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8. Errors and Exceptions

8. 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.

8.1. 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 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.

8.2. 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 only concatenate str (not "int") to str

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 ZeroDivisionError, NameError and 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
occurred, 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.

Built-in Exceptions lists the built-in exceptions and their meanings.

8.3. 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 Control-C or
whatever the operating system supports); note that a user-generated interruption
is signalled by raising the 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 try statement works as follows.

  • First, the try clause (the statement(s) between the try and
    except keywords) is executed.

  • If no exception occurs, the except clause is skipped and execution of the
    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
    except keyword, the except clause is executed, and then execution
    continues after the try/except block.

  • If an exception occurs which does not match the exception named in the except
    , it is passed on to outer try statements; if no handler is
    found, it is an unhandled exception and execution stops with a message as
    shown above.

A 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 try statement. An except clause
may name multiple exceptions as a parenthesized tuple, for example:

... except (RuntimeError, TypeError, NameError):
... pass

A class in an 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):

class C(B):

class D(C):

for cls in [B, C, D]:
raise cls()
except D:
except C:
except 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.

When an exception occurs, it may have associated values, also known as the
exception’s arguments. The presence and types of the arguments depend on the
exception type.

The except clause may specify a variable after the exception name. The
variable is bound to the exception instance which typically has an args
attribute that stores the arguments. For convenience, builtin exception
types define __str__() to print all the arguments without explicitly
accessing .args.

>>> 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

The exception’s __str__() output is printed as the last part (‘detail’)
of the message for unhandled exceptions.

BaseException is the common base class of all exceptions. One of its
subclasses, Exception, is the base class of all the non-fatal exceptions.
Exceptions which are not subclasses of Exception are not typically
handled, because they are used to indicate that the program should terminate.
They include SystemExit which is raised by sys.exit() and
KeyboardInterrupt which is raised when a user wishes to interrupt
the program.

Exception can be used as a wildcard that catches (almost) everything.
However, it is good practice to be as specific as possible with the types
of exceptions that we intend to handle, and to allow any unexpected
exceptions to propagate on.

The most common pattern for handling Exception is to print or log
the exception and then re-raise it (allowing a caller to handle the
exception as well):

import sys

f = open('myfile.txt')
s = f.readline()
i = int(s.strip())
except OSError as err:
print("OS error:", err)
except ValueError:
print("Could not convert data to an integer.")
except Exception as err:
print(f"Unexpected {err=}, {type(err)=}")

The try … except statement has an optional else
, 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:]:
f = open(arg, 'r')
except OSError:
print('cannot open', arg)
print(arg, 'has', len(f.readlines()), 'lines')

The use of the else clause is better than adding additional code to
the try clause because it avoids accidentally catching an exception
that wasn’t raised by the code being protected by the try …
except statement.

Exception handlers do not handle only exceptions that occur immediately in the
try clause, but also those that 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

8.4. Raising Exceptions¶

The 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 raise indicates the exception to be raised.
This must be either an exception instance or an exception class (a class that
derives from BaseException, such as Exception or one of its
subclasses). 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 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

8.5. Exception Chaining¶

If an unhandled exception occurs inside an except section, it will
have the exception being handled attached to it and included in the error

>>> try:
... open("database.sqlite")
... except OSError:
... raise RuntimeError("unable to handle error")
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FileNotFoundError: [Errno 2] No such file or directory: 'database.sqlite'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: unable to handle error

To indicate that an exception is a direct consequence of another, the
raise statement allows an optional from clause:

# exc must be exception instance or None.
raise RuntimeError from exc

This can be useful when you are transforming exceptions. For example:

>>> def func():
... raise ConnectionError
>>> try:
... func()
... except ConnectionError as exc:
... raise RuntimeError('Failed to open database') from exc
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "<stdin>", line 2, in func

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "<stdin>", line 4, in <module>
RuntimeError: Failed to open database

It also allows disabling automatic exception chaining using the from None

>>> try:
... open('database.sqlite')
... except OSError:
... raise RuntimeError from None
Traceback (most recent call last):
File "<stdin>", line 4, in <module>

For more information about chaining mechanics, see Built-in Exceptions.

8.6. User-defined Exceptions¶

Programs may name their own exceptions by creating a new exception class (see
Classes for more about Python classes). Exceptions should typically
be derived from the 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.

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.

