Now that we’ve had a quick introduction to Python statement syntax, this chapter begins our in-depth tour of specific Python statements. We’ll begin with the basics—assignments, expression statements, and printing. We’ve already seen all these in action, but here, we’ll fill in important details we’ve skipped so far. Although they’re fairly simple, as you’ll see, there are optional variations for each of these statement types that will come in handy once you begin writing real Python programs.
We’ve been using the Python assignment statement for a while to assign objects to names. In its basic form, you write the target of an assignment on the left of an equals sign, and the object to be assigned on the right. The target on the left may be a name or object component, and the object on the right can be an arbitrary expression that computes an object. For the most part, assignments are straightforward, but here are a few properties to keep in mind:
Assignments create object references. As discussed in Chapter 6, Python assignments store references to objects in names or data structure components. They always create references to objects instead of copying the objects. Because of that, Python variables are more like pointers than data storage areas.
Names are created when first assigned. Python creates variable names the first time you assign them a value (i.e., an object reference), so there’s no need to predeclare names ahead of time. Some (but not all) data structure slots are created when assigned, too (e.g., dictionary entries, some object attributes). Once assigned, a name is replaced with the value it references whenever it appears in an expression.
Names must be assigned before being referenced. It’s an error to use a name to which you haven’t yet assigned a value. Python raises an exception if you try, rather than returning some sort of ambiguous default value; if it returned a default instead, it would be more difficult for you to spot typos in your code.
Implicit assignments:import
, from
, def
, class
, for
, function
arguments. In this section, we’re concerned with the
=
statement, but assignment occurs
in many contexts in Python. For instance, we’ll see later that module
imports, function and class definitions, for
loop variables, and function arguments
are all implicit assignments. Because assignment works the same
everywhere it pops up, all these contexts simply bind names to object
references at runtime.
Although assignment is a general and pervasive concept in Python, we are primarily interested in assignment statements in this chapter. Table 11-1 illustrates the different assignment statement forms in Python.
Operation |
Interpretation |
|
Basic form |
|
Tuple assignment (positional) |
|
List assignment (positional) |
|
Sequence assignment, generalized |
|
Multiple-target assignment |
|
Augmented assignment
(equivalent to |
The first form in Table 11-1 is by far the most common: binding a name (or data structure component) to a single object. The other table entries represent special and optional forms that programmers often find convenient:
The second and third forms in the table are related. When
you code tuples or lists on the left side of the =
, Python pairs objects on the right
side with targets on the left by position, and assigns them from
left to right. For example, in the second line of the table, the
name spam
is assigned the
string 'yum'
, and the name
ham
is bound to the string
'YUM'
. Internally, Python makes
a tuple of the items on the right first, so this is often called a
tuple-unpacking assignment.
In recent versions of Python, tuple and list assignments
have been generalized into instances of what we now call sequence
assignment—any sequence of names can be assigned to any sequence
of values, and Python assigns the items one at a time by position.
In fact, we can mix and match the types of the sequences involved.
The fourth line in Table 11-1, for example, pairs a
tuple of names with a string of characters: a
is assigned 's'
, b
is assigned 'p'
, and so on.
The fifth line in Table 11-1 shows the
multiple-target form of assignment. In this form, Python assigns a
reference to the same object (the object farthest to the right) to
all the targets on the left. In the table, the names spam
and ham
are both assigned references to the
same string object, 'lunch'
.
The effect is the same as if we coded ham
= 'lunch'
followed by spam =
ham
, as ham
evaluates
to the original string object (i.e., not a separate copy of that
object).
The last line in Table 11-1 is an example of
augmented assignment—a shorthand that combines an expression and
an assignment in a concise way. Saying spam += 42
, for example, has the same
effect as spam = spam + 42
, but
the augmented form requires less typing, and is generally quicker
to run. There’s one augmented assignment statement for every
binary expression operator in Python.
