep_condition]
The list comprehension may be fastest.
How do you make an array in Python?¶
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 NumPy
and other third party packages 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 a 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.
How do I create a multidimensional list?¶
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.
How do I apply a method or function to a sequence of objects?¶
To call a method or function and accumulate the return values is a list, a list comprehension is an elegant solution:
result = [obj.method() for obj in mylist]
result = [function(obj) for obj in mylist]
To just run the method or function without saving the return values,
a plain for
loop will suffice:
for obj in mylist:
obj.method()
for obj in mylist:
function(obj)
Why does a_tuple[i] += [‘item’] raise an exception when the addition works?¶
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.
I want to do a complicated sort: can you do a Schwartzian Transform in Python?¶
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]))
How can I sort one list by values from another list?¶
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']
Objects¶
What is a class?¶
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.
What is a method?¶
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
What is self?¶
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?.
How do I check if an object is an instance of a given class or of a subclass of it?¶
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 isinstance()
also checks for virtual inheritance from an
abstract base class. So, the test will return True
for a
registered class even if hasn’t directly or indirectly inherited from it. To
test for “true inheritance”, scan the MRO of the class:
from collections.abc import Mapping
class P:
pass
class C(P):
pass
Mapping.register(P)
>>> c = C()
>>> isinstance(c, C) # direct
True
>>> isinstance(c, P) # indirect
True
>>> isinstance(c, Mapping) # virtual
True
# Actual inheritance chain
>>> type(c).__mro__
(<class 'C'>, <class 'P'>, <class 'object'>)
# Test for "true inheritance"
>>> Mapping in type(c).__mro__
False
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()
What is delegation?¶
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
...
Many __setattr__()
implementations call object.__setattr__()
to set
an attribute on self without causing infinite recursion:
class X:
def __setattr__(self, name, value):
# Custom logic here...
object.__setattr__(self, name, value)
Alternatively, it is possible to set attributes by inserting
entries into self.__dict__
directly.
How do I call a method defined in a base class from a derived class that extends it?¶
Use the built-in super()
function:
class Derived(Base):
def meth(self):
super().meth() # calls Base.meth
In the example, super()
will automatically determine the instance from
which it was called (the self
value), look up the method resolution
order (MRO) with type(self).__mro__
, and return the next in line after
Derived
in the MRO: Base
.
How can I organize my code to make it easier to change the base class?¶
You could assign the base class to an alias and derive from the alias. 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:
class Base:
...
BaseAlias = Base
class Derived(BaseAlias):
...
How do I create static class data and static class methods?¶
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.
How can I overload constructors (or methods) in Python?¶
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.
I try to use __spam and I get an error about _SomeClassName__spam.¶
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.
The identifier can be used unchanged within the class, but to access it outside the class, the mangled name must be used:
class A:
def __one(self):
return 1
def two(self):
return 2 * self.__one()
class B(A):
def three(self):
return 3 * self._A__one()
four = 4 * A()._A__one()
In particular, this does not guarantee privacy since an outside user can still deliberately access the private attribute; many Python programmers never bother to use private variable names at all.
See also
The private name mangling specifications for details and special cases.
My class defines __del__ but it is not called when I delete the object.¶
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 subobjects. 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
.
How do I get a list of all instances of a given class?¶
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.
Why does the result of 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 can I rely on identity tests with the is operator?¶
The is
operator tests for object identity. The test a is b
is
equivalent to id(a) == id(b)
.
The most important property of an identity test is that an object is always
identical to itself, a is a
always returns True
. Identity tests are
usually faster than equality tests. And unlike equality tests, identity tests
are guaranteed to return a boolean True
or False
.
However, identity tests can only be substituted for equality tests when object identity is assured. Generally, there are three circumstances where identity is guaranteed:
Assignments create new names but do not change object identity. After the assignment
new = old
, it is guaranteed thatnew is old
.Putting an object in a container that stores object references does not change object identity. After the list assignment
s[0] = x
, it is guaranteed thats[0] is x
.If an object is a singleton, it means that only one instance of that object can exist. After the assignments
a = None
andb = None
, it is guaranteed thata is b
becauseNone
is a singleton.
