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.. _tut-appendix:
********
Appendix
********
.. _tut-interac:
Interactive Mode
================
.. _tut-error:
Error Handling
--------------
When an error occurs, the interpreter prints an error message and a stack trace.
In interactive mode, it then returns to the primary prompt; when input came from
a file, it exits with a nonzero exit status after printing the stack trace.
(Exceptions handled by an :keyword:`except` clause in a :keyword:`try` statement
are not errors in this context.) Some errors are unconditionally fatal and
cause an exit with a nonzero exit; this applies to internal inconsistencies and
some cases of running out of memory. All error messages are written to the
standard error stream; normal output from executed commands is written to
standard output.
Typing the interrupt character (usually :kbd:`Control-C` or :kbd:`Delete`) to the primary or
secondary prompt cancels the input and returns to the primary prompt. [#]_
Typing an interrupt while a command is executing raises the
:exc:`KeyboardInterrupt` exception, which may be handled by a :keyword:`try`
statement.
.. _tut-scripts:
Executable Python Scripts
-------------------------
On BSD'ish Unix systems, Python scripts can be made directly executable, like
shell scripts, by putting the line ::
#!/usr/bin/env python3.5
(assuming that the interpreter is on the user's :envvar:`PATH`) at the beginning
of the script and giving the file an executable mode. The ``#!`` must be the
first two characters of the file. On some platforms, this first line must end
with a Unix-style line ending (``'\n'``), not a Windows (``'\r\n'``) line
ending. Note that the hash, or pound, character, ``'#'``, is used to start a
comment in Python.
The script can be given an executable mode, or permission, using the
:program:`chmod` command.
.. code-block:: shell-session
$ chmod +x myscript.py
On Windows systems, there is no notion of an "executable mode". The Python
installer automatically associates ``.py`` files with ``python.exe`` so that
a double-click on a Python file will run it as a script. The extension can
also be ``.pyw``, in that case, the console window that normally appears is
suppressed.
.. _tut-startup:
The Interactive Startup File
----------------------------
When you use Python interactively, it is frequently handy to have some standard
commands executed every time the interpreter is started. You can do this by
setting an environment variable named :envvar:`PYTHONSTARTUP` to the name of a
file containing your start-up commands. This is similar to the :file:`.profile`
feature of the Unix shells.
This file is only read in interactive sessions, not when Python reads commands
from a script, and not when :file:`/dev/tty` is given as the explicit source of
commands (which otherwise behaves like an interactive session). It is executed
in the same namespace where interactive commands are executed, so that objects
that it defines or imports can be used without qualification in the interactive
session. You can also change the prompts ``sys.ps1`` and ``sys.ps2`` in this
file.
If you want to read an additional start-up file from the current directory, you
can program this in the global start-up file using code like ``if
os.path.isfile('.pythonrc.py'): exec(open('.pythonrc.py').read())``.
If you want to use the startup file in a script, you must do this explicitly
in the script::
import os
filename = os.environ.get('PYTHONSTARTUP')
if filename and os.path.isfile(filename):
with open(filename) as fobj:
startup_file = fobj.read()
exec(startup_file)
.. _tut-customize:
The Customization Modules
-------------------------
Python provides two hooks to let you customize it: :mod:`sitecustomize` and
:mod:`usercustomize`. To see how it works, you need first to find the location
of your user site-packages directory. Start Python and run this code::
>>> import site
>>> site.getusersitepackages()
'/home/user/.local/lib/python3.5/site-packages'
Now you can create a file named :file:`usercustomize.py` in that directory and
put anything you want in it. It will affect every invocation of Python, unless
it is started with the :option:`-s` option to disable the automatic import.
:mod:`sitecustomize` works in the same way, but is typically created by an
administrator of the computer in the global site-packages directory, and is
imported before :mod:`usercustomize`. See the documentation of the :mod:`site`
module for more details.
.. rubric:: Footnotes
.. [#] A problem with the GNU Readline package may prevent this.

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.. _tut-intro:
**********************
Whetting Your Appetite
**********************
If you do much work on computers, eventually you find that there's some task
you'd like to automate. For example, you may wish to perform a
search-and-replace over a large number of text files, or rename and rearrange a
bunch of photo files in a complicated way. Perhaps you'd like to write a small
custom database, or a specialized GUI application, or a simple game.
If you're a professional software developer, you may have to work with several
C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is
too slow. Perhaps you're writing a test suite for such a library and find
writing the testing code a tedious task. Or maybe you've written a program that
could use an extension language, and you don't want to design and implement a
whole new language for your application.
Python is just the language for you.
You could write a Unix shell script or Windows batch files for some of these
tasks, but shell scripts are best at moving around files and changing text data,
not well-suited for GUI applications or games. You could write a C/C++/Java
program, but it can take a lot of development time to get even a first-draft
program. Python is simpler to use, available on Windows, Mac OS X, and Unix
operating systems, and will help you get the job done more quickly.
Python is simple to use, but it is a real programming language, offering much
more structure and support for large programs than shell scripts or batch files
can offer. On the other hand, Python also offers much more error checking than
C, and, being a *very-high-level language*, it has high-level data types built
in, such as flexible arrays and dictionaries. Because of its more general data
types Python is applicable to a much larger problem domain than Awk or even
Perl, yet many things are at least as easy in Python as in those languages.
Python allows you to split your program into modules that can be reused in other
Python programs. It comes with a large collection of standard modules that you
can use as the basis of your programs --- or as examples to start learning to
program in Python. Some of these modules provide things like file I/O, system
calls, sockets, and even interfaces to graphical user interface toolkits like
Tk.
Python is an interpreted language, which can save you considerable time during
program development because no compilation and linking is necessary. The
interpreter can be used interactively, which makes it easy to experiment with
features of the language, to write throw-away programs, or to test functions
during bottom-up program development. It is also a handy desk calculator.
Python enables programs to be written compactly and readably. Programs written
in Python are typically much shorter than equivalent C, C++, or Java programs,
for several reasons:
* the high-level data types allow you to express complex operations in a single
statement;
* statement grouping is done by indentation instead of beginning and ending
brackets;
* no variable or argument declarations are necessary.
Python is *extensible*: if you know how to program in C it is easy to add a new
built-in function or module to the interpreter, either to perform critical
operations at maximum speed, or to link Python programs to libraries that may
only be available in binary form (such as a vendor-specific graphics library).
Once you are really hooked, you can link the Python interpreter into an
application written in C and use it as an extension or command language for that
application.
By the way, the language is named after the BBC show "Monty Python's Flying
Circus" and has nothing to do with reptiles. Making references to Monty
Python skits in documentation is not only allowed, it is encouraged!
Now that you are all excited about Python, you'll want to examine it in some
more detail. Since the best way to learn a language is to use it, the tutorial
invites you to play with the Python interpreter as you read.
In the next chapter, the mechanics of using the interpreter are explained. This
is rather mundane information, but essential for trying out the examples shown
later.
The rest of the tutorial introduces various features of the Python language and
system through examples, beginning with simple expressions, statements and data
types, through functions and modules, and finally touching upon advanced
concepts like exceptions and user-defined classes.

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.. _tut-classes:
*******
Classes
*******
Classes provide a means of bundling data and functionality together. Creating
a new class creates a new *type* of object, allowing new *instances* of that
type to be made. Each class instance can have attributes attached to it for
maintaining its state. Class instances can also have methods (defined by its
class) for modifying its state.
Compared with other programming languages, Python's class mechanism adds classes
with a minimum of new syntax and semantics. It is a mixture of the class
mechanisms found in C++ and Modula-3. Python classes provide all the standard
features of Object Oriented Programming: the class inheritance mechanism allows
multiple base classes, a derived class can override any methods of its base
class or classes, and a method can call the method of a base class with the same
name. Objects can contain arbitrary amounts and kinds of data. As is true for
modules, classes partake of the dynamic nature of Python: they are created at
runtime, and can be modified further after creation.
In C++ terminology, normally class members (including the data members) are
*public* (except see below :ref:`tut-private`), and all member functions are
*virtual*. As in Modula-3, there are no shorthands for referencing the object's
members from its methods: the method function is declared with an explicit first
argument representing the object, which is provided implicitly by the call. As
in Smalltalk, classes themselves are objects. This provides semantics for
importing and renaming. Unlike C++ and Modula-3, built-in types can be used as
base classes for extension by the user. Also, like in C++, most built-in
operators with special syntax (arithmetic operators, subscripting etc.) can be
redefined for class instances.
(Lacking universally accepted terminology to talk about classes, I will make
occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since
its object-oriented semantics are closer to those of Python than C++, but I
expect that few readers have heard of it.)
.. _tut-object:
A Word About Names and Objects
==============================
Objects have individuality, and multiple names (in multiple scopes) can be bound
to the same object. This is known as aliasing in other languages. This is
usually not appreciated on a first glance at Python, and can be safely ignored
when dealing with immutable basic types (numbers, strings, tuples). However,
aliasing has a possibly surprising effect on the semantics of Python code
involving mutable objects such as lists, dictionaries, and most other types.
This is usually used to the benefit of the program, since aliases behave like
pointers in some respects. For example, passing an object is cheap since only a
pointer is passed by the implementation; and if a function modifies an object
passed as an argument, the caller will see the change --- this eliminates the
need for two different argument passing mechanisms as in Pascal.
.. _tut-scopes:
Python Scopes and Namespaces
============================
Before introducing classes, I first have to tell you something about Python's
scope rules. Class definitions play some neat tricks with namespaces, and you
need to know how scopes and namespaces work to fully understand what's going on.
Incidentally, knowledge about this subject is useful for any advanced Python
programmer.
Let's begin with some definitions.
A *namespace* is a mapping from names to objects. Most namespaces are currently
implemented as Python dictionaries, but that's normally not noticeable in any
way (except for performance), and it may change in the future. Examples of
namespaces are: the set of built-in names (containing functions such as :func:`abs`, and
built-in exception names); the global names in a module; and the local names in
a function invocation. In a sense the set of attributes of an object also form
a namespace. The important thing to know about namespaces is that there is
absolutely no relation between names in different namespaces; for instance, two
different modules may both define a function ``maximize`` without confusion ---
users of the modules must prefix it with the module name.
By the way, I use the word *attribute* for any name following a dot --- for
example, in the expression ``z.real``, ``real`` is an attribute of the object
``z``. Strictly speaking, references to names in modules are attribute
references: in the expression ``modname.funcname``, ``modname`` is a module
object and ``funcname`` is an attribute of it. In this case there happens to be
a straightforward mapping between the module's attributes and the global names
defined in the module: they share the same namespace! [#]_
Attributes may be read-only or writable. In the latter case, assignment to
attributes is possible. Module attributes are writable: you can write
``modname.the_answer = 42``. Writable attributes may also be deleted with the
:keyword:`del` statement. For example, ``del modname.the_answer`` will remove
the attribute :attr:`the_answer` from the object named by ``modname``.
Namespaces are created at different moments and have different lifetimes. The
namespace containing the built-in names is created when the Python interpreter
starts up, and is never deleted. The global namespace for a module is created
when the module definition is read in; normally, module namespaces also last
until the interpreter quits. The statements executed by the top-level
invocation of the interpreter, either read from a script file or interactively,
are considered part of a module called :mod:`__main__`, so they have their own
global namespace. (The built-in names actually also live in a module; this is
called :mod:`builtins`.)
The local namespace for a function is created when the function is called, and
deleted when the function returns or raises an exception that is not handled
within the function. (Actually, forgetting would be a better way to describe
what actually happens.) Of course, recursive invocations each have their own
local namespace.
A *scope* is a textual region of a Python program where a namespace is directly
accessible. "Directly accessible" here means that an unqualified reference to a
name attempts to find the name in the namespace.
Although scopes are determined statically, they are used dynamically. At any
time during execution, there are at least three nested scopes whose namespaces
are directly accessible:
* the innermost scope, which is searched first, contains the local names
* the scopes of any enclosing functions, which are searched starting with the
nearest enclosing scope, contains non-local, but also non-global names
* the next-to-last scope contains the current module's global names
* the outermost scope (searched last) is the namespace containing built-in names
If a name is declared global, then all references and assignments go directly to
the middle scope containing the module's global names. To rebind variables
found outside of the innermost scope, the :keyword:`nonlocal` statement can be
used; if not declared nonlocal, those variables are read-only (an attempt to
write to such a variable will simply create a *new* local variable in the
innermost scope, leaving the identically named outer variable unchanged).
Usually, the local scope references the local names of the (textually) current
function. Outside functions, the local scope references the same namespace as
the global scope: the module's namespace. Class definitions place yet another
namespace in the local scope.
It is important to realize that scopes are determined textually: the global
scope of a function defined in a module is that module's namespace, no matter
from where or by what alias the function is called. On the other hand, the
actual search for names is done dynamically, at run time --- however, the
language definition is evolving towards static name resolution, at "compile"
time, so don't rely on dynamic name resolution! (In fact, local variables are
already determined statically.)
A special quirk of Python is that -- if no :keyword:`global` statement is in
effect -- assignments to names always go into the innermost scope. Assignments
do not copy data --- they just bind names to objects. The same is true for
deletions: the statement ``del x`` removes the binding of ``x`` from the
namespace referenced by the local scope. In fact, all operations that introduce
new names use the local scope: in particular, :keyword:`import` statements and
function definitions bind the module or function name in the local scope.
The :keyword:`global` statement can be used to indicate that particular
variables live in the global scope and should be rebound there; the
:keyword:`nonlocal` statement indicates that particular variables live in
an enclosing scope and should be rebound there.
.. _tut-scopeexample:
Scopes and Namespaces Example
-----------------------------
This is an example demonstrating how to reference the different scopes and
namespaces, and how :keyword:`global` and :keyword:`nonlocal` affect variable
binding::
def scope_test():
def do_local():
spam = "local spam"
def do_nonlocal():
nonlocal spam
spam = "nonlocal spam"
def do_global():
global spam
spam = "global spam"
spam = "test spam"
do_local()
print("After local assignment:", spam)
do_nonlocal()
print("After nonlocal assignment:", spam)
do_global()
print("After global assignment:", spam)
scope_test()
print("In global scope:", spam)
The output of the example code is:
.. code-block:: none
After local assignment: test spam
After nonlocal assignment: nonlocal spam
After global assignment: nonlocal spam
In global scope: global spam
Note how the *local* assignment (which is default) didn't change *scope_test*\'s
binding of *spam*. The :keyword:`nonlocal` assignment changed *scope_test*\'s
binding of *spam*, and the :keyword:`global` assignment changed the module-level
binding.
You can also see that there was no previous binding for *spam* before the
:keyword:`global` assignment.
.. _tut-firstclasses:
A First Look at Classes
=======================
Classes introduce a little bit of new syntax, three new object types, and some
new semantics.
.. _tut-classdefinition:
Class Definition Syntax
-----------------------
The simplest form of class definition looks like this::
class ClassName:
<statement-1>
.
.
.
<statement-N>
Class definitions, like function definitions (:keyword:`def` statements) must be
executed before they have any effect. (You could conceivably place a class
definition in a branch of an :keyword:`if` statement, or inside a function.)
In practice, the statements inside a class definition will usually be function
definitions, but other statements are allowed, and sometimes useful --- we'll
come back to this later. The function definitions inside a class normally have
a peculiar form of argument list, dictated by the calling conventions for
methods --- again, this is explained later.
When a class definition is entered, a new namespace is created, and used as the
local scope --- thus, all assignments to local variables go into this new
namespace. In particular, function definitions bind the name of the new
function here.
When a class definition is left normally (via the end), a *class object* is
created. This is basically a wrapper around the contents of the namespace
created by the class definition; we'll learn more about class objects in the
next section. The original local scope (the one in effect just before the class
definition was entered) is reinstated, and the class object is bound here to the
class name given in the class definition header (:class:`ClassName` in the
example).
.. _tut-classobjects:
Class Objects
-------------
Class objects support two kinds of operations: attribute references and
instantiation.
*Attribute references* use the standard syntax used for all attribute references
in Python: ``obj.name``. Valid attribute names are all the names that were in
the class's namespace when the class object was created. So, if the class
definition looked like this::
class MyClass:
"""A simple example class"""
i = 12345
def f(self):
return 'hello world'
then ``MyClass.i`` and ``MyClass.f`` are valid attribute references, returning
an integer and a function object, respectively. Class attributes can also be
assigned to, so you can change the value of ``MyClass.i`` by assignment.
:attr:`__doc__` is also a valid attribute, returning the docstring belonging to
the class: ``"A simple example class"``.
Class *instantiation* uses function notation. Just pretend that the class
object is a parameterless function that returns a new instance of the class.
For example (assuming the above class)::
x = MyClass()
creates a new *instance* of the class and assigns this object to the local
variable ``x``.
The instantiation operation ("calling" a class object) creates an empty object.
Many classes like to create objects with instances customized to a specific
initial state. Therefore a class may define a special method named
:meth:`__init__`, like this::
def __init__(self):
self.data = []
When a class defines an :meth:`__init__` method, class instantiation
automatically invokes :meth:`__init__` for the newly-created class instance. So
in this example, a new, initialized instance can be obtained by::
x = MyClass()
Of course, the :meth:`__init__` method may have arguments for greater
flexibility. In that case, arguments given to the class instantiation operator
are passed on to :meth:`__init__`. For example, ::
>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
.. _tut-instanceobjects:
Instance Objects
----------------
Now what can we do with instance objects? The only operations understood by
instance objects are attribute references. There are two kinds of valid
attribute names: data attributes and methods.
*data attributes* correspond to "instance variables" in Smalltalk, and to "data
members" in C++. Data attributes need not be declared; like local variables,
they spring into existence when they are first assigned to. For example, if
``x`` is the instance of :class:`MyClass` created above, the following piece of
code will print the value ``16``, without leaving a trace::
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print(x.counter)
del x.counter
The other kind of instance attribute reference is a *method*. A method is a
function that "belongs to" an object. (In Python, the term method is not unique
to class instances: other object types can have methods as well. For example,
list objects have methods called append, insert, remove, sort, and so on.
However, in the following discussion, we'll use the term method exclusively to
mean methods of class instance objects, unless explicitly stated otherwise.)
.. index:: object: method
Valid method names of an instance object depend on its class. By definition,
all attributes of a class that are function objects define corresponding
methods of its instances. So in our example, ``x.f`` is a valid method
reference, since ``MyClass.f`` is a function, but ``x.i`` is not, since
``MyClass.i`` is not. But ``x.f`` is not the same thing as ``MyClass.f`` --- it
is a *method object*, not a function object.
.. _tut-methodobjects:
Method Objects
--------------
Usually, a method is called right after it is bound::
x.f()
In the :class:`MyClass` example, this will return the string ``'hello world'``.
However, it is not necessary to call a method right away: ``x.f`` is a method
object, and can be stored away and called at a later time. For example::
xf = x.f
while True:
print(xf())
will continue to print ``hello world`` until the end of time.
What exactly happens when a method is called? You may have noticed that
``x.f()`` was called without an argument above, even though the function
definition for :meth:`f` specified an argument. What happened to the argument?
Surely Python raises an exception when a function that requires an argument is
called without any --- even if the argument isn't actually used...
Actually, you may have guessed the answer: the special thing about methods is
that the instance object is passed as the first argument of the function. In our
example, the call ``x.f()`` is exactly equivalent to ``MyClass.f(x)``. In
general, calling a method with a list of *n* arguments is equivalent to calling
the corresponding function with an argument list that is created by inserting
the method's instance object before the first argument.
If you still don't understand how methods work, a look at the implementation can
perhaps clarify matters. When a non-data attribute of an instance is
referenced, the instance's class is searched. If the name denotes a valid class
attribute that is a function object, a method object is created by packing
(pointers to) the instance object and the function object just found together in
an abstract object: this is the method object. When the method object is called
with an argument list, a new argument list is constructed from the instance
object and the argument list, and the function object is called with this new
argument list.
.. _tut-class-and-instance-variables:
Class and Instance Variables
----------------------------
Generally speaking, instance variables are for data unique to each instance
and class variables are for attributes and methods shared by all instances
of the class::
class Dog:
kind = 'canine' # class variable shared by all instances
def __init__(self, name):
self.name = name # instance variable unique to each instance
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind # shared by all dogs
'canine'
>>> e.kind # shared by all dogs
'canine'
>>> d.name # unique to d
'Fido'
>>> e.name # unique to e
'Buddy'
As discussed in :ref:`tut-object`, shared data can have possibly surprising
effects with involving :term:`mutable` objects such as lists and dictionaries.
For example, the *tricks* list in the following code should not be used as a
class variable because just a single list would be shared by all *Dog*
instances::
class Dog:
tricks = [] # mistaken use of a class variable
def __init__(self, name):
self.name = name
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks # unexpectedly shared by all dogs
['roll over', 'play dead']
Correct design of the class should use an instance variable instead::
class Dog:
def __init__(self, name):
self.name = name
self.tricks = [] # creates a new empty list for each dog
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks
['roll over']
>>> e.tricks
['play dead']
.. _tut-remarks:
Random Remarks
==============
.. These should perhaps be placed more carefully...
Data attributes override method attributes with the same name; to avoid
accidental name conflicts, which may cause hard-to-find bugs in large programs,
it is wise to use some kind of convention that minimizes the chance of
conflicts. Possible conventions include capitalizing method names, prefixing
data attribute names with a small unique string (perhaps just an underscore), or
using verbs for methods and nouns for data attributes.
Data attributes may be referenced by methods as well as by ordinary users
("clients") of an object. In other words, classes are not usable to implement
pure abstract data types. In fact, nothing in Python makes it possible to
enforce data hiding --- it is all based upon convention. (On the other hand,
the Python implementation, written in C, can completely hide implementation
details and control access to an object if necessary; this can be used by
extensions to Python written in C.)
Clients should use data attributes with care --- clients may mess up invariants
maintained by the methods by stamping on their data attributes. Note that
clients may add data attributes of their own to an instance object without
affecting the validity of the methods, as long as name conflicts are avoided ---
again, a naming convention can save a lot of headaches here.
There is no shorthand for referencing data attributes (or other methods!) from
within methods. I find that this actually increases the readability of methods:
there is no chance of confusing local variables and instance variables when
glancing through a method.
Often, the first argument of a method is called ``self``. This is nothing more
than a convention: the name ``self`` has absolutely no special meaning to
Python. Note, however, that by not following the convention your code may be
less readable to other Python programmers, and it is also conceivable that a
*class browser* program might be written that relies upon such a convention.
Any function object that is a class attribute defines a method for instances of
that class. It is not necessary that the function definition is textually
enclosed in the class definition: assigning a function object to a local
variable in the class is also ok. For example::
# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)
class C:
f = f1
def g(self):
return 'hello world'
h = g
Now ``f``, ``g`` and ``h`` are all attributes of class :class:`C` that refer to
function objects, and consequently they are all methods of instances of
:class:`C` --- ``h`` being exactly equivalent to ``g``. Note that this practice
usually only serves to confuse the reader of a program.
Methods may call other methods by using method attributes of the ``self``
argument::
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
Methods may reference global names in the same way as ordinary functions. The
global scope associated with a method is the module containing its
definition. (A class is never used as a global scope.) While one
rarely encounters a good reason for using global data in a method, there are
many legitimate uses of the global scope: for one thing, functions and modules
imported into the global scope can be used by methods, as well as functions and
classes defined in it. Usually, the class containing the method is itself
defined in this global scope, and in the next section we'll find some good
reasons why a method would want to reference its own class.
Each value is an object, and therefore has a *class* (also called its *type*).
It is stored as ``object.__class__``.
.. _tut-inheritance:
Inheritance
===========
Of course, a language feature would not be worthy of the name "class" without
supporting inheritance. The syntax for a derived class definition looks like
this::
class DerivedClassName(BaseClassName):
<statement-1>
.
.
.
<statement-N>
The name :class:`BaseClassName` must be defined in a scope containing the
derived class definition. In place of a base class name, other arbitrary
expressions are also allowed. This can be useful, for example, when the base
class is defined in another module::
class DerivedClassName(modname.BaseClassName):
Execution of a derived class definition proceeds the same as for a base class.
When the class object is constructed, the base class is remembered. This is
used for resolving attribute references: if a requested attribute is not found
in the class, the search proceeds to look in the base class. This rule is
applied recursively if the base class itself is derived from some other class.
There's nothing special about instantiation of derived classes:
``DerivedClassName()`` creates a new instance of the class. Method references
are resolved as follows: the corresponding class attribute is searched,
descending down the chain of base classes if necessary, and the method reference
is valid if this yields a function object.
Derived classes may override methods of their base classes. Because methods
have no special privileges when calling other methods of the same object, a
method of a base class that calls another method defined in the same base class
may end up calling a method of a derived class that overrides it. (For C++
programmers: all methods in Python are effectively ``virtual``.)
An overriding method in a derived class may in fact want to extend rather than
simply replace the base class method of the same name. There is a simple way to
call the base class method directly: just call ``BaseClassName.methodname(self,
arguments)``. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as ``BaseClassName`` in the global
scope.)
Python has two built-in functions that work with inheritance:
* Use :func:`isinstance` to check an instance's type: ``isinstance(obj, int)``
will be ``True`` only if ``obj.__class__`` is :class:`int` or some class
derived from :class:`int`.
* Use :func:`issubclass` to check class inheritance: ``issubclass(bool, int)``
is ``True`` since :class:`bool` is a subclass of :class:`int`. However,
``issubclass(float, int)`` is ``False`` since :class:`float` is not a
subclass of :class:`int`.
.. _tut-multiple:
Multiple Inheritance
--------------------
Python supports a form of multiple inheritance as well. A class definition with
multiple base classes looks like this::
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
.
.
.
<statement-N>
For most purposes, in the simplest cases, you can think of the search for
attributes inherited from a parent class as depth-first, left-to-right, not
searching twice in the same class where there is an overlap in the hierarchy.
Thus, if an attribute is not found in :class:`DerivedClassName`, it is searched
for in :class:`Base1`, then (recursively) in the base classes of :class:`Base1`,
and if it was not found there, it was searched for in :class:`Base2`, and so on.
In fact, it is slightly more complex than that; the method resolution order
changes dynamically to support cooperative calls to :func:`super`. This
approach is known in some other multiple-inheritance languages as
call-next-method and is more powerful than the super call found in
single-inheritance languages.
Dynamic ordering is necessary because all cases of multiple inheritance exhibit
one or more diamond relationships (where at least one of the parent classes
can be accessed through multiple paths from the bottommost class). For example,
all classes inherit from :class:`object`, so any case of multiple inheritance
provides more than one path to reach :class:`object`. To keep the base classes
from being accessed more than once, the dynamic algorithm linearizes the search
order in a way that preserves the left-to-right ordering specified in each
class, that calls each parent only once, and that is monotonic (meaning that a
class can be subclassed without affecting the precedence order of its parents).
Taken together, these properties make it possible to design reliable and
extensible classes with multiple inheritance. For more detail, see
https://www.python.org/download/releases/2.3/mro/.
.. _tut-private:
Private Variables
=================
"Private" instance variables that cannot be accessed except from inside an
object don't exist in Python. However, there is a convention that is followed
by most Python code: a name prefixed with an underscore (e.g. ``_spam``) should
be treated as a non-public part of the API (whether it is a function, a method
or a data member). It should be considered an implementation detail and subject
to change without notice.
.. index::
pair: name; mangling
Since there is a valid use-case for class-private members (namely to avoid name
clashes of names with names defined by subclasses), there is limited support for
such a mechanism, called :dfn:`name mangling`. 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 leading underscore(s) stripped. This mangling is done
without regard to the syntactic position of the identifier, as long as it
occurs within the definition of a class.
Name mangling is helpful for letting subclasses override methods without
breaking intraclass method calls. For example::
class Mapping:
def __init__(self, iterable):
self.items_list = []
self.__update(iterable)
def update(self, iterable):
for item in iterable:
self.items_list.append(item)
__update = update # private copy of original update() method
class MappingSubclass(Mapping):
def update(self, keys, values):
# provides new signature for update()
# but does not break __init__()
for item in zip(keys, values):
self.items_list.append(item)
The above example would work even if ``MappingSubclass`` were to introduce a
``__update`` identifier since it is replaced with ``_Mapping__update`` in the
``Mapping`` class and ``_MappingSubclass__update`` in the ``MappingSubclass``
class respectively.
Note that the mangling rules are designed mostly to avoid accidents; it still is
possible to access or modify a variable that is considered private. This can
even be useful in special circumstances, such as in the debugger.
Notice that code passed to ``exec()`` or ``eval()`` does not consider the
classname of the invoking class to be the current class; this is similar to the
effect of the ``global`` statement, the effect of which is likewise restricted
to code that is byte-compiled together. The same restriction applies to
``getattr()``, ``setattr()`` and ``delattr()``, as well as when referencing
``__dict__`` directly.
.. _tut-odds:
Odds and Ends
=============
Sometimes it is useful to have a data type similar to the Pascal "record" or C
"struct", bundling together a few named data items. An empty class definition
will do nicely::
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
A piece of Python code that expects a particular abstract data type can often be
passed a class that emulates the methods of that data type instead. For
instance, if you have a function that formats some data from a file object, you
can define a class with methods :meth:`read` and :meth:`!readline` that get the
data from a string buffer instead, and pass it as an argument.
.. (Unfortunately, this technique has its limitations: a class can't define
operations that are accessed by special syntax such as sequence subscripting
or arithmetic operators, and assigning such a "pseudo-file" to sys.stdin will
not cause the interpreter to read further input from it.)
Instance method objects have attributes, too: ``m.__self__`` is the instance
object with the method :meth:`m`, and ``m.__func__`` is the function object
corresponding to the method.
.. _tut-iterators:
Iterators
=========
By now you have probably noticed that most container objects can be looped over
using a :keyword:`for` statement::
for element in [1, 2, 3]:
print(element)
for element in (1, 2, 3):
print(element)
for key in {'one':1, 'two':2}:
print(key)
for char in "123":
print(char)
for line in open("myfile.txt"):
print(line, end='')
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the :keyword:`for` statement
calls :func:`iter` on the container object. The function returns an iterator
object that defines the method :meth:`~iterator.__next__` which accesses
elements in the container one at a time. When there are no more elements,
:meth:`~iterator.__next__` raises a :exc:`StopIteration` exception which tells the
:keyword:`for` loop to terminate. You can call the :meth:`~iterator.__next__` method
using the :func:`next` built-in function; this example shows how it all works::
>>> s = 'abc'
>>> it = iter(s)
>>> it
<iterator object at 0x00A1DB50>
>>> next(it)
'a'
>>> next(it)
'b'
>>> next(it)
'c'
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
next(it)
StopIteration
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an :meth:`__iter__` method which
returns an object with a :meth:`~iterator.__next__` method. If the class
defines :meth:`__next__`, then :meth:`__iter__` can just return ``self``::
class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
::
>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
... print(char)
...
m
a
p
s
.. _tut-generators:
Generators
==========
:term:`Generator`\s are a simple and powerful tool for creating iterators. They
are written like regular functions but use the :keyword:`yield` statement
whenever they want to return data. Each time :func:`next` is called on it, the
generator resumes where it left off (it remembers all the data values and which
statement was last executed). An example shows that generators can be trivially
easy to create::
def reverse(data):
for index in range(len(data)-1, -1, -1):
yield data[index]
::
>>> for char in reverse('golf'):
... print(char)
...
f
l
o
g
Anything that can be done with generators can also be done with class-based
iterators as described in the previous section. What makes generators so
compact is that the :meth:`__iter__` and :meth:`~generator.__next__` methods
are created automatically.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like ``self.index``
and ``self.data``.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise :exc:`StopIteration`. In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.
.. _tut-genexps:
Generator Expressions
=====================
Some simple generators can be coded succinctly as expressions using a syntax
similar to list comprehensions but with parentheses instead of square brackets.
These expressions are designed for situations where the generator is used right
away by an enclosing function. Generator expressions are more compact but less
versatile than full generator definitions and tend to be more memory friendly
than equivalent list comprehensions.
Examples::
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> from math import pi, sin
>>> sine_table = {x: sin(x*pi/180) for x in range(0, 91)}
>>> unique_words = set(word for line in page for word in line.split())
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']
.. rubric:: Footnotes
.. [#] Except for one thing. Module objects have a secret read-only attribute called
:attr:`~object.__dict__` which returns the dictionary used to implement the module's
namespace; the name :attr:`~object.__dict__` is an attribute but not a global name.
Obviously, using this violates the abstraction of namespace implementation, and
should be restricted to things like post-mortem debuggers.