We’ve already used basic assignments in this book. Here are a few simple examples of sequence-unpacking assignments in action:
%python
>>>nudge = 1
>>>wink = 2
>>>A, B = nudge, wink
# Tuple assignment >>>A, B
# Like A = nudge; B = wink (1, 2) >>>[C, D] = [nudge, wink]
# List assignment >>>C, D
(1, 2)
Notice that we really are coding two tuples in the third line in this interaction—we’ve just omitted their enclosing parentheses. Python pairs the values in the tuple on the right side of the assignment operator with the variables in the tuple on the left side, and assigns the values one at a time.
Tuple assignment leads to a common coding trick in Python that was introduced in a solution to the exercises from Part II. Because Python creates a temporary tuple that saves the original values of the variables on the right while the statement runs, unpacking assignments are also a way to swap two variables’ values without creating a temporary variable of your own—the tuple on the right remembers the prior values of the variables automatically:
>>>nudge = 1
>>>wink = 2
>>>nudge, wink = wink, nudge
# Tuples: swaps values >>>nudge, wink
# Like T = nudge; nudge = wink; wink = T (2, 1)
In fact, the original tuple and list assignment forms in Python were eventually generalized to accept any type of sequence on the right as long as it is of the same length. You can assign a tuple of values to a list of variables, a string of characters to a tuple of variables, and so on. In all cases, Python assigns items in the sequence on the right to variables in the sequence on the left by position, from left to right:
>>>[a, b, c] = (1, 2, 3)
# Assign tuple of values to list of names >>>a, c
(1, 3) >>>(a, b, c) = "ABC"
# Assign string of characters to tuple >>>a, c
('A', 'C')
Technically speaking, sequence assignment actually supports any iterable object on the right, not just any sequence. This is a more general concept that we will explore in Chapter 13 and Chapter 17.
One note here—although we can mix and match sequence types
around the =
symbol, we still must
have the same number of items on the right as we
have variables on the left, or we’ll get an error:
>>>string = 'SPAM'
>>>a, b, c, d = string
# Same number on both sides >>>a, d
('S', 'M') >>>a, b, c = string
# Error if not...error text omitted...
ValueError: too many values to unpack
To be more general, we need to slice. There are a variety of ways to employ slicing to make this last case work:
>>>a, b, c = string[0], string[1], string[2:]
# Index and slice >>>a, b, c
('S', 'P', 'AM') >>>a, b, c = list(string[:2]) + [string[2:]]
# Slice and concatenate >>>a, b, c
('S', 'P', 'AM') >>>a, b = string[:2]
# Same, but simpler >>>c = string[2:]
>>>a, b, c
('S', 'P', 'AM') >>>(a, b), c = string[:2], string[2:]
# Nested sequences >>>a, b, c
('S', 'P', 'AM')
As the last example in this interaction demonstrates, we can even assign nested sequences, and Python unpacks their parts according to their shape, as expected. In this case, we are assigning a tuple of two items, where the first item is a nested sequence (a string) exactly as though we had coded it this way:
>>>((a, b), c) = ('SP', 'AM')
# Paired up by shape and position >>>a, b, c
('S', 'P', 'AM')
Python pairs the first string on the right ('SP'
) with the first tuple on the left
((a, b)
), and assigns one character
at a time, before assigning the entire second string ('AM'
) to the variable c
all at once. In this event, the
sequence-nesting shape of the object on the left must match that of
the object on the right. Nested sequence assignment like this is
somewhat advanced, and rare to see, but it can be convenient for
picking out the parts of data structures with known shapes. For
example, this technique also works in function argument lists because
function arguments are passed by assignment (as we’ll see in the next
part of the book).