8.7. Defining Clean-up Actions¶

The try statement has another optional clause which is intended to
define clean-up actions that must be executed under all circumstances. For

>>> try:
... raise KeyboardInterrupt
... finally:
... print('Goodbye, world!')
Goodbye, world!
Traceback (most recent call last):
File "<stdin>", line 2, in <module>

If a finally clause is present, the finally
clause will execute as the last task before the try
statement completes. The finally clause runs whether or
not the try statement produces an exception. The following
points discuss more complex cases when an exception occurs:

  • If an exception occurs during execution of the try
    clause, the exception may be handled by an except
    clause. If the exception is not handled by an except
    clause, the exception is re-raised after the finally
    clause has been executed.

  • An exception could occur during execution of an except
    or else clause. Again, the exception is re-raised after
    the finally clause has been executed.

  • If the finally clause executes a break,
    continue or return statement, exceptions are not

  • If the try statement reaches a break,
    continue or return statement, the
    finally clause will execute just prior to the
    break, continue or return
    statement’s execution.

  • If a finally clause includes a return
    statement, the returned value will be the one from the
    finally clause’s return statement, not the
    value from the try clause’s return

For example:

>>> def bool_return():
... try:
... return True
... finally:
... return False
>>> bool_return()

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 finally clause is executed in any event. The
TypeError raised by dividing two strings is not handled by the
except clause and therefore re-raised after the finally
clause has been executed.

In real world applications, the finally clause is useful for
releasing external resources (such as files or network connections), regardless
of whether the use of the resource was successful.

8.8. 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 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.

8.9. Raising and Handling Multiple Unrelated Exceptions¶

There are situations where it is necessary to report several exceptions that
have occurred. This is often the case in concurrency frameworks, when several
tasks may have failed in parallel, but there are also other use cases where
it is desirable to continue execution and collect multiple errors rather than
raise the first exception.

The builtin ExceptionGroup wraps a list of exception instances so
that they can be raised together. It is an exception itself, so it can be
caught like any other exception.

>>> def f():
... excs = [OSError('error 1'), SystemError('error 2')]
... raise ExceptionGroup('there were problems', excs)
>>> f()
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 1, in <module>
| File "<stdin>", line 3, in f
| ExceptionGroup: there were problems
+-+---------------- 1 ----------------
| OSError: error 1
+---------------- 2 ----------------
| SystemError: error 2
>>> try:
... f()
... except Exception as e:
... print(f'caught {type(e)}: e')
caught <class 'ExceptionGroup'>: e

By using except* instead of except, we can selectively
handle only the exceptions in the group that match a certain
type. In the following example, which shows a nested exception
group, each except* clause extracts from the group exceptions
of a certain type while letting all other exceptions propagate to
other clauses and eventually to be reraised.

>>> def f():
... raise ExceptionGroup("group1",
... [OSError(1),
... SystemError(2),
... ExceptionGroup("group2",
... [OSError(3), RecursionError(4)])])
>>> try:
... f()
... except* OSError as e:
... print("There were OSErrors")
... except* SystemError as e:
... print("There were SystemErrors")
There were OSErrors
There were SystemErrors
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 2, in <module>
| File "<stdin>", line 2, in f
| ExceptionGroup: group1
+-+---------------- 1 ----------------
| ExceptionGroup: group2
+-+---------------- 1 ----------------
| RecursionError: 4

Note that the exceptions nested in an exception group must be instances,
not types. This is because in practice the exceptions would typically
be ones that have already been raised and caught by the program, along
the following pattern:

>>> excs = []
... for test in tests:
... try:
... except Exception as e:
... excs.append(e)
>>> if excs:
... raise ExceptionGroup("Test Failures", excs)

8.10. Enriching Exceptions with Notes¶

When an exception is created in order to be raised, it is usually initialized
with information that describes the error that has occurred. There are cases
where it is useful to add information after the exception was caught. For this
purpose, exceptions have a method add_note(note) that accepts a string and
adds it to the exception’s notes list. The standard traceback rendering
includes all notes, in the order they were added, after the exception.

>>> try:
... raise TypeError('bad type')
... except Exception as e:
... e.add_note('Add some information')
... e.add_note('Add some more information')
... raise
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
TypeError: bad type
Add some information
Add some more information

For example, when collecting exceptions into an exception group, we may want
to add context information for the individual errors. In the following each
exception in the group has a note indicating when this error has occurred.