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.. _tut-morecontrol:
***********************
More Control Flow Tools
***********************
Besides the :keyword:`while` statement just introduced, Python knows the usual
control flow statements known from other languages, with some twists.
.. _tut-if:
:keyword:`if` Statements
========================
Perhaps the most well-known statement type is the :keyword:`if` statement. For
example::
>>> x = int(input("Please enter an integer: "))
Please enter an integer: 42
>>> if x < 0:
... x = 0
... print('Negative changed to zero')
... elif x == 0:
... print('Zero')
... elif x == 1:
... print('Single')
... else:
... print('More')
...
More
There can be zero or more :keyword:`elif` parts, and the :keyword:`else` part is
optional. The keyword ':keyword:`elif`' is short for 'else if', and is useful
to avoid excessive indentation. An :keyword:`if` ... :keyword:`elif` ...
:keyword:`elif` ... sequence is a substitute for the ``switch`` or
``case`` statements found in other languages.
.. _tut-for:
:keyword:`for` Statements
=========================
.. index::
statement: for
The :keyword:`for` statement in Python differs a bit from what you may be used
to in C or Pascal. Rather than always iterating over an arithmetic progression
of numbers (like in Pascal), or giving the user the ability to define both the
iteration step and halting condition (as C), Python's :keyword:`for` statement
iterates over the items of any sequence (a list or a string), in the order that
they appear in the sequence. For example (no pun intended):
.. One suggestion was to give a real C example here, but that may only serve to
confuse non-C programmers.
::
>>> # Measure some strings:
... words = ['cat', 'window', 'defenestrate']
>>> for w in words:
... print(w, len(w))
...
cat 3
window 6
defenestrate 12
If you need to modify the sequence you are iterating over while inside the loop
(for example to duplicate selected items), it is recommended that you first
make a copy. Iterating over a sequence does not implicitly make a copy. The
slice notation makes this especially convenient::
>>> for w in words[:]: # Loop over a slice copy of the entire list.
... if len(w) > 6:
... words.insert(0, w)
...
>>> words
['defenestrate', 'cat', 'window', 'defenestrate']
With ``for w in words:``, the example would attempt to create an infinite list,
inserting ``defenestrate`` over and over again.
.. _tut-range:
The :func:`range` Function
==========================
If you do need to iterate over a sequence of numbers, the built-in function
:func:`range` comes in handy. It generates arithmetic progressions::
>>> for i in range(5):
... print(i)
...
0
1
2
3
4
The given end point is never part of the generated sequence; ``range(10)`` generates
10 values, the legal indices for items of a sequence of length 10. It
is possible to let the range start at another number, or to specify a different
increment (even negative; sometimes this is called the 'step')::
range(5, 10)
5, 6, 7, 8, 9
range(0, 10, 3)
0, 3, 6, 9
range(-10, -100, -30)
-10, -40, -70
To iterate over the indices of a sequence, you can combine :func:`range` and
:func:`len` as follows::
>>> a = ['Mary', 'had', 'a', 'little', 'lamb']
>>> for i in range(len(a)):
... print(i, a[i])
...
0 Mary
1 had
2 a
3 little
4 lamb
In most such cases, however, it is convenient to use the :func:`enumerate`
function, see :ref:`tut-loopidioms`.
A strange thing happens if you just print a range::
>>> print(range(10))
range(0, 10)
In many ways the object returned by :func:`range` behaves as if it is a list,
but in fact it isn't. It is an object which returns the successive items of
the desired sequence when you iterate over it, but it doesn't really make
the list, thus saving space.
We say such an object is *iterable*, that is, suitable as a target for
functions and constructs that expect something from which they can
obtain successive items until the supply is exhausted. We have seen that
the :keyword:`for` statement is such an *iterator*. The function :func:`list`
is another; it creates lists from iterables::
>>> list(range(5))
[0, 1, 2, 3, 4]
Later we will see more functions that return iterables and take iterables as argument.
.. _tut-break:
:keyword:`break` and :keyword:`continue` Statements, and :keyword:`else` Clauses on Loops
=========================================================================================
The :keyword:`break` statement, like in C, breaks out of the innermost enclosing
:keyword:`for` or :keyword:`while` loop.
Loop statements may have an ``else`` clause; it is executed when the loop
terminates through exhaustion of the list (with :keyword:`for`) or when the
condition becomes false (with :keyword:`while`), but not when the loop is
terminated by a :keyword:`break` statement. This is exemplified by the
following loop, which searches for prime numbers::
>>> for n in range(2, 10):
... for x in range(2, n):
... if n % x == 0:
... print(n, 'equals', x, '*', n//x)
... break
... else:
... # loop fell through without finding a factor
... print(n, 'is a prime number')
...
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
(Yes, this is the correct code. Look closely: the ``else`` clause belongs to
the :keyword:`for` loop, **not** the :keyword:`if` statement.)
When used with a loop, the ``else`` clause has more in common with the
``else`` clause of a :keyword:`try` statement than it does that of
:keyword:`if` statements: a :keyword:`try` statement's ``else`` clause runs
when no exception occurs, and a loop's ``else`` clause runs when no ``break``
occurs. For more on the :keyword:`try` statement and exceptions, see
:ref:`tut-handling`.
The :keyword:`continue` statement, also borrowed from C, continues with the next
iteration of the loop::
>>> for num in range(2, 10):
... if num % 2 == 0:
... print("Found an even number", num)
... continue
... print("Found a number", num)
Found an even number 2
Found a number 3
Found an even number 4
Found a number 5
Found an even number 6
Found a number 7
Found an even number 8
Found a number 9
.. _tut-pass:
:keyword:`pass` Statements
==========================
The :keyword:`pass` statement does nothing. It can be used when a statement is
required syntactically but the program requires no action. For example::
>>> while True:
... pass # Busy-wait for keyboard interrupt (Ctrl+C)
...
This is commonly used for creating minimal classes::
>>> class MyEmptyClass:
... pass
...
Another place :keyword:`pass` can be used is as a place-holder for a function or
conditional body when you are working on new code, allowing you to keep thinking
at a more abstract level. The :keyword:`pass` is silently ignored::
>>> def initlog(*args):
... pass # Remember to implement this!
...
.. _tut-functions:
Defining Functions
==================
We can create a function that writes the Fibonacci series to an arbitrary
boundary::
>>> def fib(n): # write Fibonacci series up to n
... """Print a Fibonacci series up to n."""
... a, b = 0, 1
... while a < n:
... print(a, end=' ')
... a, b = b, a+b
... print()
...
>>> # Now call the function we just defined:
... fib(2000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
.. index::
single: documentation strings
single: docstrings
single: strings, documentation
The keyword :keyword:`def` introduces a function *definition*. It must be
followed by the function name and the parenthesized list of formal parameters.
The statements that form the body of the function start at the next line, and
must be indented.
The first statement of the function body can optionally be a string literal;
this string literal is the function's documentation string, or :dfn:`docstring`.
(More about docstrings can be found in the section :ref:`tut-docstrings`.)
There are tools which use docstrings to automatically produce online or printed
documentation, or to let the user interactively browse through code; it's good
practice to include docstrings in code that you write, so make a habit of it.
The *execution* of a function introduces a new symbol table used for the local
variables of the function. More precisely, all variable assignments in a
function store the value in the local symbol table; whereas variable references
first look in the local symbol table, then in the local symbol tables of
enclosing functions, then in the global symbol table, and finally in the table
of built-in names. Thus, global variables cannot be directly assigned a value
within a function (unless named in a :keyword:`global` statement), although they
may be referenced.
The actual parameters (arguments) to a function call are introduced in the local
symbol table of the called function when it is called; thus, arguments are
passed using *call by value* (where the *value* is always an object *reference*,
not the value of the object). [#]_ When a function calls another function, a new
local symbol table is created for that call.
A function definition introduces the function name in the current symbol table.
The value of the function name has a type that is recognized by the interpreter
as a user-defined function. This value can be assigned to another name which
can then also be used as a function. This serves as a general renaming
mechanism::
>>> fib
<function fib at 10042ed0>
>>> f = fib
>>> f(100)
0 1 1 2 3 5 8 13 21 34 55 89
Coming from other languages, you might object that ``fib`` is not a function but
a procedure since it doesn't return a value. In fact, even functions without a
:keyword:`return` statement do return a value, albeit a rather boring one. This
value is called ``None`` (it's a built-in name). Writing the value ``None`` is
normally suppressed by the interpreter if it would be the only value written.
You can see it if you really want to using :func:`print`::
>>> fib(0)
>>> print(fib(0))
None
It is simple to write a function that returns a list of the numbers of the
Fibonacci series, instead of printing it::
>>> def fib2(n): # return Fibonacci series up to n
... """Return a list containing the Fibonacci series up to n."""
... result = []
... a, b = 0, 1
... while a < n:
... result.append(a) # see below
... a, b = b, a+b
... return result
...
>>> f100 = fib2(100) # call it
>>> f100 # write the result
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
This example, as usual, demonstrates some new Python features:
* The :keyword:`return` statement returns with a value from a function.
:keyword:`return` without an expression argument returns ``None``. Falling off
the end of a function also returns ``None``.
* The statement ``result.append(a)`` calls a *method* of the list object
``result``. A method is a function that 'belongs' to an object and is named
``obj.methodname``, where ``obj`` is some object (this may be an expression),
and ``methodname`` is the name of a method that is defined by the object's type.
Different types define different methods. Methods of different types may have
the same name without causing ambiguity. (It is possible to define your own
object types and methods, using *classes*, see :ref:`tut-classes`)
The method :meth:`append` shown in the example is defined for list objects; it
adds a new element at the end of the list. In this example it is equivalent to
``result = result + [a]``, but more efficient.
.. _tut-defining:
More on Defining Functions
==========================
It is also possible to define functions with a variable number of arguments.
There are three forms, which can be combined.
.. _tut-defaultargs:
Default Argument Values
-----------------------
The most useful form is to specify a default value for one or more arguments.
This creates a function that can be called with fewer arguments than it is
defined to allow. For example::
def ask_ok(prompt, retries=4, reminder='Please try again!'):
while True:
ok = input(prompt)
if ok in ('y', 'ye', 'yes'):
return True
if ok in ('n', 'no', 'nop', 'nope'):
return False
retries = retries - 1
if retries < 0:
raise ValueError('invalid user response')
print(reminder)
This function can be called in several ways:
* giving only the mandatory argument:
``ask_ok('Do you really want to quit?')``
* giving one of the optional arguments:
``ask_ok('OK to overwrite the file?', 2)``
* or even giving all arguments:
``ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')``
This example also introduces the :keyword:`in` keyword. This tests whether or
not a sequence contains a certain value.
The default values are evaluated at the point of function definition in the
*defining* scope, so that ::
i = 5
def f(arg=i):
print(arg)
i = 6
f()
will print ``5``.
**Important warning:** The default value is evaluated only once. This makes a
difference when the default is a mutable object such as a list, dictionary, or
instances of most classes. For example, the following function accumulates the
arguments passed to it on subsequent calls::
def f(a, L=[]):
L.append(a)
return L
print(f(1))
print(f(2))
print(f(3))
This will print ::
[1]
[1, 2]
[1, 2, 3]
If you don't want the default to be shared between subsequent calls, you can
write the function like this instead::
def f(a, L=None):
if L is None:
L = []
L.append(a)
return L
.. _tut-keywordargs:
Keyword Arguments
-----------------
Functions can also be called using :term:`keyword arguments <keyword argument>`
of the form ``kwarg=value``. For instance, the following function::
def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
print("-- This parrot wouldn't", action, end=' ')
print("if you put", voltage, "volts through it.")
print("-- Lovely plumage, the", type)
print("-- It's", state, "!")
accepts one required argument (``voltage``) and three optional arguments
(``state``, ``action``, and ``type``). This function can be called in any
of the following ways::
parrot(1000) # 1 positional argument
parrot(voltage=1000) # 1 keyword argument
parrot(voltage=1000000, action='VOOOOOM') # 2 keyword arguments
parrot(action='VOOOOOM', voltage=1000000) # 2 keyword arguments
parrot('a million', 'bereft of life', 'jump') # 3 positional arguments
parrot('a thousand', state='pushing up the daisies') # 1 positional, 1 keyword
but all the following calls would be invalid::
parrot() # required argument missing
parrot(voltage=5.0, 'dead') # non-keyword argument after a keyword argument
parrot(110, voltage=220) # duplicate value for the same argument
parrot(actor='John Cleese') # unknown keyword argument
In a function call, keyword arguments must follow positional arguments.
All the keyword arguments passed must match one of the arguments
accepted by the function (e.g. ``actor`` is not a valid argument for the
``parrot`` function), and their order is not important. This also includes
non-optional arguments (e.g. ``parrot(voltage=1000)`` is valid too).
No argument may receive a value more than once.
Here's an example that fails due to this restriction::
>>> def function(a):
... pass
...
>>> function(0, a=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: function() got multiple values for keyword argument 'a'
When a final formal parameter of the form ``**name`` is present, it receives a
dictionary (see :ref:`typesmapping`) containing all keyword arguments except for
those corresponding to a formal parameter. This may be combined with a formal
parameter of the form ``*name`` (described in the next subsection) which
receives a tuple containing the positional arguments beyond the formal parameter
list. (``*name`` must occur before ``**name``.) For example, if we define a
function like this::
def cheeseshop(kind, *arguments, **keywords):
print("-- Do you have any", kind, "?")
print("-- I'm sorry, we're all out of", kind)
for arg in arguments:
print(arg)
print("-" * 40)
for kw in keywords:
print(kw, ":", keywords[kw])
It could be called like this::
cheeseshop("Limburger", "It's very runny, sir.",
"It's really very, VERY runny, sir.",
shopkeeper="Michael Palin",
client="John Cleese",
sketch="Cheese Shop Sketch")
and of course it would print:
.. code-block:: none
-- Do you have any Limburger ?
-- I'm sorry, we're all out of Limburger
It's very runny, sir.
It's really very, VERY runny, sir.
----------------------------------------
shopkeeper : Michael Palin
client : John Cleese
sketch : Cheese Shop Sketch
Note that the order in which the keyword arguments are printed is guaranteed
to match the order in which they were provided in the function call.
.. _tut-arbitraryargs:
Arbitrary Argument Lists
------------------------
.. index::
single: * (asterisk); in function calls
Finally, the least frequently used option is to specify that a function can be
called with an arbitrary number of arguments. These arguments will be wrapped
up in a tuple (see :ref:`tut-tuples`). Before the variable number of arguments,
zero or more normal arguments may occur. ::
def write_multiple_items(file, separator, *args):
file.write(separator.join(args))
Normally, these ``variadic`` arguments will be last in the list of formal
parameters, because they scoop up all remaining input arguments that are
passed to the function. Any formal parameters which occur after the ``*args``
parameter are 'keyword-only' arguments, meaning that they can only be used as
keywords rather than positional arguments. ::
>>> def concat(*args, sep="/"):
... return sep.join(args)
...
>>> concat("earth", "mars", "venus")
'earth/mars/venus'
>>> concat("earth", "mars", "venus", sep=".")
'earth.mars.venus'
.. _tut-unpacking-arguments:
Unpacking Argument Lists
------------------------
The reverse situation occurs when the arguments are already in a list or tuple
but need to be unpacked for a function call requiring separate positional
arguments. For instance, the built-in :func:`range` function expects separate
*start* and *stop* arguments. If they are not available separately, write the
function call with the ``*``\ -operator to unpack the arguments out of a list
or tuple::
>>> list(range(3, 6)) # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> list(range(*args)) # call with arguments unpacked from a list
[3, 4, 5]
.. index::
single: **; in function calls
In the same fashion, dictionaries can deliver keyword arguments with the
``**``\ -operator::
>>> def parrot(voltage, state='a stiff', action='voom'):
... print("-- This parrot wouldn't", action, end=' ')
... print("if you put", voltage, "volts through it.", end=' ')
... print("E's", state, "!")
...
>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
>>> parrot(**d)
-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !
.. _tut-lambda:
Lambda Expressions
------------------
Small anonymous functions can be created with the :keyword:`lambda` keyword.
This function returns the sum of its two arguments: ``lambda a, b: a+b``.
Lambda functions can be used wherever function objects are required. They are
syntactically restricted to a single expression. Semantically, they are just
syntactic sugar for a normal function definition. Like nested function
definitions, lambda functions can reference variables from the containing
scope::
>>> def make_incrementor(n):
... return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43
The above example uses a lambda expression to return a function. Another use
is to pass a small function as an argument::
>>> pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
>>> pairs.sort(key=lambda pair: pair[1])
>>> pairs
[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]
.. _tut-docstrings:
Documentation Strings
---------------------
.. index::
single: docstrings
single: documentation strings
single: strings, documentation
Here are some conventions about the content and formatting of documentation
strings.
The first line should always be a short, concise summary of the object's
purpose. For brevity, it should not explicitly state the object's name or type,
since these are available by other means (except if the name happens to be a
verb describing a function's operation). This line should begin with a capital
letter and end with a period.
If there are more lines in the documentation string, the second line should be
blank, visually separating the summary from the rest of the description. The
following lines should be one or more paragraphs describing the object's calling
conventions, its side effects, etc.
The Python parser does not strip indentation from multi-line string literals in
Python, so tools that process documentation have to strip indentation if
desired. This is done using the following convention. The first non-blank line
*after* the first line of the string determines the amount of indentation for
the entire documentation string. (We can't use the first line since it is
generally adjacent to the string's opening quotes so its indentation is not
apparent in the string literal.) Whitespace "equivalent" to this indentation is
then stripped from the start of all lines of the string. Lines that are
indented less should not occur, but if they occur all their leading whitespace
should be stripped. Equivalence of whitespace should be tested after expansion
of tabs (to 8 spaces, normally).
Here is an example of a multi-line docstring::
>>> def my_function():
... """Do nothing, but document it.
...
... No, really, it doesn't do anything.
... """
... pass
...
>>> print(my_function.__doc__)
Do nothing, but document it.
No, really, it doesn't do anything.
.. _tut-annotations:
Function Annotations
--------------------
.. sectionauthor:: Zachary Ware <zachary.ware@gmail.com>
.. index::
pair: function; annotations
single: ->; function annotations
single: : (colon); function annotations
:ref:`Function annotations <function>` are completely optional metadata
information about the types used by user-defined functions (see :pep:`3107` and
:pep:`484` for more information).
Annotations are stored in the :attr:`__annotations__` attribute of the function
as a dictionary and have no effect on any other part of the function. Parameter
annotations are defined by a colon after the parameter name, followed by an
expression evaluating to the value of the annotation. Return annotations are
defined by a literal ``->``, followed by an expression, between the parameter
list and the colon denoting the end of the :keyword:`def` statement. The
following example has a positional argument, a keyword argument, and the return
value annotated::
>>> def f(ham: str, eggs: str = 'eggs') -> str:
... print("Annotations:", f.__annotations__)
... print("Arguments:", ham, eggs)
... return ham + ' and ' + eggs
...
>>> f('spam')
Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>}
Arguments: spam eggs
'spam and eggs'
.. _tut-codingstyle:
Intermezzo: Coding Style
========================
.. sectionauthor:: Georg Brandl <georg@python.org>
.. index:: pair: coding; style
Now that you are about to write longer, more complex pieces of Python, it is a
good time to talk about *coding style*. Most languages can be written (or more
concise, *formatted*) in different styles; some are more readable than others.
Making it easy for others to read your code is always a good idea, and adopting
a nice coding style helps tremendously for that.
For Python, :pep:`8` has emerged as the style guide that most projects adhere to;
it promotes a very readable and eye-pleasing coding style. Every Python
developer should read it at some point; here are the most important points
extracted for you:
* Use 4-space indentation, and no tabs.
4 spaces are a good compromise between small indentation (allows greater
nesting depth) and large indentation (easier to read). Tabs introduce
confusion, and are best left out.
* Wrap lines so that they don't exceed 79 characters.
This helps users with small displays and makes it possible to have several
code files side-by-side on larger displays.
* Use blank lines to separate functions and classes, and larger blocks of
code inside functions.
* When possible, put comments on a line of their own.
* Use docstrings.
* Use spaces around operators and after commas, but not directly inside
bracketing constructs: ``a = f(1, 2) + g(3, 4)``.
* Name your classes and functions consistently; the convention is to use
``CamelCase`` for classes and ``lower_case_with_underscores`` for functions
and methods. Always use ``self`` as the name for the first method argument
(see :ref:`tut-firstclasses` for more on classes and methods).
* Don't use fancy encodings if your code is meant to be used in international
environments. Python's default, UTF-8, or even plain ASCII work best in any
case.
* Likewise, don't use non-ASCII characters in identifiers if there is only the
slightest chance people speaking a different language will read or maintain
the code.
.. rubric:: Footnotes
.. [#] Actually, *call by object reference* would be a better description,
since if a mutable object is passed, the caller will see any changes the
callee makes to it (items inserted into a list).