In most other circumstances, identity tests are inadvisable and equality tests
are preferred. In particular, identity tests should not be used to check
constants such as int
and str
which aren’t guaranteed to be
singletons:
>>> a = 1000
>>> b = 500
>>> c = b + 500
>>> a is c
False
>>> a = 'Python'
>>> b = 'Py'
>>> c = b + 'thon'
>>> a is c
False
Likewise, new instances of mutable containers are never identical:
>>> a = []
>>> b = []
>>> a is b
False
In the standard library code, you will see several common patterns for correctly using identity tests:
As recommended by PEP 8, an identity test is the preferred way to check for
None
. This reads like plain English in code and avoids confusion with other objects that may have boolean values that evaluate to false.Detecting optional arguments can be tricky when
None
is a valid input value. In those situations, you can create a singleton sentinel object guaranteed to be distinct from other objects. For example, here is how to implement a method that behaves likedict.pop()
:_sentinel = object() def pop(self, key, default=_sentinel): if key in self: value = self[key] del self[key] return value if default is _sentinel: raise KeyError(key) return default
Container implementations sometimes need to augment equality tests with identity tests. This prevents the code from being confused by objects such as
float('NaN')
that are not equal to themselves.
For example, here is the implementation of
collections.abc.Sequence.__contains__()
:
def __contains__(self, value):
for v in self:
if v is value or v == value:
return True
return False
How can a subclass control what data is stored in an immutable instance?¶
When subclassing an immutable type, override the __new__()
method
instead of the __init__()
method. The latter only runs after an
instance is created, which is too late to alter data in an immutable
instance.
All of these immutable classes have a different signature than their parent class:
from datetime import date
class FirstOfMonthDate(date):
"Always choose the first day of the month"
def __new__(cls, year, month, day):
return super().__new__(cls, year, month, 1)
class NamedInt(int):
"Allow text names for some numbers"
xlat = {'zero': 0, 'one': 1, 'ten': 10}
def __new__(cls, value):
value = cls.xlat.get(value, value)
return super().__new__(cls, value)
class TitleStr(str):
"Convert str to name suitable for a URL path"
def __new__(cls, s):
s = s.lower().replace(' ', '-')
s = ''.join([c for c in s if c.isalnum() or c == '-'])
return super().__new__(cls, s)
The classes can be used like this:
>>> FirstOfMonthDate(2012, 2, 14)
FirstOfMonthDate(2012, 2, 1)
>>> NamedInt('ten')
10
>>> NamedInt(20)
20
>>> TitleStr('Blog: Why Python Rocks')
'blog-why-python-rocks'
How do I cache method calls?¶
The two principal tools for caching methods are
functools.cached_property()
and functools.lru_cache()
. The
former stores results at the instance level and the latter at the class
level.
The cached_property approach only works with methods that do not take any arguments. It does not create a reference to the instance. The cached method result will be kept only as long as the instance is alive.
The advantage is that when an instance is no longer used, the cached method result will be released right away. The disadvantage is that if instances accumulate, so too will the accumulated method results. They can grow without bound.
The lru_cache approach works with methods that have hashable arguments. It creates a reference to the instance unless special efforts are made to pass in weak references.
The advantage of the least recently used algorithm is that the cache is bounded by the specified maxsize. The disadvantage is that instances are kept alive until they age out of the cache or until the cache is cleared.
This example shows the various techniques:
class Weather:
"Lookup weather information on a government website"
def __init__(self, station_id):
self._station_id = station_id
# The _station_id is private and immutable
def current_temperature(self):
"Latest hourly observation"
# Do not cache this because old results
# can be out of date.
@cached_property
def location(self):
"Return the longitude/latitude coordinates of the station"
# Result only depends on the station_id
@lru_cache(maxsize=20)
def historic_rainfall(self, date, units='mm'):
"Rainfall on a given date"
# Depends on the station_id, date, and units.
The above example assumes that the station_id never changes. If the relevant instance attributes are mutable, the cached_property approach can’t be made to work because it cannot detect changes to the attributes.
To make the lru_cache approach work when the station_id is mutable,
the class needs to define the __eq__()
and __hash__()
methods so that the cache can detect relevant attribute updates:
class Weather:
"Example with a mutable station identifier"
def __init__(self, station_id):
self.station_id = station_id
def change_station(self, station_id):
self.station_id = station_id
def __eq__(self, other):
return self.station_id == other.station_id
def __hash__(self):
return hash(self.station_id)
@lru_cache(maxsize=20)
def historic_rainfall(self, date, units='cm'):
'Rainfall on a given date'
# Depends on the station_id, date, and units.
Modules¶
How do I create a .pyc file?¶
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 .
How do I find the current module name?¶
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()
How can I have modules that mutually import each other?¶
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:
main imports
foo
Empty globals for
foo
are createdfoo
is compiled and starts executingfoo
importsbar
Empty globals for
bar
are createdbar
is compiled and starts executingbar
importsfoo
(which is a no-op since there already is a module namedfoo
)The import mechanism tries to read
foo_var
fromfoo
globals, to setbar.foo_var = foo.foo_var
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:
exports (globals, functions, and classes that don’t need imported base classes)
import
statementsactive code (including globals that are initialized from imported values).