>>> def f():
... raise OSError('operation failed')
>>> excs = []
>>> for i in range(3):
... try:
... f()
... except Exception as e:
... e.add_note(f'Happened in Iteration {i+1}')
... excs.append(e)
>>> raise ExceptionGroup('We have some problems', excs)
+ Exception Group Traceback (most recent call last):
| File "<stdin>", line 1, in <module>
| ExceptionGroup: We have some problems (3 sub-exceptions)
+-+---------------- 1 ----------------
| Traceback (most recent call last):
| File "<stdin>", line 3, in <module>
| File "<stdin>", line 2, in f
| OSError: operation failed
| Happened in Iteration 1
+---------------- 2 ----------------
| Traceback (most recent call last):
| File "<stdin>", line 3, in <module>
| File "<stdin>", line 2, in f
| OSError: operation failed
| Happened in Iteration 2
+---------------- 3 ----------------
| Traceback (most recent call last):
| File "<stdin>", line 3, in <module>
| File "<stdin>", line 2, in f
| OSError: operation failed
| Happened in Iteration 3

9. Classes

9. 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 Private Variables), 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.)

9.1. 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.

9.2. 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

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 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! 1

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
del statement. For example, del modname.the_answer will remove
the attribute 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 __main__, so they have their own
global namespace. (The built-in names actually also live in a module; this is
called 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 3 or 4 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, contain 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 next-to-last scope containing the module’s global names. To rebind variables
found outside of the innermost scope, the 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 global or nonlocal
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, import statements and
function definitions bind the module or function name in the local scope.

The global statement can be used to indicate that particular
variables live in the global scope and should be rebound there; the
nonlocal statement indicates that particular variables live in
an enclosing scope and should be rebound there.

9.2.1. Scopes and Namespaces Example¶

This is an example demonstrating how to reference the different scopes and
namespaces, and how global and nonlocal affect variable

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"
print("After local assignment:", spam)
print("After nonlocal assignment:", spam)
print("After global assignment:", spam)

print("In global scope:", spam)

The output of the example code is:

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 nonlocal assignment changed scope_test's
binding of spam, and the global assignment changed the module-level

You can also see that there was no previous binding for spam before the
global assignment.

9.3. A First Look at Classes¶

Classes introduce a little bit of new syntax, three new object types, and some
new semantics.

9.3.1. Class Definition Syntax¶

The simplest form of class definition looks like this:

class ClassName:

Class definitions, like function definitions (def statements) must be
executed before they have any effect. (You could conceivably place a class
definition in a branch of an 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 (ClassName in the

9.3.2. Class Objects¶

Class objects support two kinds of operations: attribute references and

Attribute references use the standard syntax used for all attribute references
in Python: 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.
__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
__init__(), like this:

def __init__(self): = []

When a class defines an __init__() method, class instantiation
automatically invokes __init__() for the newly created class instance. So
in this example, a new, initialized instance can be obtained by:

x = MyClass()

Of course, the __init__() method may have arguments for greater
flexibility. In that case, arguments given to the class instantiation operator
are passed on to __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)

9.3.3. 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 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
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.)

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.

9.3.4. Method Objects¶

Usually, a method is called right after it is bound:


In the 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:

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 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.

9.3.5. 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): = name # instance variable unique to each instance

>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind # shared by all dogs
>>> e.kind # shared by all dogs
>>> # unique to d
>>> # unique to e

As discussed in A Word About Names and Objects, shared data can have possibly surprising
effects with involving 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

class Dog:

tricks = [] # mistaken use of a class variable

def __init__(self, name): = name

def add_trick(self, 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): = name
self.tricks = [] # creates a new empty list for each dog

def add_trick(self, 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']

9.4. Random Remarks¶

If the same attribute name occurs in both an instance and in a class,
then attribute lookup prioritizes the instance:

>>> class Warehouse:
... purpose = 'storage'
... region = 'west'
>>> w1 = Warehouse()
>>> print(w1.purpose, w1.region)
storage west
>>> w2 = Warehouse()
>>> w2.region = 'east'
>>> print(w2.purpose, w2.region)
storage east

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 C that refer to
function objects, and consequently they are all methods of instances of
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

class Bag:
def __init__(self): = []

def add(self, x):

def addtwice(self, 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__.

9.5. 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

class DerivedClassName(BaseClassName):

The name 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,
. This is occasionally useful to clients as well. (Note that this
only works if the base class is accessible as BaseClassName in the global

Python has two built-in functions that work with inheritance:

  • Use isinstance() to check an instance’s type: isinstance(obj, int)
    will be True only if obj.__class__ is int or some class
    derived from int.