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.. _tut-structures:
***************
Data Structures
***************
This chapter describes some things you've learned about already in more detail,
and adds some new things as well.
.. _tut-morelists:
More on Lists
=============
The list data type has some more methods. Here are all of the methods of list
objects:
.. method:: list.append(x)
:noindex:
Add an item to the end of the list. Equivalent to ``a[len(a):] = [x]``.
.. method:: list.extend(iterable)
:noindex:
Extend the list by appending all the items from the iterable. Equivalent to
``a[len(a):] = iterable``.
.. method:: list.insert(i, x)
:noindex:
Insert an item at a given position. The first argument is the index of the
element before which to insert, so ``a.insert(0, x)`` inserts at the front of
the list, and ``a.insert(len(a), x)`` is equivalent to ``a.append(x)``.
.. method:: list.remove(x)
:noindex:
Remove the first item from the list whose value is *x*. It is an error if
there is no such item.
.. method:: list.pop([i])
:noindex:
Remove the item at the given position in the list, and return it. If no index
is specified, ``a.pop()`` removes and returns the last item in the list. (The
square brackets around the *i* in the method signature denote that the parameter
is optional, not that you should type square brackets at that position. You
will see this notation frequently in the Python Library Reference.)
.. method:: list.clear()
:noindex:
Remove all items from the list. Equivalent to ``del a[:]``.
.. method:: list.index(x[, start[, end]])
:noindex:
Return zero-based index in the list of the first item whose value is *x*.
Raises a :exc:`ValueError` if there is no such item.
The optional arguments *start* and *end* are interpreted as in the slice
notation and are used to limit the search to a particular subsequence of
the list. The returned index is computed relative to the beginning of the full
sequence rather than the *start* argument.
.. method:: list.count(x)
:noindex:
Return the number of times *x* appears in the list.
.. method:: list.sort(key=None, reverse=False)
:noindex:
Sort the items of the list in place (the arguments can be used for sort
customization, see :func:`sorted` for their explanation).
.. method:: list.reverse()
:noindex:
Reverse the elements of the list in place.
.. method:: list.copy()
:noindex:
Return a shallow copy of the list. Equivalent to ``a[:]``.
An example that uses most of the list methods::
>>> fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
>>> fruits.count('apple')
2
>>> fruits.count('tangerine')
0
>>> fruits.index('banana')
3
>>> fruits.index('banana', 4) # Find next banana starting a position 4
6
>>> fruits.reverse()
>>> fruits
['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']
>>> fruits.append('grape')
>>> fruits
['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape']
>>> fruits.sort()
>>> fruits
['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear']
>>> fruits.pop()
'pear'
You might have noticed that methods like ``insert``, ``remove`` or ``sort`` that
only modify the list have no return value printed -- they return the default
``None``. [1]_ This is a design principle for all mutable data structures in
Python.
.. _tut-lists-as-stacks:
Using Lists as Stacks
---------------------
.. sectionauthor:: Ka-Ping Yee <ping@lfw.org>
The list methods make it very easy to use a list as a stack, where the last
element added is the first element retrieved ("last-in, first-out"). To add an
item to the top of the stack, use :meth:`append`. To retrieve an item from the
top of the stack, use :meth:`pop` without an explicit index. For example::
>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
7
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
6
>>> stack.pop()
5
>>> stack
[3, 4]
.. _tut-lists-as-queues:
Using Lists as Queues
---------------------
.. sectionauthor:: Ka-Ping Yee <ping@lfw.org>
It is also possible to use a list as a queue, where the first element added is
the first element retrieved ("first-in, first-out"); however, lists are not
efficient for this purpose. While appends and pops from the end of list are
fast, doing inserts or pops from the beginning of a list is slow (because all
of the other elements have to be shifted by one).
To implement a queue, use :class:`collections.deque` which was designed to
have fast appends and pops from both ends. For example::
>>> from collections import deque
>>> queue = deque(["Eric", "John", "Michael"])
>>> queue.append("Terry") # Terry arrives
>>> queue.append("Graham") # Graham arrives
>>> queue.popleft() # The first to arrive now leaves
'Eric'
>>> queue.popleft() # The second to arrive now leaves
'John'
>>> queue # Remaining queue in order of arrival
deque(['Michael', 'Terry', 'Graham'])
.. _tut-listcomps:
List Comprehensions
-------------------
List comprehensions provide a concise way to create lists.
Common applications are to make new lists where each element is the result of
some operations applied to each member of another sequence or iterable, or to
create a subsequence of those elements that satisfy a certain condition.
For example, assume we want to create a list of squares, like::
>>> squares = []
>>> for x in range(10):
... squares.append(x**2)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Note that this creates (or overwrites) a variable named ``x`` that still exists
after the loop completes. We can calculate the list of squares without any
side effects using::
squares = list(map(lambda x: x**2, range(10)))
or, equivalently::
squares = [x**2 for x in range(10)]
which is more concise and readable.
A list comprehension consists of brackets containing an expression followed
by a :keyword:`for` clause, then zero or more :keyword:`for` or :keyword:`if`
clauses. The result will be a new list resulting from evaluating the expression
in the context of the :keyword:`for` and :keyword:`if` clauses which follow it.
For example, this listcomp combines the elements of two lists if they are not
equal::
>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
and it's equivalent to::
>>> combs = []
>>> for x in [1,2,3]:
... for y in [3,1,4]:
... if x != y:
... combs.append((x, y))
...
>>> combs
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
Note how the order of the :keyword:`for` and :keyword:`if` statements is the
same in both these snippets.
If the expression is a tuple (e.g. the ``(x, y)`` in the previous example),
it must be parenthesized. ::
>>> vec = [-4, -2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[-8, -4, 0, 4, 8]
>>> # filter the list to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> # apply a function to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create a list of 2-tuples like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> # the tuple must be parenthesized, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
File "<stdin>", line 1, in <module>
[x, x**2 for x in range(6)]
^
SyntaxError: invalid syntax
>>> # flatten a list using a listcomp with two 'for'
>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]
List comprehensions can contain complex expressions and nested functions::
>>> from math import pi
>>> [str(round(pi, i)) for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
Nested List Comprehensions
--------------------------
The initial expression in a list comprehension can be any arbitrary expression,
including another list comprehension.
Consider the following example of a 3x4 matrix implemented as a list of
3 lists of length 4::
>>> matrix = [
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12],
... ]
The following list comprehension will transpose rows and columns::
>>> [[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
As we saw in the previous section, the nested listcomp is evaluated in
the context of the :keyword:`for` that follows it, so this example is
equivalent to::
>>> transposed = []
>>> for i in range(4):
... transposed.append([row[i] for row in matrix])
...
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
which, in turn, is the same as::
>>> transposed = []
>>> for i in range(4):
... # the following 3 lines implement the nested listcomp
... transposed_row = []
... for row in matrix:
... transposed_row.append(row[i])
... transposed.append(transposed_row)
...
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
In the real world, you should prefer built-in functions to complex flow statements.
The :func:`zip` function would do a great job for this use case::
>>> list(zip(*matrix))
[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]
See :ref:`tut-unpacking-arguments` for details on the asterisk in this line.
.. _tut-del:
The :keyword:`del` statement
============================
There is a way to remove an item from a list given its index instead of its
value: the :keyword:`del` statement. This differs from the :meth:`pop` method
which returns a value. The :keyword:`del` statement can also be used to remove
slices from a list or clear the entire list (which we did earlier by assignment
of an empty list to the slice). For example::
>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> del a[:]
>>> a
[]
:keyword:`del` can also be used to delete entire variables::
>>> del a
Referencing the name ``a`` hereafter is an error (at least until another value
is assigned to it). We'll find other uses for :keyword:`del` later.
.. _tut-tuples:
Tuples and Sequences
====================
We saw that lists and strings have many common properties, such as indexing and
slicing operations. They are two examples of *sequence* data types (see
:ref:`typesseq`). Since Python is an evolving language, other sequence data
types may be added. There is also another standard sequence data type: the
*tuple*.
A tuple consists of a number of values separated by commas, for instance::
>>> t = 12345, 54321, 'hello!'
>>> t[0]
12345
>>> t
(12345, 54321, 'hello!')
>>> # Tuples may be nested:
... u = t, (1, 2, 3, 4, 5)
>>> u
((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
>>> # Tuples are immutable:
... t[0] = 88888
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
>>> # but they can contain mutable objects:
... v = ([1, 2, 3], [3, 2, 1])
>>> v
([1, 2, 3], [3, 2, 1])
As you see, on output tuples are always enclosed in parentheses, so that nested
tuples are interpreted correctly; they may be input with or without surrounding
parentheses, although often parentheses are necessary anyway (if the tuple is
part of a larger expression). It is not possible to assign to the individual
items of a tuple, however it is possible to create tuples which contain mutable
objects, such as lists.
Though tuples may seem similar to lists, they are often used in different
situations and for different purposes.
Tuples are :term:`immutable`, and usually contain a heterogeneous sequence of
elements that are accessed via unpacking (see later in this section) or indexing
(or even by attribute in the case of :func:`namedtuples <collections.namedtuple>`).
Lists are :term:`mutable`, and their elements are usually homogeneous and are
accessed by iterating over the list.
A special problem is the construction of tuples containing 0 or 1 items: the
syntax has some extra quirks to accommodate these. Empty tuples are constructed
by an empty pair of parentheses; a tuple with one item is constructed by
following a value with a comma (it is not sufficient to enclose a single value
in parentheses). Ugly, but effective. For example::
>>> empty = ()
>>> singleton = 'hello', # <-- note trailing comma
>>> len(empty)
0
>>> len(singleton)
1
>>> singleton
('hello',)
The statement ``t = 12345, 54321, 'hello!'`` is an example of *tuple packing*:
the values ``12345``, ``54321`` and ``'hello!'`` are packed together in a tuple.
The reverse operation is also possible::
>>> x, y, z = t
This is called, appropriately enough, *sequence unpacking* and works for any
sequence on the right-hand side. Sequence unpacking requires that there are as
many variables on the left side of the equals sign as there are elements in the
sequence. Note that multiple assignment is really just a combination of tuple
packing and sequence unpacking.
.. _tut-sets:
Sets
====
Python also includes a data type for *sets*. A set is an unordered collection
with no duplicate elements. Basic uses include membership testing and
eliminating duplicate entries. Set objects also support mathematical operations
like union, intersection, difference, and symmetric difference.
Curly braces or the :func:`set` function can be used to create sets. Note: to
create an empty set you have to use ``set()``, not ``{}``; the latter creates an
empty dictionary, a data structure that we discuss in the next section.
Here is a brief demonstration::
>>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
>>> print(basket) # show that duplicates have been removed
{'orange', 'banana', 'pear', 'apple'}
>>> 'orange' in basket # fast membership testing
True
>>> 'crabgrass' in basket
False
>>> # Demonstrate set operations on unique letters from two words
...
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a # unique letters in a
{'a', 'r', 'b', 'c', 'd'}
>>> a - b # letters in a but not in b
{'r', 'd', 'b'}
>>> a | b # letters in a or b or both
{'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
>>> a & b # letters in both a and b
{'a', 'c'}
>>> a ^ b # letters in a or b but not both
{'r', 'd', 'b', 'm', 'z', 'l'}
Similarly to :ref:`list comprehensions <tut-listcomps>`, set comprehensions
are also supported::
>>> a = {x for x in 'abracadabra' if x not in 'abc'}
>>> a
{'r', 'd'}
.. _tut-dictionaries:
Dictionaries
============
Another useful data type built into Python is the *dictionary* (see
:ref:`typesmapping`). Dictionaries are sometimes found in other languages as
"associative memories" or "associative arrays". Unlike sequences, which are
indexed by a range of numbers, dictionaries are indexed by *keys*, which can be
any immutable type; strings and numbers can always be keys. Tuples can be used
as keys if they contain only strings, numbers, or tuples; if a tuple contains
any mutable object either directly or indirectly, it cannot be used as a key.
You can't use lists as keys, since lists can be modified in place using index
assignments, slice assignments, or methods like :meth:`append` and
:meth:`extend`.
It is best to think of a dictionary as an unordered set of *key: value* pairs,
with the requirement that the keys are unique (within one dictionary). A pair of
braces creates an empty dictionary: ``{}``. Placing a comma-separated list of
key:value pairs within the braces adds initial key:value pairs to the
dictionary; this is also the way dictionaries are written on output.
The main operations on a dictionary are storing a value with some key and
extracting the value given the key. It is also possible to delete a key:value
pair with ``del``. If you store using a key that is already in use, the old
value associated with that key is forgotten. It is an error to extract a value
using a non-existent key.
Performing ``list(d.keys())`` on a dictionary returns a list of all the keys
used in the dictionary, in arbitrary order (if you want it sorted, just use
``sorted(d.keys())`` instead). [2]_ To check whether a single key is in the
dictionary, use the :keyword:`in` keyword.
Here is a small example using a dictionary::
>>> tel = {'jack': 4098, 'sape': 4139}
>>> tel['guido'] = 4127
>>> tel
{'sape': 4139, 'guido': 4127, 'jack': 4098}
>>> tel['jack']
4098
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'guido': 4127, 'irv': 4127, 'jack': 4098}
>>> list(tel.keys())
['irv', 'guido', 'jack']
>>> sorted(tel.keys())
['guido', 'irv', 'jack']
>>> 'guido' in tel
True
>>> 'jack' not in tel
False
The :func:`dict` constructor builds dictionaries directly from sequences of
key-value pairs::
>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
In addition, dict comprehensions can be used to create dictionaries from
arbitrary key and value expressions::
>>> {x: x**2 for x in (2, 4, 6)}
{2: 4, 4: 16, 6: 36}
When the keys are simple strings, it is sometimes easier to specify pairs using
keyword arguments::
>>> dict(sape=4139, guido=4127, jack=4098)
{'sape': 4139, 'jack': 4098, 'guido': 4127}
.. _tut-loopidioms:
Looping Techniques
==================
When looping through dictionaries, the key and corresponding value can be
retrieved at the same time using the :meth:`items` method. ::
>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.items():
... print(k, v)
...
gallahad the pure
robin the brave
When looping through a sequence, the position index and corresponding value can
be retrieved at the same time using the :func:`enumerate` function. ::
>>> for i, v in enumerate(['tic', 'tac', 'toe']):
... print(i, v)
...
0 tic
1 tac
2 toe
To loop over two or more sequences at the same time, the entries can be paired
with the :func:`zip` function. ::
>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in zip(questions, answers):
... print('What is your {0}? It is {1}.'.format(q, a))
...
What is your name? It is lancelot.
What is your quest? It is the holy grail.
What is your favorite color? It is blue.
To loop over a sequence in reverse, first specify the sequence in a forward
direction and then call the :func:`reversed` function. ::
>>> for i in reversed(range(1, 10, 2)):
... print(i)
...
9
7
5
3
1
To loop over a sequence in sorted order, use the :func:`sorted` function which
returns a new sorted list while leaving the source unaltered. ::
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
... print(f)
...
apple
banana
orange
pear
It is sometimes tempting to change a list while you are looping over it;
however, it is often simpler and safer to create a new list instead. ::
>>> import math
>>> raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
>>> filtered_data = []
>>> for value in raw_data:
... if not math.isnan(value):
... filtered_data.append(value)
...
>>> filtered_data
[56.2, 51.7, 55.3, 52.5, 47.8]
.. _tut-conditions:
More on Conditions
==================
The conditions used in ``while`` and ``if`` statements can contain any
operators, not just comparisons.
The comparison operators ``in`` and ``not in`` check whether a value occurs
(does not occur) in a sequence. The operators ``is`` and ``is not`` compare
whether two objects are really the same object; this only matters for mutable
objects like lists. All comparison operators have the same priority, which is
lower than that of all numerical operators.
Comparisons can be chained. For example, ``a < b == c`` tests whether ``a`` is
less than ``b`` and moreover ``b`` equals ``c``.
Comparisons may be combined using the Boolean operators ``and`` and ``or``, and
the outcome of a comparison (or of any other Boolean expression) may be negated
with ``not``. These have lower priorities than comparison operators; between
them, ``not`` has the highest priority and ``or`` the lowest, so that ``A and
not B or C`` is equivalent to ``(A and (not B)) or C``. As always, parentheses
can be used to express the desired composition.
The Boolean operators ``and`` and ``or`` are so-called *short-circuit*
operators: their arguments are evaluated from left to right, and evaluation
stops as soon as the outcome is determined. For example, if ``A`` and ``C`` are
true but ``B`` is false, ``A and B and C`` does not evaluate the expression
``C``. When used as a general value and not as a Boolean, the return value of a
short-circuit operator is the last evaluated argument.
It is possible to assign the result of a comparison or other Boolean expression
to a variable. For example, ::
>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
>>> non_null = string1 or string2 or string3
>>> non_null
'Trondheim'
Note that in Python, unlike C, assignment cannot occur inside expressions. C
programmers may grumble about this, but it avoids a common class of problems
encountered in C programs: typing ``=`` in an expression when ``==`` was
intended.
.. _tut-comparing:
Comparing Sequences and Other Types
===================================
Sequence objects may be compared to other objects with the same sequence type.
The comparison uses *lexicographical* ordering: first the first two items are
compared, and if they differ this determines the outcome of the comparison; if
they are equal, the next two items are compared, and so on, until either
sequence is exhausted. If two items to be compared are themselves sequences of
the same type, the lexicographical comparison is carried out recursively. If
all items of two sequences compare equal, the sequences are considered equal.
If one sequence is an initial sub-sequence of the other, the shorter sequence is
the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode
code point number to order individual characters. Some examples of comparisons
between sequences of the same type::
(1, 2, 3) < (1, 2, 4)
[1, 2, 3] < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4) < (1, 2, 4)
(1, 2) < (1, 2, -1)
(1, 2, 3) == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4)
Note that comparing objects of different types with ``<`` or ``>`` is legal
provided that the objects have appropriate comparison methods. For example,
mixed numeric types are compared according to their numeric value, so 0 equals
0.0, etc. Otherwise, rather than providing an arbitrary ordering, the
interpreter will raise a :exc:`TypeError` exception.
.. rubric:: Footnotes
.. [1] Other languages may return the mutated object, which allows method
chaining, such as ``d->insert("a")->remove("b")->sort();``.
.. [2] Calling ``d.keys()`` will return a :dfn:`dictionary view` object. It
supports operations like membership test and iteration, but its contents
are not independent of the original dictionary -- it is only a *view*.