Van 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.
__import__(‘x.y.z’) returns <module ‘x’>; how do I get z?¶
Consider using the convenience function import_module()
from
importlib
instead:
z = importlib.import_module('x.y.z')
When I edit an imported module and reimport it, the changes don’t show up. Why does this happen?¶
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'

File
, line
, in
:
File
, line
:
File
, line
:
:
:
:
:


General Python FAQ¶
General Information¶
What is Python?¶
Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. It supports multiple programming paradigms beyond object-oriented programming, such as procedural and functional programming. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++. It is also usable as an extension language for applications that need a programmable interface. Finally, Python is portable: it runs on many Unix variants including Linux and macOS, and on Windows.
To find out more, start with The Python Tutorial. The Beginner’s Guide to Python links to other introductory tutorials and resources for learning Python.
What is the Python Software Foundation?¶
The Python Software Foundation is an independent non-profit organization that holds the copyright on Python versions 2.1 and newer. The PSF’s mission is to advance open source technology related to the Python programming language and to publicize the use of Python. The PSF’s home page is at https://www.python.org/psf/.
Donations to the PSF are tax-exempt in the US. If you use Python and find it helpful, please contribute via the PSF donation page.
Are there copyright restrictions on the use of Python?¶
You can do anything you want with the source, as long as you leave the copyrights in and display those copyrights in any documentation about Python that you produce. If you honor the copyright rules, it’s OK to use Python for commercial use, to sell copies of Python in source or binary form (modified or unmodified), or to sell products that incorporate Python in some form. We would still like to know about all commercial use of Python, of course.
See the license page to find further explanations and the full text of the PSF License.
The Python logo is trademarked, and in certain cases permission is required to use it. Consult the Trademark Usage Policy for more information.
Why was Python created in the first place?¶
Here’s a very brief summary of what started it all, written by Guido van Rossum:
I had extensive experience with implementing an interpreted language in the ABC group at CWI, and from working with this group I had learned a lot about language design. This is the origin of many Python features, including the use of indentation for statement grouping and the inclusion of very-high-level data types (although the details are all different in Python).
I had a number of gripes about the ABC language, but also liked many of its features. It was impossible to extend the ABC language (or its implementation) to remedy my complaints – in fact its lack of extensibility was one of its biggest problems. I had some experience with using Modula-2+ and talked with the designers of Modula-3 and read the Modula-3 report. Modula-3 is the origin of the syntax and semantics used for exceptions, and some other Python features.
I was working in the Amoeba distributed operating system group at CWI. We needed a better way to do system administration than by writing either C programs or Bourne shell scripts, since Amoeba had its own system call interface which wasn’t easily accessible from the Bourne shell. My experience with error handling in Amoeba made me acutely aware of the importance of exceptions as a programming language feature.
It occurred to me that a scripting language with a syntax like ABC but with access to the Amoeba system calls would fill the need. I realized that it would be foolish to write an Amoeba-specific language, so I decided that I needed a language that was generally extensible.
During the 1989 Christmas holidays, I had a lot of time on my hand, so I decided to give it a try. During the next year, while still mostly working on it in my own time, Python was used in the Amoeba project with increasing success, and the feedback from colleagues made me add many early improvements.
In February 1991, after just over a year of development, I decided to post to USENET. The rest is in the
Misc/HISTORY
file.
What is Python good for?¶
Python is a high-level general-purpose programming language that can be applied to many different classes of problems.
The language comes with a large standard library that covers areas such as string processing (regular expressions, Unicode, calculating differences between files), internet protocols (HTTP, FTP, SMTP, XML-RPC, POP, IMAP), software engineering (unit testing, logging, profiling, parsing Python code), and operating system interfaces (system calls, filesystems, TCP/IP sockets). Look at the table of contents for The Python Standard Library to get an idea of what’s available. A wide variety of third-party extensions are also available. Consult the Python Package Index to find packages of interest to you.
How does the Python version numbering scheme work?¶
Python versions are numbered “A.B.C” or “A.B”:
A is the major version number – it is only incremented for really major changes in the language.
B is the minor version number – it is incremented for less earth-shattering changes.
C is the micro version number – it is incremented for each bugfix release.