  • Use issubclass() to check class inheritance: issubclass(bool, int)
    is True since bool is a subclass of int. However,
    issubclass(float, int) is False since float is not a
    subclass of int.

9.5.1. 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):

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 DerivedClassName, it is searched
for in Base1, then (recursively) in the base classes of Base1,
and if it was not found there, it was searched for in Base2, and so on.

In fact, it is slightly more complex than that; the method resolution order
changes dynamically to support cooperative calls to 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 object, so any case of multiple inheritance
provides more than one path to reach 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

9.6. 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.

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 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 = []

def update(self, iterable):
for item in iterable:

__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):

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.

9.7. 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. The idiomatic approach
is to use dataclasses for this purpose:

from dataclasses import dataclass

class Employee:
name: str
dept: str
salary: int

>>> john = Employee('john', 'computer lab', 1000)
>>> john.dept
'computer lab'
>>> john.salary

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 read() and readline() that get the
data from a string buffer instead, and pass it as an argument.

Instance method objects have attributes, too: m.__self__ is the instance
object with the method m(), and m.__func__ is the function object
corresponding to the method.

9.8. Iterators¶

By now you have probably noticed that most container objects can be looped over
using a for statement:

for element in [1, 2, 3]:
for element in (1, 2, 3):
for key in {'one':1, 'two':2}:
for char in "123":
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 for statement
calls iter() on the container object. The function returns an iterator
object that defines the method __next__() which accesses
elements in the container one at a time. When there are no more elements,
__next__() raises a StopIteration exception which tells the
for loop to terminate. You can call the __next__() method
using the next() built-in function; this example shows how it all works:

>>> s = 'abc'
>>> it = iter(s)
>>> it
<str_iterator object at 0x10c90e650>
>>> next(it)
>>> next(it)
>>> next(it)
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>

Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an __iter__() method which
returns an object with a __next__() method. If the class
defines __next__(), then __iter__() can just return self:

class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(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

>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
... print(char)

9.9. Generators¶

Generators are a simple and powerful tool for creating iterators. They
are written like regular functions but use the yield statement
whenever they want to return data. Each time 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)

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 __iter__() and __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

In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise StopIteration. In
combination, these features make it easy to create iterators with no more effort
than writing a regular function.

9.10. 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.


>>> sum(i*i for i in range(10))                 # sum of squares

>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product

>>> unique_words = set(word for line in page for word in line.split())

>>> valedictorian = max((student.gpa, for student in graduates)

>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']



Except for one thing. Module objects have a secret read-only attribute called
__dict__ which returns the dictionary used to implement the module’s
namespace; the name __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.

Read article
10. Brief Tour of the Standard Library

10. Brief Tour of the Standard Library¶

10.1. Operating System Interface¶

The os module provides dozens of functions for interacting with the
operating system:

>>> import os
>>> os.getcwd() # Return the current working directory
>>> os.chdir('/server/accesslogs') # Change current working directory
>>> os.system('mkdir today') # Run the command mkdir in the system shell

Be sure to use the import os style instead of from os import *. This
will keep from shadowing the built-in open() function which
operates much differently.

The built-in dir() and help() functions are useful as interactive
aids for working with large modules like 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 shutil module provides
a higher level interface that is easier to use:

>>> import shutil
>>> shutil.copyfile('data.db', 'archive.db')
>>> shutil.move('/build/executables', 'installdir')

10.2. File Wildcards¶

The glob module provides a function for making file lists from directory
wildcard searches:

>>> import glob
>>> glob.glob('*.py')
['', '', '']

10.3. Command Line Arguments¶

Common utility scripts often need to process command line arguments. These
arguments are stored in the sys module’s argv attribute as a list. For
instance the following output results from running python one two
at the command line:

>>> import sys
>>> print(sys.argv)
['', 'one', 'two', 'three']

The argparse module provides a more sophisticated mechanism to process
command line arguments. The following script extracts one or more filenames
and an optional number of lines to be displayed:

import argparse

parser = argparse.ArgumentParser(
description='Show top lines from each file')
parser.add_argument('filenames', nargs='+')
parser.add_argument('-l', '--lines', type=int, default=10)
args = parser.parse_args()

When run at the command line with python --lines=5 alpha.txt
, the script sets args.lines to 5 and args.filenames
to ['alpha.txt', 'beta.txt'].

10.4. Error Output Redirection and Program Termination¶

The 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().