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.. _tut-errors:
*********************
Errors and Exceptions
*********************
Until now error messages haven't been more than mentioned, but if you have tried
out the examples you have probably seen some. There are (at least) two
distinguishable kinds of errors: *syntax errors* and *exceptions*.
.. _tut-syntaxerrors:
Syntax Errors
=============
Syntax errors, also known as parsing errors, are perhaps the most common kind of
complaint you get while you are still learning Python::
>>> while True print('Hello world')
File "<stdin>", line 1
while True print('Hello world')
^
SyntaxError: invalid syntax
The parser repeats the offending line and displays a little 'arrow' pointing at
the earliest point in the line where the error was detected. The error is
caused by (or at least detected at) the token *preceding* the arrow: in the
example, the error is detected at the function :func:`print`, since a colon
(``':'``) is missing before it. File name and line number are printed so you
know where to look in case the input came from a script.
.. _tut-exceptions:
Exceptions
==========
Even if a statement or expression is syntactically correct, it may cause an
error when an attempt is made to execute it. Errors detected during execution
are called *exceptions* and are not unconditionally fatal: you will soon learn
how to handle them in Python programs. Most exceptions are not handled by
programs, however, and result in error messages as shown here::
>>> 10 * (1/0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ZeroDivisionError: division by zero
>>> 4 + spam*3
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'spam' is not defined
>>> '2' + 2
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Can't convert 'int' object to str implicitly
The last line of the error message indicates what happened. Exceptions come in
different types, and the type is printed as part of the message: the types in
the example are :exc:`ZeroDivisionError`, :exc:`NameError` and :exc:`TypeError`.
The string printed as the exception type is the name of the built-in exception
that occurred. This is true for all built-in exceptions, but need not be true
for user-defined exceptions (although it is a useful convention). Standard
exception names are built-in identifiers (not reserved keywords).
The rest of the line provides detail based on the type of exception and what
caused it.
The preceding part of the error message shows the context where the exception
happened, in the form of a stack traceback. In general it contains a stack
traceback listing source lines; however, it will not display lines read from
standard input.
:ref:`bltin-exceptions` lists the built-in exceptions and their meanings.
.. _tut-handling:
Handling Exceptions
===================
It is possible to write programs that handle selected exceptions. Look at the
following example, which asks the user for input until a valid integer has been
entered, but allows the user to interrupt the program (using :kbd:`Control-C` or
whatever the operating system supports); note that a user-generated interruption
is signalled by raising the :exc:`KeyboardInterrupt` exception. ::
>>> while True:
... try:
... x = int(input("Please enter a number: "))
... break
... except ValueError:
... print("Oops! That was no valid number. Try again...")
...
The :keyword:`try` statement works as follows.
* First, the *try clause* (the statement(s) between the :keyword:`try` and
:keyword:`except` keywords) is executed.
* If no exception occurs, the *except clause* is skipped and execution of the
:keyword:`try` statement is finished.
* If an exception occurs during execution of the try clause, the rest of the
clause is skipped. Then if its type matches the exception named after the
:keyword:`except` keyword, the except clause is executed, and then execution
continues after the :keyword:`try` statement.
* If an exception occurs which does not match the exception named in the except
clause, it is passed on to outer :keyword:`try` statements; if no handler is
found, it is an *unhandled exception* and execution stops with a message as
shown above.
A :keyword:`try` statement may have more than one except clause, to specify
handlers for different exceptions. At most one handler will be executed.
Handlers only handle exceptions that occur in the corresponding try clause, not
in other handlers of the same :keyword:`try` statement. An except clause may
name multiple exceptions as a parenthesized tuple, for example::
... except (RuntimeError, TypeError, NameError):
... pass
A class in an :keyword:`except` clause is compatible with an exception if it is
the same class or a base class thereof (but not the other way around --- an
except clause listing a derived class is not compatible with a base class). For
example, the following code will print B, C, D in that order::
class B(Exception):
pass
class C(B):
pass
class D(C):
pass
for cls in [B, C, D]:
try:
raise cls()
except D:
print("D")
except C:
print("C")
except B:
print("B")
Note that if the except clauses were reversed (with ``except B`` first), it
would have printed B, B, B --- the first matching except clause is triggered.
The last except clause may omit the exception name(s), to serve as a wildcard.
Use this with extreme caution, since it is easy to mask a real programming error
in this way! It can also be used to print an error message and then re-raise
the exception (allowing a caller to handle the exception as well)::
import sys
try:
f = open('myfile.txt')
s = f.readline()
i = int(s.strip())
except OSError as err:
print("OS error: {0}".format(err))
except ValueError:
print("Could not convert data to an integer.")
except:
print("Unexpected error:", sys.exc_info()[0])
raise
The :keyword:`try` ... :keyword:`except` statement has an optional *else
clause*, which, when present, must follow all except clauses. It is useful for
code that must be executed if the try clause does not raise an exception. For
example::
for arg in sys.argv[1:]:
try:
f = open(arg, 'r')
except OSError:
print('cannot open', arg)
else:
print(arg, 'has', len(f.readlines()), 'lines')
f.close()
The use of the :keyword:`else` clause is better than adding additional code to
the :keyword:`try` clause because it avoids accidentally catching an exception
that wasn't raised by the code being protected by the :keyword:`try` ...
:keyword:`except` statement.
When an exception occurs, it may have an associated value, also known as the
exception's *argument*. The presence and type of the argument depend on the
exception type.
The except clause may specify a variable after the exception name. The
variable is bound to an exception instance with the arguments stored in
``instance.args``. For convenience, the exception instance defines
:meth:`__str__` so the arguments can be printed directly without having to
reference ``.args``. One may also instantiate an exception first before
raising it and add any attributes to it as desired. ::
>>> try:
... raise Exception('spam', 'eggs')
... except Exception as inst:
... print(type(inst)) # the exception instance
... print(inst.args) # arguments stored in .args
... print(inst) # __str__ allows args to be printed directly,
... # but may be overridden in exception subclasses
... x, y = inst.args # unpack args
... print('x =', x)
... print('y =', y)
...
<class 'Exception'>
('spam', 'eggs')
('spam', 'eggs')
x = spam
y = eggs
If an exception has arguments, they are printed as the last part ('detail') of
the message for unhandled exceptions.
Exception handlers don't just handle exceptions if they occur immediately in the
try clause, but also if they occur inside functions that are called (even
indirectly) in the try clause. For example::
>>> def this_fails():
... x = 1/0
...
>>> try:
... this_fails()
... except ZeroDivisionError as err:
... print('Handling run-time error:', err)
...
Handling run-time error: division by zero
.. _tut-raising:
Raising Exceptions
==================
The :keyword:`raise` statement allows the programmer to force a specified
exception to occur. For example::
>>> raise NameError('HiThere')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: HiThere
The sole argument to :keyword:`raise` indicates the exception to be raised.
This must be either an exception instance or an exception class (a class that
derives from :class:`Exception`). If an exception class is passed, it will
be implicitly instantiated by calling its constructor with no arguments::
raise ValueError # shorthand for 'raise ValueError()'
If you need to determine whether an exception was raised but don't intend to
handle it, a simpler form of the :keyword:`raise` statement allows you to
re-raise the exception::
>>> try:
... raise NameError('HiThere')
... except NameError:
... print('An exception flew by!')
... raise
...
An exception flew by!
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
NameError: HiThere
.. _tut-userexceptions:
User-defined Exceptions
=======================
Programs may name their own exceptions by creating a new exception class (see
:ref:`tut-classes` for more about Python classes). Exceptions should typically
be derived from the :exc:`Exception` class, either directly or indirectly.
Exception classes can be defined which do anything any other class can do, but
are usually kept simple, often only offering a number of attributes that allow
information about the error to be extracted by handlers for the exception. When
creating a module that can raise several distinct errors, a common practice is
to create a base class for exceptions defined by that module, and subclass that
to create specific exception classes for different error conditions::
class Error(Exception):
"""Base class for exceptions in this module."""
pass
class InputError(Error):
"""Exception raised for errors in the input.
Attributes:
expression -- input expression in which the error occurred
message -- explanation of the error
"""
def __init__(self, expression, message):
self.expression = expression
self.message = message
class TransitionError(Error):
"""Raised when an operation attempts a state transition that's not
allowed.
Attributes:
previous -- state at beginning of transition
next -- attempted new state
message -- explanation of why the specific transition is not allowed
"""
def __init__(self, previous, next, message):
self.previous = previous
self.next = next
self.message = message
Most exceptions are defined with names that end in "Error", similar to the
naming of the standard exceptions.
Many standard modules define their own exceptions to report errors that may
occur in functions they define. More information on classes is presented in
chapter :ref:`tut-classes`.
.. _tut-cleanup:
Defining Clean-up Actions
=========================
The :keyword:`try` statement has another optional clause which is intended to
define clean-up actions that must be executed under all circumstances. For
example::
>>> try:
... raise KeyboardInterrupt
... finally:
... print('Goodbye, world!')
...
Goodbye, world!
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
KeyboardInterrupt
A *finally clause* is always executed before leaving the :keyword:`try`
statement, whether an exception has occurred or not. When an exception has
occurred in the :keyword:`try` clause and has not been handled by an
:keyword:`except` clause (or it has occurred in an :keyword:`except` or
:keyword:`else` clause), it is re-raised after the :keyword:`finally` clause has
been executed. The :keyword:`finally` clause is also executed "on the way out"
when any other clause of the :keyword:`try` statement is left via a
:keyword:`break`, :keyword:`continue` or :keyword:`return` statement. A more
complicated example::
>>> def divide(x, y):
... try:
... result = x / y
... except ZeroDivisionError:
... print("division by zero!")
... else:
... print("result is", result)
... finally:
... print("executing finally clause")
...
>>> divide(2, 1)
result is 2.0
executing finally clause
>>> divide(2, 0)
division by zero!
executing finally clause
>>> divide("2", "1")
executing finally clause
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 3, in divide
TypeError: unsupported operand type(s) for /: 'str' and 'str'
As you can see, the :keyword:`finally` clause is executed in any event. The
:exc:`TypeError` raised by dividing two strings is not handled by the
:keyword:`except` clause and therefore re-raised after the :keyword:`finally`
clause has been executed.
In real world applications, the :keyword:`finally` clause is useful for
releasing external resources (such as files or network connections), regardless
of whether the use of the resource was successful.
.. _tut-cleanup-with:
Predefined Clean-up Actions
===========================
Some objects define standard clean-up actions to be undertaken when the object
is no longer needed, regardless of whether or not the operation using the object
succeeded or failed. Look at the following example, which tries to open a file
and print its contents to the screen. ::
for line in open("myfile.txt"):
print(line, end="")
The problem with this code is that it leaves the file open for an indeterminate
amount of time after this part of the code has finished executing.
This is not an issue in simple scripts, but can be a problem for larger
applications. The :keyword:`with` statement allows objects like files to be
used in a way that ensures they are always cleaned up promptly and correctly. ::
with open("myfile.txt") as f:
for line in f:
print(line, end="")
After the statement is executed, the file *f* is always closed, even if a
problem was encountered while processing the lines. Objects which, like files,
provide predefined clean-up actions will indicate this in their documentation.

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.. testsetup::
import math
.. _tut-fp-issues:
**************************************************
Floating Point Arithmetic: Issues and Limitations
**************************************************
.. sectionauthor:: Tim Peters <tim_one@users.sourceforge.net>
Floating-point numbers are represented in computer hardware as base 2 (binary)
fractions. For example, the decimal fraction ::
0.125
has value 1/10 + 2/100 + 5/1000, and in the same way the binary fraction ::
0.001
has value 0/2 + 0/4 + 1/8. These two fractions have identical values, the only
real difference being that the first is written in base 10 fractional notation,
and the second in base 2.
Unfortunately, most decimal fractions cannot be represented exactly as binary
fractions. A consequence is that, in general, the decimal floating-point
numbers you enter are only approximated by the binary floating-point numbers
actually stored in the machine.
The problem is easier to understand at first in base 10. Consider the fraction
1/3. You can approximate that as a base 10 fraction::
0.3
or, better, ::
0.33
or, better, ::
0.333
and so on. No matter how many digits you're willing to write down, the result
will never be exactly 1/3, but will be an increasingly better approximation of
1/3.
In the same way, no matter how many base 2 digits you're willing to use, the
decimal value 0.1 cannot be represented exactly as a base 2 fraction. In base
2, 1/10 is the infinitely repeating fraction ::
0.0001100110011001100110011001100110011001100110011...
Stop at any finite number of bits, and you get an approximation. On most
machines today, floats are approximated using a binary fraction with
the numerator using the first 53 bits starting with the most significant bit and
with the denominator as a power of two. In the case of 1/10, the binary fraction
is ``3602879701896397 / 2 ** 55`` which is close to but not exactly
equal to the true value of 1/10.
Many users are not aware of the approximation because of the way values are
displayed. Python only prints a decimal approximation to the true decimal
value of the binary approximation stored by the machine. On most machines, if
Python were to print the true decimal value of the binary approximation stored
for 0.1, it would have to display ::
>>> 0.1
0.1000000000000000055511151231257827021181583404541015625
That is more digits than most people find useful, so Python keeps the number
of digits manageable by displaying a rounded value instead ::
>>> 1 / 10
0.1
Just remember, even though the printed result looks like the exact value
of 1/10, the actual stored value is the nearest representable binary fraction.
Interestingly, there are many different decimal numbers that share the same
nearest approximate binary fraction. For example, the numbers ``0.1`` and
``0.10000000000000001`` and
``0.1000000000000000055511151231257827021181583404541015625`` are all
approximated by ``3602879701896397 / 2 ** 55``. Since all of these decimal
values share the same approximation, any one of them could be displayed
while still preserving the invariant ``eval(repr(x)) == x``.
Historically, the Python prompt and built-in :func:`repr` function would choose
the one with 17 significant digits, ``0.10000000000000001``. Starting with
Python 3.1, Python (on most systems) is now able to choose the shortest of
these and simply display ``0.1``.
Note that this is in the very nature of binary floating-point: this is not a bug
in Python, and it is not a bug in your code either. You'll see the same kind of
thing in all languages that support your hardware's floating-point arithmetic
(although some languages may not *display* the difference by default, or in all
output modes).
For more pleasant output, you may wish to use string formatting to produce a limited number of significant digits::
>>> format(math.pi, '.12g') # give 12 significant digits
'3.14159265359'
>>> format(math.pi, '.2f') # give 2 digits after the point
'3.14'
>>> repr(math.pi)
'3.141592653589793'
It's important to realize that this is, in a real sense, an illusion: you're
simply rounding the *display* of the true machine value.
One illusion may beget another. For example, since 0.1 is not exactly 1/10,
summing three values of 0.1 may not yield exactly 0.3, either::
>>> .1 + .1 + .1 == .3
False
Also, since the 0.1 cannot get any closer to the exact value of 1/10 and
0.3 cannot get any closer to the exact value of 3/10, then pre-rounding with
:func:`round` function cannot help::
>>> round(.1, 1) + round(.1, 1) + round(.1, 1) == round(.3, 1)
False
Though the numbers cannot be made closer to their intended exact values,
the :func:`round` function can be useful for post-rounding so that results
with inexact values become comparable to one another::
>>> round(.1 + .1 + .1, 10) == round(.3, 10)
True
Binary floating-point arithmetic holds many surprises like this. The problem
with "0.1" is explained in precise detail below, in the "Representation Error"
section. See `The Perils of Floating Point <http://www.lahey.com/float.htm>`_
for a more complete account of other common surprises.
As that says near the end, "there are no easy answers." Still, don't be unduly
wary of floating-point! The errors in Python float operations are inherited
from the floating-point hardware, and on most machines are on the order of no
more than 1 part in 2\*\*53 per operation. That's more than adequate for most
tasks, but you do need to keep in mind that it's not decimal arithmetic and
that every float operation can suffer a new rounding error.
While pathological cases do exist, for most casual use of floating-point
arithmetic you'll see the result you expect in the end if you simply round the
display of your final results to the number of decimal digits you expect.
:func:`str` usually suffices, and for finer control see the :meth:`str.format`
method's format specifiers in :ref:`formatstrings`.
For use cases which require exact decimal representation, try using the
:mod:`decimal` module which implements decimal arithmetic suitable for
accounting applications and high-precision applications.
Another form of exact arithmetic is supported by the :mod:`fractions` module
which implements arithmetic based on rational numbers (so the numbers like
1/3 can be represented exactly).
If you are a heavy user of floating point operations you should take a look
at the Numerical Python package and many other packages for mathematical and
statistical operations supplied by the SciPy project. See <https://scipy.org>.
Python provides tools that may help on those rare occasions when you really
*do* want to know the exact value of a float. The
:meth:`float.as_integer_ratio` method expresses the value of a float as a
fraction::
>>> x = 3.14159
>>> x.as_integer_ratio()
(3537115888337719, 1125899906842624)
Since the ratio is exact, it can be used to losslessly recreate the
original value::
>>> x == 3537115888337719 / 1125899906842624
True
The :meth:`float.hex` method expresses a float in hexadecimal (base
16), again giving the exact value stored by your computer::
>>> x.hex()
'0x1.921f9f01b866ep+1'
This precise hexadecimal representation can be used to reconstruct
the float value exactly::
>>> x == float.fromhex('0x1.921f9f01b866ep+1')
True
Since the representation is exact, it is useful for reliably porting values
across different versions of Python (platform independence) and exchanging
data with other languages that support the same format (such as Java and C99).
Another helpful tool is the :func:`math.fsum` function which helps mitigate
loss-of-precision during summation. It tracks "lost digits" as values are
added onto a running total. That can make a difference in overall accuracy
so that the errors do not accumulate to the point where they affect the
final total:
>>> sum([0.1] * 10) == 1.0
False
>>> math.fsum([0.1] * 10) == 1.0
True
.. _tut-fp-error:
Representation Error
====================
This section explains the "0.1" example in detail, and shows how you can perform
an exact analysis of cases like this yourself. Basic familiarity with binary
floating-point representation is assumed.
:dfn:`Representation error` refers to the fact that some (most, actually)
decimal fractions cannot be represented exactly as binary (base 2) fractions.
This is the chief reason why Python (or Perl, C, C++, Java, Fortran, and many
others) often won't display the exact decimal number you expect.
Why is that? 1/10 is not exactly representable as a binary fraction. Almost all
machines today (November 2000) use IEEE-754 floating point arithmetic, and
almost all platforms map Python floats to IEEE-754 "double precision". 754
doubles contain 53 bits of precision, so on input the computer strives to
convert 0.1 to the closest fraction it can of the form *J*/2**\ *N* where *J* is
an integer containing exactly 53 bits. Rewriting ::
1 / 10 ~= J / (2**N)
as ::
J ~= 2**N / 10
and recalling that *J* has exactly 53 bits (is ``>= 2**52`` but ``< 2**53``),
the best value for *N* is 56::
>>> 2**52 <= 2**56 // 10 < 2**53
True
That is, 56 is the only value for *N* that leaves *J* with exactly 53 bits. The
best possible value for *J* is then that quotient rounded::
>>> q, r = divmod(2**56, 10)
>>> r
6
Since the remainder is more than half of 10, the best approximation is obtained
by rounding up::
>>> q+1
7205759403792794
Therefore the best possible approximation to 1/10 in 754 double precision is::
7205759403792794 / 2 ** 56
Dividing both the numerator and denominator by two reduces the fraction to::
3602879701896397 / 2 ** 55
Note that since we rounded up, this is actually a little bit larger than 1/10;
if we had not rounded up, the quotient would have been a little bit smaller than
1/10. But in no case can it be *exactly* 1/10!
So the computer never "sees" 1/10: what it sees is the exact fraction given
above, the best 754 double approximation it can get::
>>> 0.1 * 2 ** 55
3602879701896397.0
If we multiply that fraction by 10\*\*55, we can see the value out to
55 decimal digits::
>>> 3602879701896397 * 10 ** 55 // 2 ** 55
1000000000000000055511151231257827021181583404541015625
meaning that the exact number stored in the computer is equal to
the decimal value 0.1000000000000000055511151231257827021181583404541015625.
Instead of displaying the full decimal value, many languages (including
older versions of Python), round the result to 17 significant digits::
>>> format(0.1, '.17f')
'0.10000000000000001'
The :mod:`fractions` and :mod:`decimal` modules make these calculations
easy::
>>> from decimal import Decimal
>>> from fractions import Fraction
>>> Fraction.from_float(0.1)
Fraction(3602879701896397, 36028797018963968)
>>> (0.1).as_integer_ratio()
(3602879701896397, 36028797018963968)
>>> Decimal.from_float(0.1)
Decimal('0.1000000000000000055511151231257827021181583404541015625')
>>> format(Decimal.from_float(0.1), '.17')
'0.10000000000000001'