Sequence-unpacking assignments also give rise to another common coding idiom in Python—assigning an integer series to a set of variables:
>>>red, green, blue = range(3)
>>>red, blue
(0, 2)
This initializes the three names to the integer codes 0, 1, and
2, respectively (it’s Python’s equivalent of the
enumerated data types you may have seen in other
languages). To make sense of this, you need to know that the range
built-in function generates a list of
successive integers:
>>> range(3)
# Try list(range(3)) in Python 3.0
[0, 1, 2]
Because range
is commonly
used in for
loops, we’ll say more
about it in Chapter 13. Another place you
may see a tuple assignment at work is for splitting a sequence into
its front and the rest in loops like this:
>>>L = [1, 2, 3, 4]
>>>while L:
...front, L = L[0], L[1:]
...print front, L
... 1 [2, 3, 4] 2 [3, 4] 3 [4] 4 []
The tuple assignment in the loop here could be coded as the following two lines instead, but it’s often more convenient to string them together:
...front = L[0]
...L = L[1:]
Notice that this code is using the list as a sort of stack data
structure—something we can often also achieve with the append
and pop
methods of list objects; here, front = L.pop(0)
would have much the same
effect as the tuple assignment statement, but it would be an in-place
change. We’ll learn more about while
loops, and other (and often better)
ways to step through a sequence with for
loops, in Chapter 13.
A multiple-target assignment simply assigns all the given names to
the object all the way to the right. The following, for example, assigns
the three variables a
, b
, and c
to
the string 'spam'
:
>>>a = b = c = 'spam'
>>>a, b, c
('spam', 'spam', 'spam')
This form is equivalent to (but easier to code) than these three assignments:
>>>c = 'spam'
>>>b = c
>>>a = b
Keep in mind that there is just one object here, shared by all three variables (they all wind up pointing to the same object in memory). This behavior is fine for immutable types—initializing a set of counters to zero, for example (recall that variables must be assigned before they can be used in Python, so you must initialize counters to zero before you can start adding to them):
>>>a = b = 0
>>>b = b + 1
>>>a, b
(0, 1)
Here, changing b
only changes
b
because numbers do not support
in-place changes. As long as the object assigned is immutable, it’s
irrelevant if more than one name references it.
As usual, though, we have to be more cautious when initializing variables to an empty mutable object such as a list or dictionary:
>>>a = b = []
>>>b.append(42)
>>>a, b
([42], [42])
This time, because a
and
b
reference the same object,
appending to it in-place through b
will impact what we see through a
as well. This is really just another example of the shared reference
phenomenon we first met in Chapter 6. To avoid the issue,
initialize mutable objects in separate statements instead, so that
each creates a distinct empty object by running a distinct literal
expression:
>>>a = []
>>>b = []
>>>b.append(42)
>>>a, b
([], [42])
Beginning with Python 2.0, the set of additional assignment statement formats listed in Table 11-2 became available. Known as augmented assignments, and borrowed from the C language, these formats are mostly just shorthand. They imply the combination of a binary expression, and an assignment. For instance, the following two formats are now roughly equivalent:
X = X + Y # Traditional form X += Y # Newer augmented form
|
|
|
|
|
|
|
|
|
|
|
|
Augmented assignment works on any type that supports the implied binary expression. For example, here are two ways to add 1 to a name:
>>>x = 1
>>>x = x + 1
# Traditional >>>x
2 >>>x += 1
# Augmented >>>x
3
When applied to a string, the augmented form performs
concatenation instead. Thus, the second line here is equivalent to
typing the longer S = S +
"SPAM"
:
>>>S = "spam"
>>>S += "SPAM"
# Implied concatenation >>>S
'spamSPAM'
As shown in Table 11-2,
there are analogous augmented assignment forms for every Python binary
expression operator (i.e., each operator with values on the left and
right side). For instance, X *= Y
multiplies and assigns, X >>= Y
shifts right and assigns, and so on. X //=
Y
(for floor division) was added in version 2.2.
Augmented assignments have three advantages:[26]
There’s less for you to type. Need I say more?
The left side only has to be evaluated once. In X += Y
, X
may be a complicated object expression.
In the augmented form, it need only be evaluated once. However, in
the long form, X = X + Y
,
X
appears twice, and must be run
twice. Because of this, augmented assignments usually run
faster.
The optimal technique is automatically chosen. For objects that support in-place changes, the augmented forms automatically perform in-place change operations instead of slower copies.
The last point here requires a bit more explanation. For augmented
assignments, in-place operations may be applied for mutable objects as
an optimization. Recall that lists can be extended in a variety of ways.