Not all releases are bugfix releases. In the run-up to a new feature release, a series of development releases are made, denoted as alpha, beta, or release candidate. Alphas are early releases in which interfaces aren’t yet finalized; it’s not unexpected to see an interface change between two alpha releases. Betas are more stable, preserving existing interfaces but possibly adding new modules, and release candidates are frozen, making no changes except as needed to fix critical bugs.
Alpha, beta and release candidate versions have an additional suffix:
The suffix for an alpha version is “aN” for some small number N.
The suffix for a beta version is “bN” for some small number N.
The suffix for a release candidate version is “rcN” for some small number N.
In other words, all versions labeled 2.0aN precede the versions labeled 2.0bN, which precede versions labeled 2.0rcN, and those precede 2.0.
You may also find version numbers with a “+” suffix, e.g. “2.2+”. These are unreleased versions, built directly from the CPython development repository. In practice, after a final minor release is made, the version is incremented to the next minor version, which becomes the “a0” version, e.g. “2.4a0”.
See the Developer’s Guide
for more information about the development cycle, and
PEP 387 to learn more about Python’s backward compatibility policy. See also
the documentation for sys.version
, sys.hexversion
, and
sys.version_info
.
How do I obtain a copy of the Python source?¶
The latest Python source distribution is always available from python.org, at https://www.python.org/downloads/. The latest development sources can be obtained at https://github.com/python/cpython/.
The source distribution is a gzipped tar file containing the complete C source, Sphinx-formatted documentation, Python library modules, example programs, and several useful pieces of freely distributable software. The source will compile and run out of the box on most UNIX platforms.
Consult the Getting Started section of the Python Developer’s Guide for more information on getting the source code and compiling it.
How do I get documentation on Python?¶
The standard documentation for the current stable version of Python is available at https://docs.python.org/3/. EPUB, plain text, and downloadable HTML versions are also available at https://docs.python.org/3/download.html.
The documentation is written in reStructuredText and processed by the Sphinx documentation tool. The reStructuredText source for the documentation is part of the Python source distribution.
I’ve never programmed before. Is there a Python tutorial?¶
There are numerous tutorials and books available. The standard documentation includes The Python Tutorial.
Consult the Beginner’s Guide to find information for beginning Python programmers, including lists of tutorials.
Is there a newsgroup or mailing list devoted to Python?¶
There is a newsgroup, comp.lang.python, and a mailing list, python-list. The newsgroup and mailing list are gatewayed into each other – if you can read news it’s unnecessary to subscribe to the mailing list. comp.lang.python is high-traffic, receiving hundreds of postings every day, and Usenet readers are often more able to cope with this volume.
Announcements of new software releases and events can be found in comp.lang.python.announce, a low-traffic moderated list that receives about five postings per day. It’s available as the python-announce mailing list.
More info about other mailing lists and newsgroups can be found at https://www.python.org/community/lists/.
How do I get a beta test version of Python?¶
Alpha and beta releases are available from https://www.python.org/downloads/. All releases are announced on the comp.lang.python and comp.lang.python.announce newsgroups and on the Python home page at https://www.python.org/; an RSS feed of news is available.
You can also access the development version of Python through Git. See The Python Developer’s Guide for details.
How do I submit bug reports and patches for Python?¶
To report a bug or submit a patch, use the issue tracker at https://github.com/python/cpython/issues.
For more information on how Python is developed, consult the Python Developer’s Guide.
Are there any published articles about Python that I can reference?¶
It’s probably best to cite your favorite book about Python.
The very first article about Python was written in 1991 and is now quite outdated.
Guido van Rossum and Jelke de Boer, “Interactively Testing Remote Servers Using the Python Programming Language”, CWI Quarterly, Volume 4, Issue 4 (December 1991), Amsterdam, pp 283–303.
Are there any books on Python?¶
Yes, there are many, and more are being published. See the python.org wiki at https://wiki.python.org/moin/PythonBooks for a list.
You can also search online bookstores for “Python” and filter out the Monty Python references; or perhaps search for “Python” and “language”.
Where in the world is www.python.org located?¶
The Python project’s infrastructure is located all over the world and is managed by the Python Infrastructure Team. Details here.
Why is it called Python?¶
When he began implementing Python, Guido van Rossum was also reading the published scripts from “Monty Python’s Flying Circus”, a BBC comedy series from the 1970s. Van Rossum thought he needed a name that was short, unique, and slightly mysterious, so he decided to call the language Python.
Do I have to like “Monty Python’s Flying Circus”?¶
No, but it helps. :)
Python in the real world¶
How stable is Python?¶
Very stable. New, stable releases have been coming out roughly every 6 to 18 months since 1991, and this seems likely to continue. As of version 3.9, Python will have a new feature release every 12 months (PEP 602).