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.. _tutorial-index:
######################
The Python Tutorial
######################
Python is an easy to learn, powerful programming language. It has efficient
high-level data structures and a simple but effective approach to
object-oriented programming. Python's elegant syntax and dynamic typing,
together with its interpreted nature, make it an ideal language for scripting
and rapid application development in many areas on most platforms.
The Python interpreter and the extensive standard library are freely available
in source or binary form for all major platforms from the Python Web site,
https://www.python.org/, and may be freely distributed. The same site also
contains distributions of and pointers to many free third party Python modules,
programs and tools, and additional documentation.
The Python interpreter is easily extended with new functions and data types
implemented in C or C++ (or other languages callable from C). Python is also
suitable as an extension language for customizable applications.
This tutorial introduces the reader informally to the basic concepts and
features of the Python language and system. It helps to have a Python
interpreter handy for hands-on experience, but all examples are self-contained,
so the tutorial can be read off-line as well.
For a description of standard objects and modules, see :ref:`library-index`.
:ref:`reference-index` gives a more formal definition of the language. To write
extensions in C or C++, read :ref:`extending-index` and
:ref:`c-api-index`. There are also several books covering Python in depth.
This tutorial does not attempt to be comprehensive and cover every single
feature, or even every commonly used feature. Instead, it introduces many of
Python's most noteworthy features, and will give you a good idea of the
language's flavor and style. After reading it, you will be able to read and
write Python modules and programs, and you will be ready to learn more about the
various Python library modules described in :ref:`library-index`.
The :ref:`glossary` is also worth going through.
.. toctree::
:numbered:
appetite.rst
interpreter.rst
introduction.rst
controlflow.rst
datastructures.rst
modules.rst
inputoutput.rst
errors.rst
classes.rst
stdlib.rst
stdlib2.rst
venv.rst
whatnow.rst
interactive.rst
floatingpoint.rst
appendix.rst

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.. _tut-io:
****************
Input and Output
****************
There are several ways to present the output of a program; data can be printed
in a human-readable form, or written to a file for future use. This chapter will
discuss some of the possibilities.
.. _tut-formatting:
Fancier Output Formatting
=========================
So far we've encountered two ways of writing values: *expression statements* and
the :func:`print` function. (A third way is using the :meth:`write` method
of file objects; the standard output file can be referenced as ``sys.stdout``.
See the Library Reference for more information on this.)
Often you'll want more control over the formatting of your output than simply
printing space-separated values. There are two ways to format your output; the
first way is to do all the string handling yourself; using string slicing and
concatenation operations you can create any layout you can imagine. The
string type has some methods that perform useful operations for padding
strings to a given column width; these will be discussed shortly. The second
way is to use :ref:`formatted string literals <f-strings>`, or the
:meth:`str.format` method.
The :mod:`string` module contains a :class:`~string.Template` class which offers
yet another way to substitute values into strings.
One question remains, of course: how do you convert values to strings? Luckily,
Python has ways to convert any value to a string: pass it to the :func:`repr`
or :func:`str` functions.
The :func:`str` function is meant to return representations of values which are
fairly human-readable, while :func:`repr` is meant to generate representations
which can be read by the interpreter (or will force a :exc:`SyntaxError` if
there is no equivalent syntax). For objects which don't have a particular
representation for human consumption, :func:`str` will return the same value as
:func:`repr`. Many values, such as numbers or structures like lists and
dictionaries, have the same representation using either function. Strings, in
particular, have two distinct representations.
Some examples::
>>> s = 'Hello, world.'
>>> str(s)
'Hello, world.'
>>> repr(s)
"'Hello, world.'"
>>> str(1/7)
'0.14285714285714285'
>>> x = 10 * 3.25
>>> y = 200 * 200
>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
>>> print(s)
The value of x is 32.5, and y is 40000...
>>> # The repr() of a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = repr(hello)
>>> print(hellos)
'hello, world\n'
>>> # The argument to repr() may be any Python object:
... repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"
Here are two ways to write a table of squares and cubes::
>>> for x in range(1, 11):
... print(repr(x).rjust(2), repr(x*x).rjust(3), end=' ')
... # Note use of 'end' on previous line
... print(repr(x*x*x).rjust(4))
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
>>> for x in range(1, 11):
... print('{0:2d} {1:3d} {2:4d}'.format(x, x*x, x*x*x))
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
(Note that in the first example, one space between each column was added by the
way :func:`print` works: by default it adds spaces between its arguments.)
This example demonstrates the :meth:`str.rjust` method of string
objects, which right-justifies a string in a field of a given width by padding
it with spaces on the left. There are similar methods :meth:`str.ljust` and
:meth:`str.center`. These methods do not write anything, they just return a
new string. If the input string is too long, they don't truncate it, but
return it unchanged; this will mess up your column lay-out but that's usually
better than the alternative, which would be lying about a value. (If you
really want truncation you can always add a slice operation, as in
``x.ljust(n)[:n]``.)
There is another method, :meth:`str.zfill`, which pads a numeric string on the
left with zeros. It understands about plus and minus signs::
>>> '12'.zfill(5)
'00012'
>>> '-3.14'.zfill(7)
'-003.14'
>>> '3.14159265359'.zfill(5)
'3.14159265359'
Basic usage of the :meth:`str.format` method looks like this::
>>> print('We are the {} who say "{}!"'.format('knights', 'Ni'))
We are the knights who say "Ni!"
The brackets and characters within them (called format fields) are replaced with
the objects passed into the :meth:`str.format` method. A number in the
brackets can be used to refer to the position of the object passed into the
:meth:`str.format` method. ::
>>> print('{0} and {1}'.format('spam', 'eggs'))
spam and eggs
>>> print('{1} and {0}'.format('spam', 'eggs'))
eggs and spam
If keyword arguments are used in the :meth:`str.format` method, their values
are referred to by using the name of the argument. ::
>>> print('This {food} is {adjective}.'.format(
... food='spam', adjective='absolutely horrible'))
This spam is absolutely horrible.
Positional and keyword arguments can be arbitrarily combined::
>>> print('The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred',
other='Georg'))
The story of Bill, Manfred, and Georg.
``'!a'`` (apply :func:`ascii`), ``'!s'`` (apply :func:`str`) and ``'!r'``
(apply :func:`repr`) can be used to convert the value before it is formatted::
>>> contents = 'eels'
>>> print('My hovercraft is full of {}.'.format(contents))
My hovercraft is full of eels.
>>> print('My hovercraft is full of {!r}.'.format(contents))
My hovercraft is full of 'eels'.
An optional ``':'`` and format specifier can follow the field name. This allows
greater control over how the value is formatted. The following example
rounds Pi to three places after the decimal.
>>> import math
>>> print('The value of PI is approximately {0:.3f}.'.format(math.pi))
The value of PI is approximately 3.142.
Passing an integer after the ``':'`` will cause that field to be a minimum
number of characters wide. This is useful for making tables pretty. ::
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print('{0:10} ==> {1:10d}'.format(name, phone))
...
Jack ==> 4098
Dcab ==> 7678
Sjoerd ==> 4127
If you have a really long format string that you don't want to split up, it
would be nice if you could reference the variables to be formatted by name
instead of by position. This can be done by simply passing the dict and using
square brackets ``'[]'`` to access the keys ::
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print('Jack: {0[Jack]:d}; Sjoerd: {0[Sjoerd]:d}; '
... 'Dcab: {0[Dcab]:d}'.format(table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This could also be done by passing the table as keyword arguments with the '**'
notation. ::
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print('Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This is particularly useful in combination with the built-in function
:func:`vars`, which returns a dictionary containing all local variables.
For a complete overview of string formatting with :meth:`str.format`, see
:ref:`formatstrings`.
Old string formatting
---------------------
The ``%`` operator can also be used for string formatting. It interprets the
left argument much like a :c:func:`sprintf`\ -style format string to be applied
to the right argument, and returns the string resulting from this formatting
operation. For example::
>>> import math
>>> print('The value of PI is approximately %5.3f.' % math.pi)
The value of PI is approximately 3.142.
More information can be found in the :ref:`old-string-formatting` section.
.. _tut-files:
Reading and Writing Files
=========================
.. index::
builtin: open
object: file
:func:`open` returns a :term:`file object`, and is most commonly used with
two arguments: ``open(filename, mode)``.
::
>>> f = open('workfile', 'w')
.. XXX str(f) is <io.TextIOWrapper object at 0x82e8dc4>
>>> print(f)
<open file 'workfile', mode 'w' at 80a0960>
The first argument is a string containing the filename. The second argument is
another string containing a few characters describing the way in which the file
will be used. *mode* can be ``'r'`` when the file will only be read, ``'w'``
for only writing (an existing file with the same name will be erased), and
``'a'`` opens the file for appending; any data written to the file is
automatically added to the end. ``'r+'`` opens the file for both reading and
writing. The *mode* argument is optional; ``'r'`` will be assumed if it's
omitted.
Normally, files are opened in :dfn:`text mode`, that means, you read and write
strings from and to the file, which are encoded in a specific encoding. If
encoding is not specified, the default is platform dependent (see
:func:`open`). ``'b'`` appended to the mode opens the file in
:dfn:`binary mode`: now the data is read and written in the form of bytes
objects. This mode should be used for all files that don't contain text.
In text mode, the default when reading is to convert platform-specific line
endings (``\n`` on Unix, ``\r\n`` on Windows) to just ``\n``. When writing in
text mode, the default is to convert occurrences of ``\n`` back to
platform-specific line endings. This behind-the-scenes modification
to file data is fine for text files, but will corrupt binary data like that in
:file:`JPEG` or :file:`EXE` files. Be very careful to use binary mode when
reading and writing such files.
It is good practice to use the :keyword:`with` keyword when dealing
with file objects. The advantage is that the file is properly closed
after its suite finishes, even if an exception is raised at some
point. Using :keyword:`with` is also much shorter than writing
equivalent :keyword:`try`\ -\ :keyword:`finally` blocks::
>>> with open('workfile') as f:
... read_data = f.read()
>>> f.closed
True
If you're not using the :keyword:`with` keyword, then you should call
``f.close()`` to close the file and immediately free up any system
resources used by it. If you don't explicitly close a file, Python's
garbage collector will eventually destroy the object and close the
open file for you, but the file may stay open for a while. Another
risk is that different Python implementations will do this clean-up at
different times.
After a file object is closed, either by a :keyword:`with` statement
or by calling ``f.close()``, attempts to use the file object will
automatically fail. ::
>>> f.close()
>>> f.read()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: I/O operation on closed file.
.. _tut-filemethods:
Methods of File Objects
-----------------------
The rest of the examples in this section will assume that a file object called
``f`` has already been created.
To read a file's contents, call ``f.read(size)``, which reads some quantity of
data and returns it as a string (in text mode) or bytes object (in binary mode).
*size* is an optional numeric argument. When *size* is omitted or negative, the
entire contents of the file will be read and returned; it's your problem if the
file is twice as large as your machine's memory. Otherwise, at most *size* bytes
are read and returned.
If the end of the file has been reached, ``f.read()`` will return an empty
string (``''``). ::
>>> f.read()
'This is the entire file.\n'
>>> f.read()
''
``f.readline()`` reads a single line from the file; a newline character (``\n``)
is left at the end of the string, and is only omitted on the last line of the
file if the file doesn't end in a newline. This makes the return value
unambiguous; if ``f.readline()`` returns an empty string, the end of the file
has been reached, while a blank line is represented by ``'\n'``, a string
containing only a single newline. ::
>>> f.readline()
'This is the first line of the file.\n'
>>> f.readline()
'Second line of the file\n'
>>> f.readline()
''
For reading lines from a file, you can loop over the file object. This is memory
efficient, fast, and leads to simple code::
>>> for line in f:
... print(line, end='')
...
This is the first line of the file.
Second line of the file
If you want to read all the lines of a file in a list you can also use
``list(f)`` or ``f.readlines()``.
``f.write(string)`` writes the contents of *string* to the file, returning
the number of characters written. ::
>>> f.write('This is a test\n')
15
Other types of objects need to be converted -- either to a string (in text mode)
or a bytes object (in binary mode) -- before writing them::
>>> value = ('the answer', 42)
>>> s = str(value) # convert the tuple to string
>>> f.write(s)
18
``f.tell()`` returns an integer giving the file object's current position in the file
represented as number of bytes from the beginning of the file when in binary mode and
an opaque number when in text mode.
To change the file object's position, use ``f.seek(offset, from_what)``. The position is computed
from adding *offset* to a reference point; the reference point is selected by
the *from_what* argument. A *from_what* value of 0 measures from the beginning
of the file, 1 uses the current file position, and 2 uses the end of the file as
the reference point. *from_what* can be omitted and defaults to 0, using the
beginning of the file as the reference point. ::
>>> f = open('workfile', 'rb+')
>>> f.write(b'0123456789abcdef')
16
>>> f.seek(5) # Go to the 6th byte in the file
5
>>> f.read(1)
b'5'
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
13
>>> f.read(1)
b'd'
In text files (those opened without a ``b`` in the mode string), only seeks
relative to the beginning of the file are allowed (the exception being seeking
to the very file end with ``seek(0, 2)``) and the only valid *offset* values are
those returned from the ``f.tell()``, or zero. Any other *offset* value produces
undefined behaviour.
File objects have some additional methods, such as :meth:`~file.isatty` and
:meth:`~file.truncate` which are less frequently used; consult the Library
Reference for a complete guide to file objects.
.. _tut-json:
Saving structured data with :mod:`json`
---------------------------------------
.. index:: module: json
Strings can easily be written to and read from a file. Numbers take a bit more
effort, since the :meth:`read` method only returns strings, which will have to
be passed to a function like :func:`int`, which takes a string like ``'123'``
and returns its numeric value 123. When you want to save more complex data
types like nested lists and dictionaries, parsing and serializing by hand
becomes complicated.
Rather than having users constantly writing and debugging code to save
complicated data types to files, Python allows you to use the popular data
interchange format called `JSON (JavaScript Object Notation)
<http://json.org>`_. The standard module called :mod:`json` can take Python
data hierarchies, and convert them to string representations; this process is
called :dfn:`serializing`. Reconstructing the data from the string representation
is called :dfn:`deserializing`. Between serializing and deserializing, the
string representing the object may have been stored in a file or data, or
sent over a network connection to some distant machine.
.. note::
The JSON format is commonly used by modern applications to allow for data
exchange. Many programmers are already familiar with it, which makes
it a good choice for interoperability.
If you have an object ``x``, you can view its JSON string representation with a
simple line of code::
>>> import json
>>> json.dumps([1, 'simple', 'list'])
'[1, "simple", "list"]'
Another variant of the :func:`~json.dumps` function, called :func:`~json.dump`,
simply serializes the object to a :term:`text file`. So if ``f`` is a
:term:`text file` object opened for writing, we can do this::
json.dump(x, f)
To decode the object again, if ``f`` is a :term:`text file` object which has
been opened for reading::
x = json.load(f)
This simple serialization technique can handle lists and dictionaries, but
serializing arbitrary class instances in JSON requires a bit of extra effort.
The reference for the :mod:`json` module contains an explanation of this.
.. seealso::
:mod:`pickle` - the pickle module
Contrary to :ref:`JSON <tut-json>`, *pickle* is a protocol which allows
the serialization of arbitrarily complex Python objects. As such, it is
specific to Python and cannot be used to communicate with applications
written in other languages. It is also insecure by default:
deserializing pickle data coming from an untrusted source can execute
arbitrary code, if the data was crafted by a skilled attacker.