10.5. String Pattern Matching¶

The 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'

10.6. Mathematics¶

The math module gives access to the underlying C library functions for
floating point math:

>>> import math
>>> math.cos(math.pi / 4)
>>> math.log(1024, 2)

The random module provides tools for making random selections:

>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
>>> random.sample(range(100), 10) # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random() # random float
>>> random.randrange(6) # random integer chosen from range(6)

The 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)
>>> statistics.median(data)
>>> statistics.variance(data)

The SciPy project <> has many other modules for numerical

10.7. Internet Access¶

There are a number of modules for accessing the internet and processing internet
protocols. Two of the simplest are urllib.request for retrieving data
from URLs and smtplib for sending mail:

>>> from urllib.request import urlopen
>>> with urlopen('') as response:
... for line in response:
... line = line.decode() # Convert bytes to a str
... if line.startswith('datetime'):
... print(line.rstrip()) # Remove trailing newline
datetime: 2022-01-01T01:36:47.689215+00:00

>>> import smtplib
>>> server = smtplib.SMTP('localhost')
>>> server.sendmail('', '',
... """To:
... From:
... Beware the Ides of March.
... """)
>>> server.quit()

(Note that the second example needs a mailserver running on localhost.)

10.8. Dates and Times¶

The 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

>>> # dates are easily constructed and formatted
>>> from datetime import date
>>> now =
>>> now, 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

10.9. Data Compression¶

Common data archiving and compression formats are directly supported by modules
including: zlib, gzip, bz2, lzma, zipfile and

>>> import zlib
>>> s = b'witch which has which witches wrist watch'
>>> len(s)
>>> t = zlib.compress(s)
>>> len(t)
>>> zlib.decompress(t)
b'witch which has which witches wrist watch'
>>> zlib.crc32(s)

10.10. 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 timeit
module quickly demonstrates a modest performance advantage:

>>> from timeit import Timer
>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()

In contrast to timeit’s fine level of granularity, the profile and
pstats modules provide tools for identifying time critical sections in
larger blocks of code.

10.11. 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 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

def average(values):
"""Computes the arithmetic mean of a list of numbers.

>>> print(average([20, 30, 70]))
return sum(values) / len(values)

import doctest
doctest.testmod() # automatically validate the embedded tests

The unittest module is not as effortless as the doctest module,
but it allows a more comprehensive set of tests to be maintained in a separate

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):
with self.assertRaises(TypeError):
average(20, 30, 70)

unittest.main() # Calling from the command line invokes all tests

10.12. 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 xmlrpc.client and 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 email package is a library for managing email messages, including
    MIME and other RFC 2822-based message documents. Unlike smtplib and
    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

  • The json package provides robust support for parsing this
    popular data interchange format. The 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 xml.etree.ElementTree, xml.dom and
    xml.sax packages. Together, these modules and packages
    greatly simplify data interchange between Python applications and
    other tools.

  • The 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
    gettext, locale, and the codecs package.

Read article
11. Brief Tour of the Standard Library — Part II

11. 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.

11.1. Output Formatting¶

The reprlib module provides a version of repr() customized for
abbreviated displays of large or deeply nested containers:

>>> import reprlib
>>> reprlib.repr(set('supercalifragilisticexpialidocious'))
"{'a', 'c', 'd', 'e', 'f', 'g', ...}"

The 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'],
['green', 'red']],
[['magenta', 'yellow'],

The textwrap module formats paragraphs of text to fit a given screen

>>> 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 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)
>>> locale.format_string("%s%.*f", (conv['currency_symbol'],
... conv['frac_digits'], x), grouping=True)

11.2. Templating¶

The string module includes a versatile 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 substitute() method raises a 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
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.

11.3. Working with Binary Data Record Layouts¶

The struct module provides pack() and
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
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('', 'rb') as f:
data =

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

11.4. 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 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):
self.infile = infile
self.outfile = outfile

def run(self):
f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
print('Finished background zip of:', self.infile)

background = AsyncZip('mydata.txt', '')
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
queue module to feed that thread with requests from other threads.
Applications using Queue objects for inter-thread communication and
coordination are easier to design, more readable, and more reliable.