To add a single item to the end of a list, we can concatenate or call
append
:
>>>L = [1, 2]
>>>L = L + [3]
# Concatenate: slower >>>L
[1, 2, 3] >>>L.append(4)
# Faster, but in-place >>>L
[1, 2, 3, 4]
And to add a set of items to the end, we can either concatenate
again, or call the list extend
method:[27]
>>>L = L + [5, 6]
# Concatenate: slower >>>L
[1, 2, 3, 4, 5, 6] >>>L.extend([7, 8])
# Faster, but in-place >>>L
[1, 2, 3, 4, 5, 6, 7, 8]
In both cases, concatenation is less prone to the side effects of shared object references, but will generally run slower than the in-place equivalent. Concatenation operations must create a new object, copy in the list on the left, and then copy in the list on the right. By contrast, in-place method calls simply add items at the end of a memory block.
When we use augmented assignment to extend a list, we can forget
these details—for example, Python automatically calls the quicker
extend
method instead of using the
slower concatenation operation implied by +
:
>>>L += [9, 10]
# Mapped to L.extend([9, 10]) >>>L
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
This behavior is usually what we want, but notice that it
implies the +=
is an in-place
change for lists; thus, it is not exactly like +
concatenation, which always makes a new
object. As for all shared reference cases, the difference might matter
if other names reference the object being changed:
>>>L = [1, 2]
>>>M = L
# L and M reference the same object >>>L = L + [3, 4]
# Concatenation makes a new object >>>L, M
# Changes L but not M ([1, 2, 3, 4], [1, 2]) >>>L = [1, 2]
>>>M = L
>>>L += [3, 4]
# But += really means extend >>>L, M
# M sees the in-place change too! ([1, 2, 3, 4], [1, 2, 3, 4])
This only matters for mutables like lists and dictionaries, and it is a fairly obscure case (at least, until it impacts your code!). As always, make copies of your mutable objects if you need to break the shared reference structure.
Now that we’ve explored assignment statements, it’s time to get more formal about the use of variable names. In Python, names come into existence when you assign values to them, but there are a few rules to follow when picking names for things in your programs:
Variable names must start with an underscore or letter,
which can be followed by any number of letters, digits, or
underscores. _spam
, spam
, and Spam_1
are legal names, but 1_Spam
, spam$
, and @#!
are not.
SPAM
is not the same
as
spam
Python always pays attention to case in programs, both in
names you create, and in reserved words. For instance, the names
X
and x
refer to two different variables. For
portability, case also matters in the names of imported module
files, even on platforms where the filesystems are case
insensitive.
Names you define cannot be the same as words that mean
special things in the Python language. For instance, if you try to
use a variable name like class
,
Python will raise a syntax error, but klass
and Class
work fine. Table 11-3 lists the words that are
currently reserved (and hence off-limits) in Python.
yield
was an optional
extension in Python 2.2, but is a standard keyword as of 2.3. It is
used in conjunction with generator functions, a newer feature
discussed in Chapter 17. This was one
of a small handful of instances where Python broke with backward
compatibility. Still, yield
was
phased in over time—it began generating warnings in 2.2, and was not
enabled until 2.3.
Similarly, in Python 2.6, the words with
and as
are scheduled to become new reserved
words for use in context managers (a new form of exception handling).
These two words are not reserved in 2.5, unless the context manager
feature is turned on manually (see Chapter 27
for details). When used in 2.5, with
and as
generate warnings about the upcoming
change—except in the version of IDLE in Python 2.5, which appears to
have enabled this feature for you (that is, using these words as
variable names does generate errors in 2.5, but only in its version of
the IDLE GUI).
Python’s reserved words are always all lowercase, and they are
truly reserved; unlike names in the built-in scope that you will meet in
the next part of this book, you cannot redefine reserved words by
assignment (e.g., and = 1
results in
a syntax error).[28]
Furthermore, because module names in import
statements become variables in your
script, this constraint extends to your module filenames—you can code
files called and.py and my-code.py, but you cannot import them
because their names without the “.py” extension become variables in your
code, and so must follow all the variable rules just outlined (reserved
words are off-limits, and dashes won’t work, though underscores will).