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.. _tut-interacting:
**************************************************
Interactive Input Editing and History Substitution
**************************************************
Some versions of the Python interpreter support editing of the current input
line and history substitution, similar to facilities found in the Korn shell and
the GNU Bash shell. This is implemented using the `GNU Readline`_ library,
which supports various styles of editing. This library has its own
documentation which we won't duplicate here.
.. _tut-keybindings:
Tab Completion and History Editing
==================================
Completion of variable and module names is
:ref:`automatically enabled <rlcompleter-config>` at interpreter startup so
that the :kbd:`Tab` key invokes the completion function; it looks at
Python statement names, the current local variables, and the available
module names. For dotted expressions such as ``string.a``, it will evaluate
the expression up to the final ``'.'`` and then suggest completions from
the attributes of the resulting object. Note that this may execute
application-defined code if an object with a :meth:`__getattr__` method
is part of the expression. The default configuration also saves your
history into a file named :file:`.python_history` in your user directory.
The history will be available again during the next interactive interpreter
session.
.. _tut-commentary:
Alternatives to the Interactive Interpreter
===========================================
This facility is an enormous step forward compared to earlier versions of the
interpreter; however, some wishes are left: It would be nice if the proper
indentation were suggested on continuation lines (the parser knows if an indent
token is required next). The completion mechanism might use the interpreter's
symbol table. A command to check (or even suggest) matching parentheses,
quotes, etc., would also be useful.
One alternative enhanced interactive interpreter that has been around for quite
some time is IPython_, which features tab completion, object exploration and
advanced history management. It can also be thoroughly customized and embedded
into other applications. Another similar enhanced interactive environment is
bpython_.
.. _GNU Readline: https://tiswww.case.edu/php/chet/readline/rltop.html
.. _IPython: https://ipython.org/
.. _bpython: http://www.bpython-interpreter.org/

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.. _tut-using:
****************************
Using the Python Interpreter
****************************
.. _tut-invoking:
Invoking the Interpreter
========================
The Python interpreter is usually installed as :file:`/usr/local/bin/python3.6`
on those machines where it is available; putting :file:`/usr/local/bin` in your
Unix shell's search path makes it possible to start it by typing the command:
.. code-block:: text
python3.6
to the shell. [#]_ Since the choice of the directory where the interpreter lives
is an installation option, other places are possible; check with your local
Python guru or system administrator. (E.g., :file:`/usr/local/python` is a
popular alternative location.)
On Windows machines, the Python installation is usually placed in
:file:`C:\\Python36`, though you can change this when you're running the
installer. To add this directory to your path, you can type the following
command into the command prompt in a DOS box::
set path=%path%;C:\python36
Typing an end-of-file character (:kbd:`Control-D` on Unix, :kbd:`Control-Z` on
Windows) at the primary prompt causes the interpreter to exit with a zero exit
status. If that doesn't work, you can exit the interpreter by typing the
following command: ``quit()``.
The interpreter's line-editing features include interactive editing, history
substitution and code completion on systems that support readline. Perhaps the
quickest check to see whether command line editing is supported is typing
:kbd:`Control-P` to the first Python prompt you get. If it beeps, you have command
line editing; see Appendix :ref:`tut-interacting` for an introduction to the
keys. If nothing appears to happen, or if ``^P`` is echoed, command line
editing isn't available; you'll only be able to use backspace to remove
characters from the current line.
The interpreter operates somewhat like the Unix shell: when called with standard
input connected to a tty device, it reads and executes commands interactively;
when called with a file name argument or with a file as standard input, it reads
and executes a *script* from that file.
A second way of starting the interpreter is ``python -c command [arg] ...``,
which executes the statement(s) in *command*, analogous to the shell's
:option:`-c` option. Since Python statements often contain spaces or other
characters that are special to the shell, it is usually advised to quote
*command* in its entirety with single quotes.
Some Python modules are also useful as scripts. These can be invoked using
``python -m module [arg] ...``, which executes the source file for *module* as
if you had spelled out its full name on the command line.
When a script file is used, it is sometimes useful to be able to run the script
and enter interactive mode afterwards. This can be done by passing :option:`-i`
before the script.
All command line options are described in :ref:`using-on-general`.
.. _tut-argpassing:
Argument Passing
----------------
When known to the interpreter, the script name and additional arguments
thereafter are turned into a list of strings and assigned to the ``argv``
variable in the ``sys`` module. You can access this list by executing ``import
sys``. The length of the list is at least one; when no script and no arguments
are given, ``sys.argv[0]`` is an empty string. When the script name is given as
``'-'`` (meaning standard input), ``sys.argv[0]`` is set to ``'-'``. When
:option:`-c` *command* is used, ``sys.argv[0]`` is set to ``'-c'``. When
:option:`-m` *module* is used, ``sys.argv[0]`` is set to the full name of the
located module. Options found after :option:`-c` *command* or :option:`-m`
*module* are not consumed by the Python interpreter's option processing but
left in ``sys.argv`` for the command or module to handle.
.. _tut-interactive:
Interactive Mode
----------------
When commands are read from a tty, the interpreter is said to be in *interactive
mode*. In this mode it prompts for the next command with the *primary prompt*,
usually three greater-than signs (``>>>``); for continuation lines it prompts
with the *secondary prompt*, by default three dots (``...``). The interpreter
prints a welcome message stating its version number and a copyright notice
before printing the first prompt:
.. code-block:: shell-session
$ python3.6
Python 3.6 (default, Sep 16 2015, 09:25:04)
[GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
.. XXX update for new releases
Continuation lines are needed when entering a multi-line construct. As an
example, take a look at this :keyword:`if` statement::
>>> the_world_is_flat = True
>>> if the_world_is_flat:
... print("Be careful not to fall off!")
...
Be careful not to fall off!
For more on interactive mode, see :ref:`tut-interac`.
.. _tut-interp:
The Interpreter and Its Environment
===================================
.. _tut-source-encoding:
Source Code Encoding
--------------------
By default, Python source files are treated as encoded in UTF-8. In that
encoding, characters of most languages in the world can be used simultaneously
in string literals, identifiers and comments --- although the standard library
only uses ASCII characters for identifiers, a convention that any portable code
should follow. To display all these characters properly, your editor must
recognize that the file is UTF-8, and it must use a font that supports all the
characters in the file.
To declare an encoding other than the default one, a special comment line
should be added as the *first* line of the file. The syntax is as follows::
# -*- coding: encoding -*-
where *encoding* is one of the valid :mod:`codecs` supported by Python.
For example, to declare that Windows-1252 encoding is to be used, the first
line of your source code file should be::
# -*- coding: cp1252 -*-
One exception to the *first line* rule is when the source code starts with a
:ref:`UNIX "shebang" line <tut-scripts>`. In this case, the encoding
declaration should be added as the second line of the file. For example::
#!/usr/bin/env python3
# -*- coding: cp1252 -*-
.. rubric:: Footnotes
.. [#] On Unix, the Python 3.x interpreter is by default not installed with the
executable named ``python``, so that it does not conflict with a
simultaneously installed Python 2.x executable.

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.. _tut-informal:
**********************************
An Informal Introduction to Python
**********************************
In the following examples, input and output are distinguished by the presence or
absence of prompts (:term:`>>>` and :term:`...`): to repeat the example, you must type
everything after the prompt, when the prompt appears; lines that do not begin
with a prompt are output from the interpreter. Note that a secondary prompt on a
line by itself in an example means you must type a blank line; this is used to
end a multi-line command.
.. index:: single: # (hash); comment
Many of the examples in this manual, even those entered at the interactive
prompt, include comments. Comments in Python start with the hash character,
``#``, and extend to the end of the physical line. A comment may appear at the
start of a line or following whitespace or code, but not within a string
literal. A hash character within a string literal is just a hash character.
Since comments are to clarify code and are not interpreted by Python, they may
be omitted when typing in examples.
Some examples::
# this is the first comment
spam = 1 # and this is the second comment
# ... and now a third!
text = "# This is not a comment because it's inside quotes."
.. _tut-calculator:
Using Python as a Calculator
============================
Let's try some simple Python commands. Start the interpreter and wait for the
primary prompt, ``>>>``. (It shouldn't take long.)
.. _tut-numbers:
Numbers
-------
The interpreter acts as a simple calculator: you can type an expression at it
and it will write the value. Expression syntax is straightforward: the
operators ``+``, ``-``, ``*`` and ``/`` work just like in most other languages
(for example, Pascal or C); parentheses (``()``) can be used for grouping.
For example::
>>> 2 + 2
4
>>> 50 - 5*6
20
>>> (50 - 5*6) / 4
5.0
>>> 8 / 5 # division always returns a floating point number
1.6
The integer numbers (e.g. ``2``, ``4``, ``20``) have type :class:`int`,
the ones with a fractional part (e.g. ``5.0``, ``1.6``) have type
:class:`float`. We will see more about numeric types later in the tutorial.
Division (``/``) always returns a float. To do :term:`floor division` and
get an integer result (discarding any fractional result) you can use the ``//``
operator; to calculate the remainder you can use ``%``::
>>> 17 / 3 # classic division returns a float
5.666666666666667
>>>
>>> 17 // 3 # floor division discards the fractional part
5
>>> 17 % 3 # the % operator returns the remainder of the division
2
>>> 5 * 3 + 2 # result * divisor + remainder
17
With Python, it is possible to use the ``**`` operator to calculate powers [#]_::
>>> 5 ** 2 # 5 squared
25
>>> 2 ** 7 # 2 to the power of 7
128
The equal sign (``=``) is used to assign a value to a variable. Afterwards, no
result is displayed before the next interactive prompt::
>>> width = 20
>>> height = 5 * 9
>>> width * height
900
If a variable is not "defined" (assigned a value), trying to use it will
give you an error::
>>> n # try to access an undefined variable
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'n' is not defined
There is full support for floating point; operators with mixed type operands
convert the integer operand to floating point::
>>> 4 * 3.75 - 1
14.0
In interactive mode, the last printed expression is assigned to the variable
``_``. This means that when you are using Python as a desk calculator, it is
somewhat easier to continue calculations, for example::
>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
12.5625
>>> price + _
113.0625
>>> round(_, 2)
113.06
This variable should be treated as read-only by the user. Don't explicitly
assign a value to it --- you would create an independent local variable with the
same name masking the built-in variable with its magic behavior.
In addition to :class:`int` and :class:`float`, Python supports other types of
numbers, such as :class:`~decimal.Decimal` and :class:`~fractions.Fraction`.
Python also has built-in support for :ref:`complex numbers <typesnumeric>`,
and uses the ``j`` or ``J`` suffix to indicate the imaginary part
(e.g. ``3+5j``).
.. _tut-strings:
Strings
-------
Besides numbers, Python can also manipulate strings, which can be expressed
in several ways. They can be enclosed in single quotes (``'...'``) or
double quotes (``"..."``) with the same result [#]_. ``\`` can be used
to escape quotes::
>>> 'spam eggs' # single quotes
'spam eggs'
>>> 'doesn\'t' # use \' to escape the single quote...
"doesn't"
>>> "doesn't" # ...or use double quotes instead
"doesn't"
>>> '"Yes," they said.'
'"Yes," they said.'
>>> "\"Yes,\" they said."
'"Yes," they said.'
>>> '"Isn\'t," they said.'
'"Isn\'t," they said.'
In the interactive interpreter, the output string is enclosed in quotes and
special characters are escaped with backslashes. While this might sometimes
look different from the input (the enclosing quotes could change), the two
strings are equivalent. The string is enclosed in double quotes if
the string contains a single quote and no double quotes, otherwise it is
enclosed in single quotes. The :func:`print` function produces a more
readable output, by omitting the enclosing quotes and by printing escaped
and special characters::
>>> '"Isn\'t," they said.'
'"Isn\'t," they said.'
>>> print('"Isn\'t," they said.')
"Isn't," they said.
>>> s = 'First line.\nSecond line.' # \n means newline
>>> s # without print(), \n is included in the output
'First line.\nSecond line.'
>>> print(s) # with print(), \n produces a new line
First line.
Second line.
If you don't want characters prefaced by ``\`` to be interpreted as
special characters, you can use *raw strings* by adding an ``r`` before
the first quote::
>>> print('C:\some\name') # here \n means newline!
C:\some
ame
>>> print(r'C:\some\name') # note the r before the quote
C:\some\name
String literals can span multiple lines. One way is using triple-quotes:
``"""..."""`` or ``'''...'''``. End of lines are automatically
included in the string, but it's possible to prevent this by adding a ``\`` at
the end of the line. The following example::
print("""\
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
""")
produces the following output (note that the initial newline is not included):
.. code-block:: text
Usage: thingy [OPTIONS]
-h Display this usage message
-H hostname Hostname to connect to
Strings can be concatenated (glued together) with the ``+`` operator, and
repeated with ``*``::
>>> # 3 times 'un', followed by 'ium'
>>> 3 * 'un' + 'ium'
'unununium'
Two or more *string literals* (i.e. the ones enclosed between quotes) next
to each other are automatically concatenated. ::
>>> 'Py' 'thon'
'Python'
This feature is particularly useful when you want to break long strings::
>>> text = ('Put several strings within parentheses '
... 'to have them joined together.')
>>> text
'Put several strings within parentheses to have them joined together.'
This only works with two literals though, not with variables or expressions::
>>> prefix = 'Py'
>>> prefix 'thon' # can't concatenate a variable and a string literal
...
SyntaxError: invalid syntax
>>> ('un' * 3) 'ium'
...
SyntaxError: invalid syntax
If you want to concatenate variables or a variable and a literal, use ``+``::
>>> prefix + 'thon'
'Python'
Strings can be *indexed* (subscripted), with the first character having index 0.
There is no separate character type; a character is simply a string of size
one::
>>> word = 'Python'
>>> word[0] # character in position 0
'P'
>>> word[5] # character in position 5
'n'
Indices may also be negative numbers, to start counting from the right::
>>> word[-1] # last character
'n'
>>> word[-2] # second-last character
'o'
>>> word[-6]
'P'
Note that since -0 is the same as 0, negative indices start from -1.
In addition to indexing, *slicing* is also supported. While indexing is used
to obtain individual characters, *slicing* allows you to obtain substring::
>>> word[0:2] # characters from position 0 (included) to 2 (excluded)
'Py'
>>> word[2:5] # characters from position 2 (included) to 5 (excluded)
'tho'
Note how the start is always included, and the end always excluded. This
makes sure that ``s[:i] + s[i:]`` is always equal to ``s``::
>>> word[:2] + word[2:]
'Python'
>>> word[:4] + word[4:]
'Python'
Slice indices have useful defaults; an omitted first index defaults to zero, an
omitted second index defaults to the size of the string being sliced. ::
>>> word[:2] # character from the beginning to position 2 (excluded)
'Py'
>>> word[4:] # characters from position 4 (included) to the end
'on'
>>> word[-2:] # characters from the second-last (included) to the end
'on'
One way to remember how slices work is to think of the indices as pointing
*between* characters, with the left edge of the first character numbered 0.
Then the right edge of the last character of a string of *n* characters has
index *n*, for example::
+---+---+---+---+---+---+
| P | y | t | h | o | n |
+---+---+---+---+---+---+
0 1 2 3 4 5 6
-6 -5 -4 -3 -2 -1
The first row of numbers gives the position of the indices 0...6 in the string;
the second row gives the corresponding negative indices. The slice from *i* to
*j* consists of all characters between the edges labeled *i* and *j*,
respectively.
For non-negative indices, the length of a slice is the difference of the
indices, if both are within bounds. For example, the length of ``word[1:3]`` is
2.
Attempting to use an index that is too large will result in an error::
>>> word[42] # the word only has 6 characters
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: string index out of range
However, out of range slice indexes are handled gracefully when used for
slicing::
>>> word[4:42]
'on'
>>> word[42:]
''
Python strings cannot be changed --- they are :term:`immutable`.
Therefore, assigning to an indexed position in the string results in an error::
>>> word[0] = 'J'
...
TypeError: 'str' object does not support item assignment
>>> word[2:] = 'py'
...
TypeError: 'str' object does not support item assignment
If you need a different string, you should create a new one::
>>> 'J' + word[1:]
'Jython'
>>> word[:2] + 'py'
'Pypy'
The built-in function :func:`len` returns the length of a string::
>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34
.. seealso::
:ref:`textseq`
Strings are examples of *sequence types*, and support the common
operations supported by such types.
:ref:`string-methods`
Strings support a large number of methods for
basic transformations and searching.
:ref:`f-strings`
String literals that have embedded expressions.
:ref:`formatstrings`
Information about string formatting with :meth:`str.format`.
:ref:`old-string-formatting`
The old formatting operations invoked when strings are
the left operand of the ``%`` operator are described in more detail here.
.. _tut-lists:
Lists
-----
Python knows a number of *compound* data types, used to group together other
values. The most versatile is the *list*, which can be written as a list of
comma-separated values (items) between square brackets. Lists might contain
items of different types, but usually the items all have the same type. ::
>>> squares = [1, 4, 9, 16, 25]
>>> squares
[1, 4, 9, 16, 25]
Like strings (and all other built-in :term:`sequence` type), lists can be
indexed and sliced::
>>> squares[0] # indexing returns the item
1
>>> squares[-1]
25
>>> squares[-3:] # slicing returns a new list
[9, 16, 25]
All slice operations return a new list containing the requested elements. This
means that the following slice returns a new (shallow) copy of the list::
>>> squares[:]
[1, 4, 9, 16, 25]
Lists also support operations like concatenation::
>>> squares + [36, 49, 64, 81, 100]
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
Unlike strings, which are :term:`immutable`, lists are a :term:`mutable`
type, i.e. it is possible to change their content::
>>> cubes = [1, 8, 27, 65, 125] # something's wrong here
>>> 4 ** 3 # the cube of 4 is 64, not 65!
64
>>> cubes[3] = 64 # replace the wrong value
>>> cubes
[1, 8, 27, 64, 125]
You can also add new items at the end of the list, by using
the :meth:`~list.append` *method* (we will see more about methods later)::
>>> cubes.append(216) # add the cube of 6
>>> cubes.append(7 ** 3) # and the cube of 7
>>> cubes
[1, 8, 27, 64, 125, 216, 343]
Assignment to slices is also possible, and this can even change the size of the
list or clear it entirely::
>>> letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> letters
['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> # replace some values
>>> letters[2:5] = ['C', 'D', 'E']
>>> letters
['a', 'b', 'C', 'D', 'E', 'f', 'g']
>>> # now remove them
>>> letters[2:5] = []
>>> letters
['a', 'b', 'f', 'g']
>>> # clear the list by replacing all the elements with an empty list
>>> letters[:] = []
>>> letters
[]
The built-in function :func:`len` also applies to lists::
>>> letters = ['a', 'b', 'c', 'd']
>>> len(letters)
4
It is possible to nest lists (create lists containing other lists), for
example::
>>> a = ['a', 'b', 'c']
>>> n = [1, 2, 3]
>>> x = [a, n]
>>> x
[['a', 'b', 'c'], [1, 2, 3]]
>>> x[0]
['a', 'b', 'c']
>>> x[0][1]
'b'
.. _tut-firststeps:
First Steps Towards Programming
===============================
Of course, we can use Python for more complicated tasks than adding two and two
together. For instance, we can write an initial sub-sequence of the *Fibonacci*
series as follows::
>>> # Fibonacci series:
... # the sum of two elements defines the next
... a, b = 0, 1
>>> while b < 10:
... print(b)
... a, b = b, a+b
...
1
1
2
3
5
8
This example introduces several new features.
* The first line contains a *multiple assignment*: the variables ``a`` and ``b``
simultaneously get the new values 0 and 1. On the last line this is used again,
demonstrating that the expressions on the right-hand side are all evaluated
first before any of the assignments take place. The right-hand side expressions
are evaluated from the left to the right.
* The :keyword:`while` loop executes as long as the condition (here: ``b < 10``)
remains true. In Python, like in C, any non-zero integer value is true; zero is
false. The condition may also be a string or list value, in fact any sequence;
anything with a non-zero length is true, empty sequences are false. The test
used in the example is a simple comparison. The standard comparison operators
are written the same as in C: ``<`` (less than), ``>`` (greater than), ``==``
(equal to), ``<=`` (less than or equal to), ``>=`` (greater than or equal to)
and ``!=`` (not equal to).
* The *body* of the loop is *indented*: indentation is Python's way of grouping
statements. At the interactive prompt, you have to type a tab or space(s) for
each indented line. In practice you will prepare more complicated input
for Python with a text editor; all decent text editors have an auto-indent
facility. When a compound statement is entered interactively, it must be
followed by a blank line to indicate completion (since the parser cannot
guess when you have typed the last line). Note that each line within a basic
block must be indented by the same amount.
* The :func:`print` function writes the value of the argument(s) it is given.
It differs from just writing the expression you want to write (as we did
earlier in the calculator examples) in the way it handles multiple arguments,
floating point quantities, and strings. Strings are printed without quotes,
and a space is inserted between items, so you can format things nicely, like
this::
>>> i = 256*256
>>> print('The value of i is', i)
The value of i is 65536
The keyword argument *end* can be used to avoid the newline after the output,
or end the output with a different string::
>>> a, b = 0, 1
>>> while b < 1000:
... print(b, end=',')
... a, b = b, a+b
...
1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,
.. rubric:: Footnotes
.. [#] Since ``**`` has higher precedence than ``-``, ``-3**2`` will be
interpreted as ``-(3**2)`` and thus result in ``-9``. To avoid this
and get ``9``, you can use ``(-3)**2``.
.. [#] Unlike other languages, special characters such as ``\n`` have the
same meaning with both single (``'...'``) and double (``"..."``) quotes.
The only difference between the two is that within single quotes you don't
need to escape ``"`` (but you have to escape ``\'``) and vice versa.