The developers issue bugfix releases of older versions, so the stability of existing releases gradually improves. Bugfix releases, indicated by a third component of the version number (e.g. 3.5.3, 3.6.2), are managed for stability; only fixes for known problems are included in a bugfix release, and it’s guaranteed that interfaces will remain the same throughout a series of bugfix releases.
The latest stable releases can always be found on the Python download page. Python 3.x is the recommended version and supported by most widely used libraries. Python 2.x is not maintained anymore.
How many people are using Python?¶
There are probably millions of users, though it’s difficult to obtain an exact count.
Python is available for free download, so there are no sales figures, and it’s available from many different sites and packaged with many Linux distributions, so download statistics don’t tell the whole story either.
The comp.lang.python newsgroup is very active, but not all Python users post to the group or even read it.
Have any significant projects been done in Python?¶
See https://www.python.org/about/success for a list of projects that use Python. Consulting the proceedings for past Python conferences will reveal contributions from many different companies and organizations.
High-profile Python projects include the Mailman mailing list manager and the Zope application server. Several Linux distributions, most notably Red Hat, have written part or all of their installer and system administration software in Python. Companies that use Python internally include Google, Yahoo, and Lucasfilm Ltd.
What new developments are expected for Python in the future?¶
See https://peps.python.org/ for the Python Enhancement Proposals (PEPs). PEPs are design documents describing a suggested new feature for Python, providing a concise technical specification and a rationale. Look for a PEP titled “Python X.Y Release Schedule”, where X.Y is a version that hasn’t been publicly released yet.
New development is discussed on the python-dev mailing list.
Is it reasonable to propose incompatible changes to Python?¶
In general, no. There are already millions of lines of Python code around the world, so any change in the language that invalidates more than a very small fraction of existing programs has to be frowned upon. Even if you can provide a conversion program, there’s still the problem of updating all documentation; many books have been written about Python, and we don’t want to invalidate them all at a single stroke.
Providing a gradual upgrade path is necessary if a feature has to be changed. PEP 5 describes the procedure followed for introducing backward-incompatible changes while minimizing disruption for users.
Is Python a good language for beginning programmers?¶
Yes.
It is still common to start students with a procedural and statically typed language such as Pascal, C, or a subset of C++ or Java. Students may be better served by learning Python as their first language. Python has a very simple and consistent syntax and a large standard library and, most importantly, using Python in a beginning programming course lets students concentrate on important programming skills such as problem decomposition and data type design. With Python, students can be quickly introduced to basic concepts such as loops and procedures. They can probably even work with user-defined objects in their very first course.
For a student who has never programmed before, using a statically typed language seems unnatural. It presents additional complexity that the student must master and slows the pace of the course. The students are trying to learn to think like a computer, decompose problems, design consistent interfaces, and encapsulate data. While learning to use a statically typed language is important in the long term, it is not necessarily the best topic to address in the students’ first programming course.
Many other aspects of Python make it a good first language. Like Java, Python has a large standard library so that students can be assigned programming projects very early in the course that do something. Assignments aren’t restricted to the standard four-function calculator and check balancing programs. By using the standard library, students can gain the satisfaction of working on realistic applications as they learn the fundamentals of programming. Using the standard library also teaches students about code reuse. Third-party modules such as PyGame are also helpful in extending the students’ reach.
Python’s interactive interpreter enables students to test language features while they’re programming. They can keep a window with the interpreter running while they enter their program’s source in another window. If they can’t remember the methods for a list, they can do something like this:
>>> L = []
>>> dir(L)
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__',
'__dir__', '__doc__', '__eq__', '__format__', '__ge__',
'__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__',
'__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__',
'__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
'__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__',
'__sizeof__', '__str__', '__subclasshook__', 'append', 'clear',
'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove',
'reverse', 'sort']
>>> [d for d in dir(L) if '__' not in d]
['append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
>>> help(L.append)
Help on built-in function append:
append(...)
L.append(object) -> None -- append object to end
>>> L.append(1)
>>> L
[1]
With the interpreter, documentation is never far from the student as they are programming.
There are also good IDEs for Python. IDLE is a cross-platform IDE for Python that is written in Python using Tkinter. Emacs users will be happy to know that there is a very good Python mode for Emacs. All of these programming environments provide syntax highlighting, auto-indenting, and access to the interactive interpreter while coding. Consult the Python wiki for a full list of Python editing environments.
If you want to discuss Python’s use in education, you may be interested in joining the edu-sig mailing list.