We’ll revisit this idea in Part V.
Besides these rules, there is also a set of naming
conventions—rules that are not required, but are
followed in normal practice. For instance, because names with two
leading and trailing underscores (e.g., _
_name_ _
) generally have special meaning to the Python
interpreter, you should avoid this pattern for your own names. Here is
a list of the conventions Python follows:
Names that begin with a single underscore (_X
) are not imported by a from module import *
statement
(described in Chapter 19).
Names that have two leading and trailing underscores
(_ _X_ _
) are system-defined
names that have special meaning to the interpreter.
Names that begin with two underscores, and do not end with
two more (_ _X
) are localized
(“mangled”) to enclosing classes (described in Chapter 26).
The name that is just a single underscore (_
) retains the result of the last
expression when working interactively.
In addition to these Python interpreter conventions, there are
various other conventions that Python programmers usually follow as
well. For instance, later in the book, we’ll see that class names
commonly start with an uppercase letter, and module names with a
lowercase letter, and that the name self
, though not reserved, usually has a
special role in classes. In Part IV, we’ll also
study another, larger category of names known as the
built-ins, which are predefined, but not reserved
(and so can be reassigned: open =
42
works, though sometimes you might wish it
didn’t!).
This is mostly review, but remember that it’s crucial to keep Python’s distinction between names and objects clear. As described in "Chapter 6" in Chapter 6, objects have a type (e.g., integer, list), and may be mutable or not. Names (a.k.a. variables), on the other hand, are always just references to objects; they have no notion of mutability, and have no associated type information, apart from the type of the object they happen to reference at a given point in time.
It’s OK to assign the same name to different kinds of objects at different times:
>>>x = 0
# x bound to an integer object >>>x = "Hello"
# Now it's a string >>>x = [1, 2, 3]
# And now it's a list
In later examples, you’ll see that this generic nature of names can be a decided advantage in Python programming.[29] In Part IV, you’ll learn that names also live in something called a scope, which defines where they can be used; the place where you assign a name determines where it is visible.
In Python, you can use an expression as a statement, too (that is, on a line by itself). But because the result of the expression won’t be saved, it makes sense to do so only if the expression does something useful as a side effect. Expressions are commonly used as statements in two situations:
Some functions and methods do lots of work without returning a value. Such functions are sometimes called procedures in other languages. Because they don’t return values that you might be interested in retaining, you can call these functions with expression statements.
Python echoes back the results of expressions typed at the
interactive command line. Technically, these are expression
statements, too; they serve as a shorthand for typing print
statements.
Table 11-4 lists some common expression statement forms in Python. Calls to functions and methods are coded with zero or more argument objects (really, expressions that evaluate to objects) in parentheses, after the function/method name.
Operation |
Interpretation |
|
Function calls |
|
Method calls |
|
Printing variables in the interactive interpreter |
|
Compound expressions |
|
Range tests |
The last line in the table is a special form: Python lets us string
together magnitude comparison tests, to code chained comparisons such as
range tests. For instance, the expression (A <
B < C)
tests whether B
is
between A
and C
; it’s equivalent to the Boolean test (A < B and B < C)
, but is easier on the
eyes (and the keyboard). Compound expressions aren’t normally written as
statements, but doing so is syntactically legal, and can even be useful at
the interactive prompt if you’re not sure of an expression’s
result.
Beware that although expressions can appear as statements in Python,
statements can’t be used as expressions. For instance, Python doesn’t
allow you to embed assignment statements (=
) in other expressions. The rationale for this
is that it avoids common coding mistakes; you can’t accidentally change a
variable by typing =
when you really
mean to use the ==
equality test.
You’ll see how to code around this when you meet the Python while
loop in Chapter 13.
This brings up a mistake that is common in Python work. Expression statements are often used to run list methods that change a list in-place:
>>>L = [1, 2]
>>>L.append(3)
# Append is an in-place change >>>L
[1, 2, 3]
However, it’s not unusual for Python newcomers to code such an
operation as an assignment statement instead, intending to assign
L
to the larger list:
>>>L = L.append(4)
# But append returns None, not L >>>print L
# So we lose our list! None
This doesn’t quite work, though—calling an in-place change
operation such as append
, sort
, or reverse
on a list always changes the list
in-place, but these methods do not return the list they have
changed.