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.. _tut-modules:
*******
Modules
*******
If you quit from the Python interpreter and enter it again, the definitions you
have made (functions and variables) are lost. Therefore, if you want to write a
somewhat longer program, you are better off using a text editor to prepare the
input for the interpreter and running it with that file as input instead. This
is known as creating a *script*. As your program gets longer, you may want to
split it into several files for easier maintenance. You may also want to use a
handy function that you've written in several programs without copying its
definition into each program.
To support this, Python has a way to put definitions in a file and use them in a
script or in an interactive instance of the interpreter. Such a file is called a
*module*; definitions from a module can be *imported* into other modules or into
the *main* module (the collection of variables that you have access to in a
script executed at the top level and in calculator mode).
A module is a file containing Python definitions and statements. The file name
is the module name with the suffix :file:`.py` appended. Within a module, the
module's name (as a string) is available as the value of the global variable
``__name__``. For instance, use your favorite text editor to create a file
called :file:`fibo.py` in the current directory with the following contents::
# Fibonacci numbers module
def fib(n): # write Fibonacci series up to n
a, b = 0, 1
while b < n:
print(b, end=' ')
a, b = b, a+b
print()
def fib2(n): # return Fibonacci series up to n
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
Now enter the Python interpreter and import this module with the following
command::
>>> import fibo
This does not enter the names of the functions defined in ``fibo`` directly in
the current symbol table; it only enters the module name ``fibo`` there. Using
the module name you can access the functions::
>>> fibo.fib(1000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
>>> fibo.fib2(100)
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
>>> fibo.__name__
'fibo'
If you intend to use a function often you can assign it to a local name::
>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
.. _tut-moremodules:
More on Modules
===============
A module can contain executable statements as well as function definitions.
These statements are intended to initialize the module. They are executed only
the *first* time the module name is encountered in an import statement. [#]_
(They are also run if the file is executed as a script.)
Each module has its own private symbol table, which is used as the global symbol
table by all functions defined in the module. Thus, the author of a module can
use global variables in the module without worrying about accidental clashes
with a user's global variables. On the other hand, if you know what you are
doing you can touch a module's global variables with the same notation used to
refer to its functions, ``modname.itemname``.
Modules can import other modules. It is customary but not required to place all
:keyword:`import` statements at the beginning of a module (or script, for that
matter). The imported module names are placed in the importing module's global
symbol table.
There is a variant of the :keyword:`import` statement that imports names from a
module directly into the importing module's symbol table. For example::
>>> from fibo import fib, fib2
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This does not introduce the module name from which the imports are taken in the
local symbol table (so in the example, ``fibo`` is not defined).
There is even a variant to import all names that a module defines::
>>> from fibo import *
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those beginning with an underscore (``_``).
In most cases Python programmers do not use this facility since it introduces
an unknown set of names into the interpreter, possibly hiding some things
you have already defined.
Note that in general the practice of importing ``*`` from a module or package is
frowned upon, since it often causes poorly readable code. However, it is okay to
use it to save typing in interactive sessions.
If the module name is followed by :keyword:`as`, then the name
following :keyword:`as` is bound directly to the imported module.
::
>>> import fibo as fib
>>> fib.fib(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
This is effectively importing the module in the same way that ``import fibo``
will do, with the only difference of it being available as ``fib``.
It can also be used when utilising :keyword:`from` with similar effects::
>>> from fibo import fib as fibonacci
>>> fibonacci(500)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
.. note::
For efficiency reasons, each module is only imported once per interpreter
session. Therefore, if you change your modules, you must restart the
interpreter -- or, if it's just one module you want to test interactively,
use :func:`importlib.reload`, e.g. ``import importlib;
importlib.reload(modulename)``.
.. _tut-modulesasscripts:
Executing modules as scripts
----------------------------
When you run a Python module with ::
python fibo.py <arguments>
the code in the module will be executed, just as if you imported it, but with
the ``__name__`` set to ``"__main__"``. That means that by adding this code at
the end of your module::
if __name__ == "__main__":
import sys
fib(int(sys.argv[1]))
you can make the file usable as a script as well as an importable module,
because the code that parses the command line only runs if the module is
executed as the "main" file:
.. code-block:: shell-session
$ python fibo.py 50
1 1 2 3 5 8 13 21 34
If the module is imported, the code is not run::
>>> import fibo
>>>
This is often used either to provide a convenient user interface to a module, or
for testing purposes (running the module as a script executes a test suite).
.. _tut-searchpath:
The Module Search Path
----------------------
.. index:: triple: module; search; path
When a module named :mod:`spam` is imported, the interpreter first searches for
a built-in module with that name. If not found, it then searches for a file
named :file:`spam.py` in a list of directories given by the variable
:data:`sys.path`. :data:`sys.path` is initialized from these locations:
* The directory containing the input script (or the current directory when no
file is specified).
* :envvar:`PYTHONPATH` (a list of directory names, with the same syntax as the
shell variable :envvar:`PATH`).
* The installation-dependent default.
.. note::
On file systems which support symlinks, the directory containing the input
script is calculated after the symlink is followed. In other words the
directory containing the symlink is **not** added to the module search path.
After initialization, Python programs can modify :data:`sys.path`. The
directory containing the script being run is placed at the beginning of the
search path, ahead of the standard library path. This means that scripts in that
directory will be loaded instead of modules of the same name in the library
directory. This is an error unless the replacement is intended. See section
:ref:`tut-standardmodules` for more information.
.. %
Do we need stuff on zip files etc. ? DUBOIS
"Compiled" Python files
-----------------------
To speed up loading modules, Python caches the compiled version of each module
in the ``__pycache__`` directory under the name :file:`module.{version}.pyc`,
where the version encodes the format of the compiled file; it generally contains
the Python version number. For example, in CPython release 3.3 the compiled
version of spam.py would be cached as ``__pycache__/spam.cpython-33.pyc``. This
naming convention allows compiled modules from different releases and different
versions of Python to coexist.
Python checks the modification date of the source against the compiled version
to see if it's out of date and needs to be recompiled. This is a completely
automatic process. Also, the compiled modules are platform-independent, so the
same library can be shared among systems with different architectures.
Python does not check the cache in two circumstances. First, it always
recompiles and does not store the result for the module that's loaded directly
from the command line. Second, it does not check the cache if there is no
source module. To support a non-source (compiled only) distribution, the
compiled module must be in the source directory, and there must not be a source
module.
Some tips for experts:
* You can use the :option:`-O` or :option:`-OO` switches on the Python command
to reduce the size of a compiled module. The ``-O`` switch removes assert
statements, the ``-OO`` switch removes both assert statements and __doc__
strings. Since some programs may rely on having these available, you should
only use this option if you know what you're doing. "Optimized" modules have
an ``opt-`` tag and are usually smaller. Future releases may
change the effects of optimization.
* A program doesn't run any faster when it is read from a ``.pyc``
file than when it is read from a ``.py`` file; the only thing that's faster
about ``.pyc`` files is the speed with which they are loaded.
* The module :mod:`compileall` can create .pyc files for all modules in a
directory.
* There is more detail on this process, including a flow chart of the
decisions, in :pep:`3147`.
.. _tut-standardmodules:
Standard Modules
================
.. index:: module: sys
Python comes with a library of standard modules, described in a separate
document, the Python Library Reference ("Library Reference" hereafter). Some
modules are built into the interpreter; these provide access to operations that
are not part of the core of the language but are nevertheless built in, either
for efficiency or to provide access to operating system primitives such as
system calls. The set of such modules is a configuration option which also
depends on the underlying platform. For example, the :mod:`winreg` module is only
provided on Windows systems. One particular module deserves some attention:
:mod:`sys`, which is built into every Python interpreter. The variables
``sys.ps1`` and ``sys.ps2`` define the strings used as primary and secondary
prompts::
>>> import sys
>>> sys.ps1
'>>> '
>>> sys.ps2
'... '
>>> sys.ps1 = 'C> '
C> print('Yuck!')
Yuck!
C>
These two variables are only defined if the interpreter is in interactive mode.
The variable ``sys.path`` is a list of strings that determines the interpreter's
search path for modules. It is initialized to a default path taken from the
environment variable :envvar:`PYTHONPATH`, or from a built-in default if
:envvar:`PYTHONPATH` is not set. You can modify it using standard list
operations::
>>> import sys
>>> sys.path.append('/ufs/guido/lib/python')
.. _tut-dir:
The :func:`dir` Function
========================
The built-in function :func:`dir` is used to find out which names a module
defines. It returns a sorted list of strings::
>>> import fibo, sys
>>> dir(fibo)
['__name__', 'fib', 'fib2']
>>> dir(sys) # doctest: +NORMALIZE_WHITESPACE
['__displayhook__', '__doc__', '__excepthook__', '__loader__', '__name__',
'__package__', '__stderr__', '__stdin__', '__stdout__',
'_clear_type_cache', '_current_frames', '_debugmallocstats', '_getframe',
'_home', '_mercurial', '_xoptions', 'abiflags', 'api_version', 'argv',
'base_exec_prefix', 'base_prefix', 'builtin_module_names', 'byteorder',
'call_tracing', 'callstats', 'copyright', 'displayhook',
'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix',
'executable', 'exit', 'flags', 'float_info', 'float_repr_style',
'getcheckinterval', 'getdefaultencoding', 'getdlopenflags',
'getfilesystemencoding', 'getobjects', 'getprofile', 'getrecursionlimit',
'getrefcount', 'getsizeof', 'getswitchinterval', 'gettotalrefcount',
'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info',
'intern', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path',
'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1',
'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit',
'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout',
'thread_info', 'version', 'version_info', 'warnoptions']
Without arguments, :func:`dir` lists the names you have defined currently::
>>> a = [1, 2, 3, 4, 5]
>>> import fibo
>>> fib = fibo.fib
>>> dir()
['__builtins__', '__name__', 'a', 'fib', 'fibo', 'sys']
Note that it lists all types of names: variables, modules, functions, etc.
.. index:: module: builtins
:func:`dir` does not list the names of built-in functions and variables. If you
want a list of those, they are defined in the standard module
:mod:`builtins`::
>>> import builtins
>>> dir(builtins) # doctest: +NORMALIZE_WHITESPACE
['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException',
'BlockingIOError', 'BrokenPipeError', 'BufferError', 'BytesWarning',
'ChildProcessError', 'ConnectionAbortedError', 'ConnectionError',
'ConnectionRefusedError', 'ConnectionResetError', 'DeprecationWarning',
'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False',
'FileExistsError', 'FileNotFoundError', 'FloatingPointError',
'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError',
'ImportWarning', 'IndentationError', 'IndexError', 'InterruptedError',
'IsADirectoryError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
'MemoryError', 'NameError', 'None', 'NotADirectoryError', 'NotImplemented',
'NotImplementedError', 'OSError', 'OverflowError',
'PendingDeprecationWarning', 'PermissionError', 'ProcessLookupError',
'ReferenceError', 'ResourceWarning', 'RuntimeError', 'RuntimeWarning',
'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError',
'SystemExit', 'TabError', 'TimeoutError', 'True', 'TypeError',
'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError',
'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning',
'ValueError', 'Warning', 'ZeroDivisionError', '_', '__build_class__',
'__debug__', '__doc__', '__import__', '__name__', '__package__', 'abs',
'all', 'any', 'ascii', 'bin', 'bool', 'bytearray', 'bytes', 'callable',
'chr', 'classmethod', 'compile', 'complex', 'copyright', 'credits',
'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'exec', 'exit',
'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr',
'hash', 'help', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass',
'iter', 'len', 'license', 'list', 'locals', 'map', 'max', 'memoryview',
'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property',
'quit', 'range', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice',
'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'vars',
'zip']
.. _tut-packages:
Packages
========
Packages are a way of structuring Python's module namespace by using "dotted
module names". For example, the module name :mod:`A.B` designates a submodule
named ``B`` in a package named ``A``. Just like the use of modules saves the
authors of different modules from having to worry about each other's global
variable names, the use of dotted module names saves the authors of multi-module
packages like NumPy or Pillow from having to worry about
each other's module names.
Suppose you want to design a collection of modules (a "package") for the uniform
handling of sound files and sound data. There are many different sound file
formats (usually recognized by their extension, for example: :file:`.wav`,
:file:`.aiff`, :file:`.au`), so you may need to create and maintain a growing
collection of modules for the conversion between the various file formats.
There are also many different operations you might want to perform on sound data
(such as mixing, adding echo, applying an equalizer function, creating an
artificial stereo effect), so in addition you will be writing a never-ending
stream of modules to perform these operations. Here's a possible structure for
your package (expressed in terms of a hierarchical filesystem):
.. code-block:: text
sound/ Top-level package
__init__.py Initialize the sound package
formats/ Subpackage for file format conversions
__init__.py
wavread.py
wavwrite.py
aiffread.py
aiffwrite.py
auread.py
auwrite.py
...
effects/ Subpackage for sound effects
__init__.py
echo.py
surround.py
reverse.py
...
filters/ Subpackage for filters
__init__.py
equalizer.py
vocoder.py
karaoke.py
...
When importing the package, Python searches through the directories on
``sys.path`` looking for the package subdirectory.
The :file:`__init__.py` files are required to make Python treat the directories
as containing packages; this is done to prevent directories with a common name,
such as ``string``, from unintentionally hiding valid modules that occur later
on the module search path. In the simplest case, :file:`__init__.py` can just be
an empty file, but it can also execute initialization code for the package or
set the ``__all__`` variable, described later.
Users of the package can import individual modules from the package, for
example::
import sound.effects.echo
This loads the submodule :mod:`sound.effects.echo`. It must be referenced with
its full name. ::
sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)
An alternative way of importing the submodule is::
from sound.effects import echo
This also loads the submodule :mod:`echo`, and makes it available without its
package prefix, so it can be used as follows::
echo.echofilter(input, output, delay=0.7, atten=4)
Yet another variation is to import the desired function or variable directly::
from sound.effects.echo import echofilter
Again, this loads the submodule :mod:`echo`, but this makes its function
:func:`echofilter` directly available::
echofilter(input, output, delay=0.7, atten=4)
Note that when using ``from package import item``, the item can be either a
submodule (or subpackage) of the package, or some other name defined in the
package, like a function, class or variable. The ``import`` statement first
tests whether the item is defined in the package; if not, it assumes it is a
module and attempts to load it. If it fails to find it, an :exc:`ImportError`
exception is raised.
Contrarily, when using syntax like ``import item.subitem.subsubitem``, each item
except for the last must be a package; the last item can be a module or a
package but can't be a class or function or variable defined in the previous
item.
.. _tut-pkg-import-star:
Importing \* From a Package
---------------------------
.. index:: single: __all__
Now what happens when the user writes ``from sound.effects import *``? Ideally,
one would hope that this somehow goes out to the filesystem, finds which
submodules are present in the package, and imports them all. This could take a
long time and importing sub-modules might have unwanted side-effects that should
only happen when the sub-module is explicitly imported.
The only solution is for the package author to provide an explicit index of the
package. The :keyword:`import` statement uses the following convention: if a package's
:file:`__init__.py` code defines a list named ``__all__``, it is taken to be the
list of module names that should be imported when ``from package import *`` is
encountered. It is up to the package author to keep this list up-to-date when a
new version of the package is released. Package authors may also decide not to
support it, if they don't see a use for importing \* from their package. For
example, the file :file:`sound/effects/__init__.py` could contain the following
code::
__all__ = ["echo", "surround", "reverse"]
This would mean that ``from sound.effects import *`` would import the three
named submodules of the :mod:`sound` package.
If ``__all__`` is not defined, the statement ``from sound.effects import *``
does *not* import all submodules from the package :mod:`sound.effects` into the
current namespace; it only ensures that the package :mod:`sound.effects` has
been imported (possibly running any initialization code in :file:`__init__.py`)
and then imports whatever names are defined in the package. This includes any
names defined (and submodules explicitly loaded) by :file:`__init__.py`. It
also includes any submodules of the package that were explicitly loaded by
previous :keyword:`import` statements. Consider this code::
import sound.effects.echo
import sound.effects.surround
from sound.effects import *
In this example, the :mod:`echo` and :mod:`surround` modules are imported in the
current namespace because they are defined in the :mod:`sound.effects` package
when the ``from...import`` statement is executed. (This also works when
``__all__`` is defined.)
Although certain modules are designed to export only names that follow certain
patterns when you use ``import *``, it is still considered bad practice in
production code.
Remember, there is nothing wrong with using ``from Package import
specific_submodule``! In fact, this is the recommended notation unless the
importing module needs to use submodules with the same name from different
packages.
Intra-package References
------------------------
When packages are structured into subpackages (as with the :mod:`sound` package
in the example), you can use absolute imports to refer to submodules of siblings
packages. For example, if the module :mod:`sound.filters.vocoder` needs to use
the :mod:`echo` module in the :mod:`sound.effects` package, it can use ``from
sound.effects import echo``.
You can also write relative imports, with the ``from module import name`` form
of import statement. These imports use leading dots to indicate the current and
parent packages involved in the relative import. From the :mod:`surround`
module for example, you might use::
from . import echo
from .. import formats
from ..filters import equalizer
Note that relative imports are based on the name of the current module. Since
the name of the main module is always ``"__main__"``, modules intended for use
as the main module of a Python application must always use absolute imports.
Packages in Multiple Directories
--------------------------------
Packages support one more special attribute, :attr:`__path__`. This is
initialized to be a list containing the name of the directory holding the
package's :file:`__init__.py` before the code in that file is executed. This
variable can be modified; doing so affects future searches for modules and
subpackages contained in the package.
While this feature is not often needed, it can be used to extend the set of
modules found in a package.
.. rubric:: Footnotes
.. [#] In fact function definitions are also 'statements' that are 'executed'; the
execution of a module-level function definition enters the function name in
the module's global symbol table.

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.. _tut-brieftour:
**********************************
Brief Tour of the Standard Library
**********************************
.. _tut-os-interface:
Operating System Interface
==========================
The :mod:`os` module provides dozens of functions for interacting with the
operating system::
>>> import os
>>> os.getcwd() # Return the current working directory
'C:\\Python36'
>>> os.chdir('/server/accesslogs') # Change current working directory
>>> os.system('mkdir today') # Run the command mkdir in the system shell
0
Be sure to use the ``import os`` style instead of ``from os import *``. This
will keep :func:`os.open` from shadowing the built-in :func:`open` function which
operates much differently.
.. index:: builtin: help
The built-in :func:`dir` and :func:`help` functions are useful as interactive
aids for working with large modules like :mod:`os`::
>>> import os
>>> dir(os)
<returns a list of all module functions>
>>> help(os)
<returns an extensive manual page created from the module's docstrings>
For daily file and directory management tasks, the :mod:`shutil` module provides
a higher level interface that is easier to use::
>>> import shutil
>>> shutil.copyfile('data.db', 'archive.db')
'archive.db'
>>> shutil.move('/build/executables', 'installdir')
'installdir'
.. _tut-file-wildcards:
File Wildcards
==============
The :mod:`glob` module provides a function for making file lists from directory
wildcard searches::
>>> import glob
>>> glob.glob('*.py')
['primes.py', 'random.py', 'quote.py']
.. _tut-command-line-arguments:
Command Line Arguments
======================
Common utility scripts often need to process command line arguments. These
arguments are stored in the :mod:`sys` module's *argv* attribute as a list. For
instance the following output results from running ``python demo.py one two
three`` at the command line::
>>> import sys
>>> print(sys.argv)
['demo.py', 'one', 'two', 'three']
The :mod:`getopt` module processes *sys.argv* using the conventions of the Unix
:func:`getopt` function. More powerful and flexible command line processing is
provided by the :mod:`argparse` module.
.. _tut-stderr:
Error Output Redirection and Program Termination
================================================
The :mod:`sys` module also has attributes for *stdin*, *stdout*, and *stderr*.
The latter is useful for emitting warnings and error messages to make them
visible even when *stdout* has been redirected::
>>> sys.stderr.write('Warning, log file not found starting a new one\n')
Warning, log file not found starting a new one
The most direct way to terminate a script is to use ``sys.exit()``.
.. _tut-string-pattern-matching:
String Pattern Matching
=======================
The :mod:`re` module provides regular expression tools for advanced string
processing. For complex matching and manipulation, regular expressions offer
succinct, optimized solutions::
>>> import re
>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
['foot', 'fell', 'fastest']
>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
'cat in the hat'
When only simple capabilities are needed, string methods are preferred because
they are easier to read and debug::
>>> 'tea for too'.replace('too', 'two')
'tea for two'
.. _tut-mathematics:
Mathematics
===========
The :mod:`math` module gives access to the underlying C library functions for
floating point math::
>>> import math
>>> math.cos(math.pi / 4)
0.70710678118654757
>>> math.log(1024, 2)
10.0
The :mod:`random` module provides tools for making random selections::
>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
'apple'
>>> random.sample(range(100), 10) # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random() # random float
0.17970987693706186
>>> random.randrange(6) # random integer chosen from range(6)
4
The :mod:`statistics` module calculates basic statistical properties
(the mean, median, variance, etc.) of numeric data::
>>> import statistics
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> statistics.mean(data)
1.6071428571428572
>>> statistics.median(data)
1.25
>>> statistics.variance(data)
1.3720238095238095
The SciPy project <https://scipy.org> has many other modules for numerical
computations.
.. _tut-internet-access:
Internet Access
===============
There are a number of modules for accessing the internet and processing internet
protocols. Two of the simplest are :mod:`urllib.request` for retrieving data
from URLs and :mod:`smtplib` for sending mail::
>>> from urllib.request import urlopen
>>> with urlopen('http://tycho.usno.navy.mil/cgi-bin/timer.pl') as response:
... for line in response:
... line = line.decode('utf-8') # Decoding the binary data to text.
... if 'EST' in line or 'EDT' in line: # look for Eastern Time
... print(line)
<BR>Nov. 25, 09:43:32 PM EST
>>> import smtplib
>>> server = smtplib.SMTP('localhost')
>>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
... """To: jcaesar@example.org
... From: soothsayer@example.org
...
... Beware the Ides of March.
... """)
>>> server.quit()
(Note that the second example needs a mailserver running on localhost.)
.. _tut-dates-and-times:
Dates and Times
===============
The :mod:`datetime` module supplies classes for manipulating dates and times in
both simple and complex ways. While date and time arithmetic is supported, the
focus of the implementation is on efficient member extraction for output
formatting and manipulation. The module also supports objects that are timezone
aware. ::
>>> # dates are easily constructed and formatted
>>> from datetime import date
>>> now = date.today()
>>> now
datetime.date(2003, 12, 2)
>>> now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
'12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'
>>> # dates support calendar arithmetic
>>> birthday = date(1964, 7, 31)
>>> age = now - birthday
>>> age.days
14368
.. _tut-data-compression:
Data Compression
================
Common data archiving and compression formats are directly supported by modules
including: :mod:`zlib`, :mod:`gzip`, :mod:`bz2`, :mod:`lzma`, :mod:`zipfile` and
:mod:`tarfile`. ::
>>> import zlib
>>> s = b'witch which has which witches wrist watch'
>>> len(s)
41
>>> t = zlib.compress(s)
>>> len(t)
37
>>> zlib.decompress(t)
b'witch which has which witches wrist watch'
>>> zlib.crc32(s)
226805979
.. _tut-performance-measurement:
Performance Measurement
=======================
Some Python users develop a deep interest in knowing the relative performance of
different approaches to the same problem. Python provides a measurement tool
that answers those questions immediately.
For example, it may be tempting to use the tuple packing and unpacking feature
instead of the traditional approach to swapping arguments. The :mod:`timeit`
module quickly demonstrates a modest performance advantage::
>>> from timeit import Timer
>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
0.57535828626024577
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()
0.54962537085770791
In contrast to :mod:`timeit`'s fine level of granularity, the :mod:`profile` and
:mod:`pstats` modules provide tools for identifying time critical sections in
larger blocks of code.
.. _tut-quality-control:
Quality Control
===============
One approach for developing high quality software is to write tests for each
function as it is developed and to run those tests frequently during the
development process.
The :mod:`doctest` module provides a tool for scanning a module and validating
tests embedded in a program's docstrings. Test construction is as simple as
cutting-and-pasting a typical call along with its results into the docstring.
This improves the documentation by providing the user with an example and it
allows the doctest module to make sure the code remains true to the
documentation::
def average(values):
"""Computes the arithmetic mean of a list of numbers.
>>> print(average([20, 30, 70]))
40.0
"""
return sum(values) / len(values)
import doctest
doctest.testmod() # automatically validate the embedded tests
The :mod:`unittest` module is not as effortless as the :mod:`doctest` module,
but it allows a more comprehensive set of tests to be maintained in a separate
file::
import unittest
class TestStatisticalFunctions(unittest.TestCase):
def test_average(self):
self.assertEqual(average([20, 30, 70]), 40.0)
self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
with self.assertRaises(ZeroDivisionError):
average([])
with self.assertRaises(TypeError):
average(20, 30, 70)
unittest.main() # Calling from the command line invokes all tests
.. _tut-batteries-included:
Batteries Included
==================
Python has a "batteries included" philosophy. This is best seen through the
sophisticated and robust capabilities of its larger packages. For example:
* The :mod:`xmlrpc.client` and :mod:`xmlrpc.server` modules make implementing
remote procedure calls into an almost trivial task. Despite the modules
names, no direct knowledge or handling of XML is needed.
* The :mod:`email` package is a library for managing email messages, including
MIME and other :rfc:`2822`-based message documents. Unlike :mod:`smtplib` and
:mod:`poplib` which actually send and receive messages, the email package has
a complete toolset for building or decoding complex message structures
(including attachments) and for implementing internet encoding and header
protocols.
* The :mod:`json` package provides robust support for parsing this
popular data interchange format. The :mod:`csv` module supports
direct reading and writing of files in Comma-Separated Value format,
commonly supported by databases and spreadsheets. XML processing is
supported by the :mod:`xml.etree.ElementTree`, :mod:`xml.dom` and
:mod:`xml.sax` packages. Together, these modules and packages
greatly simplify data interchange between Python applications and
other tools.
* The :mod:`sqlite3` module is a wrapper for the SQLite database
library, providing a persistent database that can be updated and
accessed using slightly nonstandard SQL syntax.
* Internationalization is supported by a number of modules including
:mod:`gettext`, :mod:`locale`, and the :mod:`codecs` package.