11.5. Logging¶

The 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')'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:

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: DEBUG,

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

11.6. Weak References¶

Python does automatic memory management (reference counting for most objects and
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 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
>>> del a # remove the one reference
>>> gc.collect() # run garbage collection right away
>>> 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:/python311/lib/", line 46, in __getitem__
o =[key]()
KeyError: 'primary'

11.7. 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 array module provides an 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)
>>> a[1:3]
array('H', [10, 700])

The collections module provides a 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

In addition to alternative list implementations, the library also offers other
tools such as the bisect module with functions for manipulating sorted

>>> 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 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]

11.8. Decimal Floating Point Arithmetic¶

The decimal module offers a Decimal datatype for
decimal floating point arithmetic. Compared to the built-in float
implementation of binary floating point, the class is especially helpful for

  • financial applications and other uses which require exact decimal

  • 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

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)
>>> round(.70 * 1.05, 2)

The 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 Decimal class to perform
modulo calculations and equality tests that are unsuitable for binary floating

>>> Decimal('1.00') % Decimal('.10')
>>> 1.00 % 0.10

>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
>>> sum([0.1]*10) == 1.0

The decimal module provides arithmetic with as much precision as needed:

>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)

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12. Virtual Environments and Packages

12. Virtual Environments and Packages¶

12.1. 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 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.

12.2. Creating Virtual Environments¶

The module used to create and manage virtual environments is called
venv. 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 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 and various supporting files.

A common directory location for a virtual environment is .venv.
This name keeps the directory typically hidden in your shell and thus
out of the way while giving it a name that explains why the directory
exists. It also prevents clashing with .env environment variable
definition files that some tooling supports.

Once you’ve created a virtual environment, you may activate it.

On Windows, run:


On Unix or MacOS, run:

source tutorial-env/bin/activate

(This script is written for the bash shell. If you use the
csh or fish shells, there are alternate
activate.csh and scripts you should use

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:

$ 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/', ...,

To deactivate a virtual environment, type:


into the terminal.

12.3. Managing Packages with pip¶

You can install, upgrade, and remove packages using a program called
pip. By default pip will install packages from the Python
Package Index, <>. You can browse the Python
Package Index by going to it in your web browser.

pip has a number of subcommands: “install”, “uninstall”,
“freeze”, etc. (Consult the Installing Python Modules guide for
complete documentation for pip.)

You can install the latest version of a package by specifying a package’s name:

(tutorial-env) $ python -m pip install novas
Collecting novas
Downloading novas- (136kB)
Installing collected packages: novas
Running install for novas
Successfully installed novas-

You can also install a specific version of a package by giving the
package name followed by == and the version number:

(tutorial-env) $ python -m 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 python
-m pip install --upgrade
to upgrade the package to the latest version:

(tutorial-env) $ python -m 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

python -m pip uninstall followed by one or more package names will
remove the packages from the virtual environment.

python -m pip show will display information about a particular package:

(tutorial-env) $ python -m pip show requests
Metadata-Version: 2.0
Name: requests
Version: 2.7.0
Summary: Python HTTP for Humans.
Author: Kenneth Reitz
License: Apache 2.0
Location: /Users/akuchling/envs/tutorial-env/lib/python3.4/site-packages

python -m pip list will display all of the packages installed in
the virtual environment:

(tutorial-env) $ python -m pip list
novas (
numpy (1.9.2)
pip (7.0.3)
requests (2.7.0)
setuptools (16.0)

python -m pip freeze will produce a similar list of the installed packages,
but the output uses the format that python -m pip install expects.
A common convention is to put this list in a requirements.txt file:

(tutorial-env) $ python -m pip freeze > requirements.txt
(tutorial-env) $ cat requirements.txt

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:

(tutorial-env) $ python -m pip install -r requirements.txt
Collecting novas== (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 install for novas
Successfully installed novas- numpy-1.9.2 requests-2.7.0

pip has many more options. Consult the Installing Python Modules
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 Distributing Python Modules guide.

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13. What Now?

13. 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:

  • The Python Standard Library:

    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, compress data,
    and many other tasks. Skimming through the Library Reference will give you an
    idea of what’s available.

  • Installing Python Modules explains how to install additional modules written
    by other Python users.

  • The Python Language Reference: 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:

  • The major Python web site. It contains code,
    documentation, and pointers to Python-related pages around the web.

  • Fast access to Python’s documentation.

  • The Python Package Index, previously also nicknamed
    the Cheese Shop 1, 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.

  • 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.)

  • collects links to Python-related videos from
    conferences and user-group meetings.

  • 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
comp.lang.python, or send them to the mailing list at 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

Before posting, be sure to check the list of
Frequently Asked Questions (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.



“Cheese Shop” is a Monty Python’s sketch: a customer enters a cheese shop,
but whatever cheese he asks for, the clerk says it’s missing.

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