In fact, they return the None
object. If you assign such an operation’s result back to the variable
name, you effectively lose the list (and it is probably garbage
collected in the process!).
So, don’t do this. We’ll revisit this phenomenon in the "Common Coding Gotchas" warnings section at the end of this part of the book because it can also appear in the context of some looping statements we’ll meet in later chapters.
The print
statement prints
things—it’s simply a programmer-friendly interface to the standard output
stream. Technically, it converts an object to its textual representation,
and sends this to standard output.
The standard output stream is the same as the C language’s stdout
; it is usually mapped to the window where
you started your Python program (unless redirected to a file or pipe in
your system’s shell).
In Chapter 9, we
looked at some file methods that write text. The print
statement is similar, but more focused:
print
writes objects to the stdout
stream (with some default formatting),
but file write methods write strings to arbitrary files. Because the
standard output stream is available in Python as the stdout
object in the built-in sys
module (i.e., sys.stdout
), it’s possible to emulate print
with file writes, but print
is easier to use.
Table 11-5 lists the print
statement’s forms.
We’ve seen the basic print
statement in action already. By default, it adds a space between the items
separated by commas, and adds a linefeed at the end of the current output
line:
>>>x = 'a'
>>>y = 'b'
>>>print x, y
a b
This formatting is just a default; you can choose to use it or not.
To suppress the linefeed (so you can add more text to the current line
later), end your print
statement with a
comma, as shown in the second line of Table 11-5. To suppress the space between items,
don’t print this way—instead, build up an output string yourself using the
string concatenation and formatting tools covered in Chapter 7, and print the string all at once:
>>>print x + y
ab >>>print '%s...%s' % (x, y)
a...b
To print a “hello world” message, simply print the string:
>>> print 'hello world'
# Print a string object
hello world
Because expression results are echoed on the interactive command
line, you often don’t even need to use a print
statement there; simply type expressions
you’d like to have printed, and their results are echoed back:
>>> 'hello world'
# Interactive echoes
'hello world'
Really, the print
statement is
just an ergonomic feature of Python—it provides a simple interface to
the sys.stdout
object, with a bit of
default formatting. In fact, if you like to work harder than you must,
you can also code print operations this way:
>>>import sys
# Printing the hard way >>>sys.stdout.write('hello world ')
hello world
This code explicitly calls the write
method of sys.stdout
—an attribute preset when Python
starts up to an open file object connected to the output stream. The
print
statement hides most of those
details, providing a simple tool for simple printing tasks.
So, why did I just show you the hard way to print? The sys.stdout
print equivalent turns out to be
the basis of a common technique in Python. In general, print
and sys.stdout
are related as follows. This
statement:
print X
is equivalent to the longer:
import sys sys.stdout.write(str(X) + ' ')
which manually performs a string conversion with str
, adds a newline with +
, and calls the output stream’s write
method. The long form isn’t all that
useful for printing by itself. However, it is useful to know that this
is exactly what print
statements do
because it is possible to reassign sys.stdout
to something different from the
standard output stream. In other words, this equivalence provides a way
for making your print
statements send
their text to other places. For example:
import sys sys.stdout = open('log.txt', 'a') # Redirects prints to file ... print x, y, x # Shows up in log.txt
Here, we reset sys.stdout
to a
manually opened output file object opened in append mode. After the
reset, every print
statement anywhere
in the program will write its text to the end of the file log.txt instead of to the original output
stream. The print
statements are
happy to keep calling sys.stdout
’s
write
method, no matter what sys.stdout
happens to refer to. Because there
is just one sys
module in your
process, assigning sys.stdout
this
way will redirect every print
anywhere in your program.
In fact, as this chapter’s upcoming sidebar on print
and stdout
will explain, you can even reset
sys.stdout
to a nonfile object, as
long as it has the expected protocol (a write
method); when that object is a class,
printed text can be routed and processed arbitrarily.