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.. _tut-brieftourtwo:
**********************************************
Brief Tour of the Standard Library --- Part II
**********************************************
This second tour covers more advanced modules that support professional
programming needs. These modules rarely occur in small scripts.
.. _tut-output-formatting:
Output Formatting
=================
The :mod:`reprlib` module provides a version of :func:`repr` customized for
abbreviated displays of large or deeply nested containers::
>>> import reprlib
>>> reprlib.repr(set('supercalifragilisticexpialidocious'))
"{'a', 'c', 'd', 'e', 'f', 'g', ...}"
The :mod:`pprint` module offers more sophisticated control over printing both
built-in and user defined objects in a way that is readable by the interpreter.
When the result is longer than one line, the "pretty printer" adds line breaks
and indentation to more clearly reveal data structure::
>>> import pprint
>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
... 'yellow'], 'blue']]]
...
>>> pprint.pprint(t, width=30)
[[[['black', 'cyan'],
'white',
['green', 'red']],
[['magenta', 'yellow'],
'blue']]]
The :mod:`textwrap` module formats paragraphs of text to fit a given screen
width::
>>> import textwrap
>>> doc = """The wrap() method is just like fill() except that it returns
... a list of strings instead of one big string with newlines to separate
... the wrapped lines."""
...
>>> print(textwrap.fill(doc, width=40))
The wrap() method is just like fill()
except that it returns a list of strings
instead of one big string with newlines
to separate the wrapped lines.
The :mod:`locale` module accesses a database of culture specific data formats.
The grouping attribute of locale's format function provides a direct way of
formatting numbers with group separators::
>>> import locale
>>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')
'English_United States.1252'
>>> conv = locale.localeconv() # get a mapping of conventions
>>> x = 1234567.8
>>> locale.format("%d", x, grouping=True)
'1,234,567'
>>> locale.format_string("%s%.*f", (conv['currency_symbol'],
... conv['frac_digits'], x), grouping=True)
'$1,234,567.80'
.. _tut-templating:
Templating
==========
The :mod:`string` module includes a versatile :class:`~string.Template` class
with a simplified syntax suitable for editing by end-users. This allows users
to customize their applications without having to alter the application.
The format uses placeholder names formed by ``$`` with valid Python identifiers
(alphanumeric characters and underscores). Surrounding the placeholder with
braces allows it to be followed by more alphanumeric letters with no intervening
spaces. Writing ``$$`` creates a single escaped ``$``::
>>> from string import Template
>>> t = Template('${village}folk send $$10 to $cause.')
>>> t.substitute(village='Nottingham', cause='the ditch fund')
'Nottinghamfolk send $10 to the ditch fund.'
The :meth:`~string.Template.substitute` method raises a :exc:`KeyError` when a
placeholder is not supplied in a dictionary or a keyword argument. For
mail-merge style applications, user supplied data may be incomplete and the
:meth:`~string.Template.safe_substitute` method may be more appropriate ---
it will leave placeholders unchanged if data is missing::
>>> t = Template('Return the $item to $owner.')
>>> d = dict(item='unladen swallow')
>>> t.substitute(d)
Traceback (most recent call last):
...
KeyError: 'owner'
>>> t.safe_substitute(d)
'Return the unladen swallow to $owner.'
Template subclasses can specify a custom delimiter. For example, a batch
renaming utility for a photo browser may elect to use percent signs for
placeholders such as the current date, image sequence number, or file format::
>>> import time, os.path
>>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
>>> class BatchRename(Template):
... delimiter = '%'
>>> fmt = input('Enter rename style (%d-date %n-seqnum %f-format): ')
Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f
>>> t = BatchRename(fmt)
>>> date = time.strftime('%d%b%y')
>>> for i, filename in enumerate(photofiles):
... base, ext = os.path.splitext(filename)
... newname = t.substitute(d=date, n=i, f=ext)
... print('{0} --> {1}'.format(filename, newname))
img_1074.jpg --> Ashley_0.jpg
img_1076.jpg --> Ashley_1.jpg
img_1077.jpg --> Ashley_2.jpg
Another application for templating is separating program logic from the details
of multiple output formats. This makes it possible to substitute custom
templates for XML files, plain text reports, and HTML web reports.
.. _tut-binary-formats:
Working with Binary Data Record Layouts
=======================================
The :mod:`struct` module provides :func:`~struct.pack` and
:func:`~struct.unpack` functions for working with variable length binary
record formats. The following example shows
how to loop through header information in a ZIP file without using the
:mod:`zipfile` module. Pack codes ``"H"`` and ``"I"`` represent two and four
byte unsigned numbers respectively. The ``"<"`` indicates that they are
standard size and in little-endian byte order::
import struct
with open('myfile.zip', 'rb') as f:
data = f.read()
start = 0
for i in range(3): # show the first 3 file headers
start += 14
fields = struct.unpack('<IIIHH', data[start:start+16])
crc32, comp_size, uncomp_size, filenamesize, extra_size = fields
start += 16
filename = data[start:start+filenamesize]
start += filenamesize
extra = data[start:start+extra_size]
print(filename, hex(crc32), comp_size, uncomp_size)
start += extra_size + comp_size # skip to the next header
.. _tut-multi-threading:
Multi-threading
===============
Threading is a technique for decoupling tasks which are not sequentially
dependent. Threads can be used to improve the responsiveness of applications
that accept user input while other tasks run in the background. A related use
case is running I/O in parallel with computations in another thread.
The following code shows how the high level :mod:`threading` module can run
tasks in background while the main program continues to run::
import threading, zipfile
class AsyncZip(threading.Thread):
def __init__(self, infile, outfile):
threading.Thread.__init__(self)
self.infile = infile
self.outfile = outfile
def run(self):
f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
f.write(self.infile)
f.close()
print('Finished background zip of:', self.infile)
background = AsyncZip('mydata.txt', 'myarchive.zip')
background.start()
print('The main program continues to run in foreground.')
background.join() # Wait for the background task to finish
print('Main program waited until background was done.')
The principal challenge of multi-threaded applications is coordinating threads
that share data or other resources. To that end, the threading module provides
a number of synchronization primitives including locks, events, condition
variables, and semaphores.
While those tools are powerful, minor design errors can result in problems that
are difficult to reproduce. So, the preferred approach to task coordination is
to concentrate all access to a resource in a single thread and then use the
:mod:`queue` module to feed that thread with requests from other threads.
Applications using :class:`~queue.Queue` objects for inter-thread communication and
coordination are easier to design, more readable, and more reliable.
.. _tut-logging:
Logging
=======
The :mod:`logging` module offers a full featured and flexible logging system.
At its simplest, log messages are sent to a file or to ``sys.stderr``::
import logging
logging.debug('Debugging information')
logging.info('Informational message')
logging.warning('Warning:config file %s not found', 'server.conf')
logging.error('Error occurred')
logging.critical('Critical error -- shutting down')
This produces the following output:
.. code-block:: none
WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down
By default, informational and debugging messages are suppressed and the output
is sent to standard error. Other output options include routing messages
through email, datagrams, sockets, or to an HTTP Server. New filters can select
different routing based on message priority: :const:`~logging.DEBUG`,
:const:`~logging.INFO`, :const:`~logging.WARNING`, :const:`~logging.ERROR`,
and :const:`~logging.CRITICAL`.
The logging system can be configured directly from Python or can be loaded from
a user editable configuration file for customized logging without altering the
application.
.. _tut-weak-references:
Weak References
===============
Python does automatic memory management (reference counting for most objects and
:term:`garbage collection` to eliminate cycles). The memory is freed shortly
after the last reference to it has been eliminated.
This approach works fine for most applications but occasionally there is a need
to track objects only as long as they are being used by something else.
Unfortunately, just tracking them creates a reference that makes them permanent.
The :mod:`weakref` module provides tools for tracking objects without creating a
reference. When the object is no longer needed, it is automatically removed
from a weakref table and a callback is triggered for weakref objects. Typical
applications include caching objects that are expensive to create::
>>> import weakref, gc
>>> class A:
... def __init__(self, value):
... self.value = value
... def __repr__(self):
... return str(self.value)
...
>>> a = A(10) # create a reference
>>> d = weakref.WeakValueDictionary()
>>> d['primary'] = a # does not create a reference
>>> d['primary'] # fetch the object if it is still alive
10
>>> del a # remove the one reference
>>> gc.collect() # run garbage collection right away
0
>>> d['primary'] # entry was automatically removed
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
d['primary'] # entry was automatically removed
File "C:/python36/lib/weakref.py", line 46, in __getitem__
o = self.data[key]()
KeyError: 'primary'
.. _tut-list-tools:
Tools for Working with Lists
============================
Many data structure needs can be met with the built-in list type. However,
sometimes there is a need for alternative implementations with different
performance trade-offs.
The :mod:`array` module provides an :class:`~array.array()` object that is like
a list that stores only homogeneous data and stores it more compactly. The
following example shows an array of numbers stored as two byte unsigned binary
numbers (typecode ``"H"``) rather than the usual 16 bytes per entry for regular
lists of Python int objects::
>>> from array import array
>>> a = array('H', [4000, 10, 700, 22222])
>>> sum(a)
26932
>>> a[1:3]
array('H', [10, 700])
The :mod:`collections` module provides a :class:`~collections.deque()` object
that is like a list with faster appends and pops from the left side but slower
lookups in the middle. These objects are well suited for implementing queues
and breadth first tree searches::
>>> from collections import deque
>>> d = deque(["task1", "task2", "task3"])
>>> d.append("task4")
>>> print("Handling", d.popleft())
Handling task1
::
unsearched = deque([starting_node])
def breadth_first_search(unsearched):
node = unsearched.popleft()
for m in gen_moves(node):
if is_goal(m):
return m
unsearched.append(m)
In addition to alternative list implementations, the library also offers other
tools such as the :mod:`bisect` module with functions for manipulating sorted
lists::
>>> import bisect
>>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]
>>> bisect.insort(scores, (300, 'ruby'))
>>> scores
[(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]
The :mod:`heapq` module provides functions for implementing heaps based on
regular lists. The lowest valued entry is always kept at position zero. This
is useful for applications which repeatedly access the smallest element but do
not want to run a full list sort::
>>> from heapq import heapify, heappop, heappush
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> heapify(data) # rearrange the list into heap order
>>> heappush(data, -5) # add a new entry
>>> [heappop(data) for i in range(3)] # fetch the three smallest entries
[-5, 0, 1]
.. _tut-decimal-fp:
Decimal Floating Point Arithmetic
=================================
The :mod:`decimal` module offers a :class:`~decimal.Decimal` datatype for
decimal floating point arithmetic. Compared to the built-in :class:`float`
implementation of binary floating point, the class is especially helpful for
* financial applications and other uses which require exact decimal
representation,
* control over precision,
* control over rounding to meet legal or regulatory requirements,
* tracking of significant decimal places, or
* applications where the user expects the results to match calculations done by
hand.
For example, calculating a 5% tax on a 70 cent phone charge gives different
results in decimal floating point and binary floating point. The difference
becomes significant if the results are rounded to the nearest cent::
>>> from decimal import *
>>> round(Decimal('0.70') * Decimal('1.05'), 2)
Decimal('0.74')
>>> round(.70 * 1.05, 2)
0.73
The :class:`~decimal.Decimal` result keeps a trailing zero, automatically
inferring four place significance from multiplicands with two place
significance. Decimal reproduces mathematics as done by hand and avoids
issues that can arise when binary floating point cannot exactly represent
decimal quantities.
Exact representation enables the :class:`~decimal.Decimal` class to perform
modulo calculations and equality tests that are unsuitable for binary floating
point::
>>> Decimal('1.00') % Decimal('.10')
Decimal('0.00')
>>> 1.00 % 0.10
0.09999999999999995
>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
True
>>> sum([0.1]*10) == 1.0
False
The :mod:`decimal` module provides arithmetic with as much precision as needed::
>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)
Decimal('0.142857142857142857142857142857142857')

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.. _tut-venv:
*********************************
Virtual Environments and Packages
*********************************
Introduction
============
Python applications will often use packages and modules that don't
come as part of the standard library. Applications will sometimes
need a specific version of a library, because the application may
require that a particular bug has been fixed or the application may be
written using an obsolete version of the library's interface.
This means it may not be possible for one Python installation to meet
the requirements of every application. If application A needs version
1.0 of a particular module but application B needs version 2.0, then
the requirements are in conflict and installing either version 1.0 or 2.0
will leave one application unable to run.
The solution for this problem is to create a :term:`virtual environment`, a
self-contained directory tree that contains a Python installation for a
particular version of Python, plus a number of additional packages.
Different applications can then use different virtual environments.
To resolve the earlier example of conflicting requirements,
application A can have its own virtual environment with version 1.0
installed while application B has another virtual environment with version 2.0.
If application B requires a library be upgraded to version 3.0, this will
not affect application A's environment.
Creating Virtual Environments
=============================
The module used to create and manage virtual environments is called
:mod:`venv`. :mod:`venv` will usually install the most recent version of
Python that you have available. If you have multiple versions of Python on your
system, you can select a specific Python version by running ``python3`` or
whichever version you want.
To create a virtual environment, decide upon a directory where you want to
place it, and run the :mod:`venv` module as a script with the directory path::
python3 -m venv tutorial-env
This will create the ``tutorial-env`` directory if it doesn't exist,
and also create directories inside it containing a copy of the Python
interpreter, the standard library, and various supporting files.
Once you've created a virtual environment, you may activate it.
On Windows, run::
tutorial-env\Scripts\activate.bat
On Unix or MacOS, run::
source tutorial-env/bin/activate
(This script is written for the bash shell. If you use the
:program:`csh` or :program:`fish` shells, there are alternate
``activate.csh`` and ``activate.fish`` scripts you should use
instead.)
Activating the virtual environment will change your shell's prompt to show what
virtual environment you're using, and modify the environment so that running
``python`` will get you that particular version and installation of Python.
For example:
.. code-block:: bash
$ source ~/envs/tutorial-env/bin/activate
(tutorial-env) $ python
Python 3.5.1 (default, May 6 2016, 10:59:36)
...
>>> import sys
>>> sys.path
['', '/usr/local/lib/python35.zip', ...,
'~/envs/tutorial-env/lib/python3.5/site-packages']
>>>
Managing Packages with pip
==========================
You can install, upgrade, and remove packages using a program called
:program:`pip`. By default ``pip`` will install packages from the Python
Package Index, <https://pypi.org>. You can browse the Python
Package Index by going to it in your web browser, or you can use ``pip``'s
limited search feature:
.. code-block:: bash
(tutorial-env) $ pip search astronomy
skyfield - Elegant astronomy for Python
gary - Galactic astronomy and gravitational dynamics.
novas - The United States Naval Observatory NOVAS astronomy library
astroobs - Provides astronomy ephemeris to plan telescope observations
PyAstronomy - A collection of astronomy related tools for Python.
...
``pip`` has a number of subcommands: "search", "install", "uninstall",
"freeze", etc. (Consult the :ref:`installing-index` guide for
complete documentation for ``pip``.)
You can install the latest version of a package by specifying a package's name:
.. code-block:: bash
(tutorial-env) $ pip install novas
Collecting novas
Downloading novas-3.1.1.3.tar.gz (136kB)
Installing collected packages: novas
Running setup.py install for novas
Successfully installed novas-3.1.1.3
You can also install a specific version of a package by giving the
package name followed by ``==`` and the version number:
.. code-block:: bash
(tutorial-env) $ pip install requests==2.6.0
Collecting requests==2.6.0
Using cached requests-2.6.0-py2.py3-none-any.whl
Installing collected packages: requests
Successfully installed requests-2.6.0
If you re-run this command, ``pip`` will notice that the requested
version is already installed and do nothing. You can supply a
different version number to get that version, or you can run ``pip
install --upgrade`` to upgrade the package to the latest version:
.. code-block:: bash
(tutorial-env) $ pip install --upgrade requests
Collecting requests
Installing collected packages: requests
Found existing installation: requests 2.6.0
Uninstalling requests-2.6.0:
Successfully uninstalled requests-2.6.0
Successfully installed requests-2.7.0
``pip uninstall`` followed by one or more package names will remove the
packages from the virtual environment.
``pip show`` will display information about a particular package:
.. code-block:: bash
(tutorial-env) $ pip show requests
---
Metadata-Version: 2.0
Name: requests
Version: 2.7.0
Summary: Python HTTP for Humans.
Home-page: http://python-requests.org
Author: Kenneth Reitz
Author-email: me@kennethreitz.com
License: Apache 2.0
Location: /Users/akuchling/envs/tutorial-env/lib/python3.4/site-packages
Requires:
``pip list`` will display all of the packages installed in the virtual
environment:
.. code-block:: bash
(tutorial-env) $ pip list
novas (3.1.1.3)
numpy (1.9.2)
pip (7.0.3)
requests (2.7.0)
setuptools (16.0)
``pip freeze`` will produce a similar list of the installed packages,
but the output uses the format that ``pip install`` expects.
A common convention is to put this list in a ``requirements.txt`` file:
.. code-block:: bash
(tutorial-env) $ pip freeze > requirements.txt
(tutorial-env) $ cat requirements.txt
novas==3.1.1.3
numpy==1.9.2
requests==2.7.0
The ``requirements.txt`` can then be committed to version control and
shipped as part of an application. Users can then install all the
necessary packages with ``install -r``:
.. code-block:: bash
(tutorial-env) $ pip install -r requirements.txt
Collecting novas==3.1.1.3 (from -r requirements.txt (line 1))
...
Collecting numpy==1.9.2 (from -r requirements.txt (line 2))
...
Collecting requests==2.7.0 (from -r requirements.txt (line 3))
...
Installing collected packages: novas, numpy, requests
Running setup.py install for novas
Successfully installed novas-3.1.1.3 numpy-1.9.2 requests-2.7.0
``pip`` has many more options. Consult the :ref:`installing-index`
guide for complete documentation for ``pip``. When you've written
a package and want to make it available on the Python Package Index,
consult the :ref:`distributing-index` guide.

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.. _tut-whatnow:
*********
What Now?
*********
Reading this tutorial has probably reinforced your interest in using Python ---
you should be eager to apply Python to solving your real-world problems. Where
should you go to learn more?
This tutorial is part of Python's documentation set. Some other documents in
the set are:
* :ref:`library-index`:
You should browse through this manual, which gives complete (though terse)
reference material about types, functions, and the modules in the standard
library. The standard Python distribution includes a *lot* of additional code.
There are modules to read Unix mailboxes, retrieve documents via HTTP, generate
random numbers, parse command-line options, write CGI programs, compress data,
and many other tasks. Skimming through the Library Reference will give you an
idea of what's available.
* :ref:`installing-index` explains how to install additional modules written
by other Python users.
* :ref:`reference-index`: A detailed explanation of Python's syntax and
semantics. It's heavy reading, but is useful as a complete guide to the
language itself.
More Python resources:
* https://www.python.org: The major Python Web site. It contains code,
documentation, and pointers to Python-related pages around the Web. This Web
site is mirrored in various places around the world, such as Europe, Japan, and
Australia; a mirror may be faster than the main site, depending on your
geographical location.
* https://docs.python.org: Fast access to Python's documentation.
* https://pypi.org: The Python Package Index, previously also nicknamed
the Cheese Shop, is an index of user-created Python modules that are available
for download. Once you begin releasing code, you can register it here so that
others can find it.
* https://code.activestate.com/recipes/langs/python/: The Python Cookbook is a
sizable collection of code examples, larger modules, and useful scripts.
Particularly notable contributions are collected in a book also titled Python
Cookbook (O'Reilly & Associates, ISBN 0-596-00797-3.)
* http://www.pyvideo.org collects links to Python-related videos from
conferences and user-group meetings.
* https://scipy.org: The Scientific Python project includes modules for fast
array computations and manipulations plus a host of packages for such
things as linear algebra, Fourier transforms, non-linear solvers,
random number distributions, statistical analysis and the like.
For Python-related questions and problem reports, you can post to the newsgroup
:newsgroup:`comp.lang.python`, or send them to the mailing list at
python-list@python.org. The newsgroup and mailing list are gatewayed, so
messages posted to one will automatically be forwarded to the other. There are
hundreds of postings a day, asking (and
answering) questions, suggesting new features, and announcing new modules.
Mailing list archives are available at https://mail.python.org/pipermail/.
Before posting, be sure to check the list of
:ref:`Frequently Asked Questions <faq-index>` (also called the FAQ). The
FAQ answers many of the questions that come up again and again, and may
already contain the solution for your problem.