This trick of resetting the output stream is primarily useful for
programs originally coded with print
statements. If you know that output should go to a file to begin with,
you can always call file write methods instead. To redirect the output
of a print
-based program, though,
resetting sys.stdout
provides a
convenient alternative to changing every print
statement, or using system-shell-based
redirection syntax.
This trick of redirecting printed text by assigning sys.stdout
is very commonly used in practice.
But one potential problem with the last section’s code is that there is
no direct way to restore the original output stream should you need to
switch back after printing to a file. Because sys.stdout
is just a normal file object, you
can always save it and restore it if needed:[30]
>>>import sys
>>>temp = sys.stdout
# Save for restoring later >>>sys.stdout = open('log.txt', 'a')
# Redirect prints to a file >>>print 'spam'
# Prints go to file, not here >>>print 1, 2, 3
>>>sys.stdout.close( )
# Flush output to disk >>>sys.stdout = temp
# Restore original stream >>>print 'back here'
# Prints show up here again back here >>>print open('log.txt').read( )
# Result of earlier prints spam 1 2 3
This need crops up fairly regularly, and this manual saving and
restoring of the original output stream is just complicated enough that
a print
extension was added to make
it unnecessary. When a print
statement begins with a >>
followed by an output file (or other) object, that single print
statement sends its text to the object’s
write
method, but it does not reset
sys.stdout
. Because the redirection
is temporary, normal print
statements
keep printing to the original output stream:
log = open('log.txt', 'a') print >> log, x, y, z # Print to a file-like object print a, b, c # Print to original stdout
The >>
form of print
is handy if you need to print to both
files and the standard output stream in the same program. If you use
this form, however, be sure to give it a file object (or an object that
has the same write
method as a file
object), not a file’s name string:
>>>log = open('log.txt', 'w')
>>>print >> log, 1, 2, 3
>>>print >> log, 4, 5, 6
>>>log.close( )
>>>print 7, 8, 9
7 8 9 >>>print open('log.txt').read( )
1 2 3 4 5 6
This extended form of print
is
also commonly used to print error messages to the standard error stream,
sys.stderr
. You can either use its
file write methods and format the output manually, or print with
redirection syntax:
>>>import sys
>>>sys.stderr.write(('Bad!' * 8) + ' ')
Bad!Bad!Bad!Bad!Bad!Bad!Bad!Bad! >>>print >> sys.stderr, 'Bad!' * 8
Bad!Bad!Bad!Bad!Bad!Bad!Bad!Bad!
In Python 3.0, the print
statement is scheduled to become a built-in function, with equivalent
utility, but slightly different syntax. The target file and
end-of-line behavior are to be specified with keyword arguments. For
instance, the statement print x, y
will become the call print(x, y)
,
and print >> f, x
will become
print(x, file=f, end=' ')
.
All of this is still in the future, so consult the 3.0 release
notes for more details. Current 3.0 plans also call for including a
converter script (tentatively named “2to3”) that will, among a handful
of other things, automatically convert print
statements in your existing code to
print
calls. It should help, if you
find yourself torn between 2.x and 3.x (like this book!).
In this chapter, we began our in-depth look at Python statements by
exploring assignments, expressions, and print
s. Although these are generally simple to
use, they have some alternative forms that are optional, but often
convenient in practice. Augmented assignment statements and the
redirection form of print
statements,
for example, allow us to avoid some manual coding work. Along the way, we
also studied the syntax of variable names, stream redirection techniques,
and a variety of common mistakes to avoid, such as assigning the result of
an append
method call back to a
variable.
In the next chapter, we continue our statement tour by filling in
details about the if
statement,
Python’s main selection tool; there, we’ll also revisit Python’s syntax
model in more depth, and look at the behavior of Boolean expressions.
Before we move on, though, the end-of-chapter quiz will test your
knowledge of what you’ve learned here.
1. Chapter Quiz | |
Q: | Name three ways that you can assign three variables to the same value. |
Q: | Why might you need to care when assigning three variables to a mutable object? |
Q: | What’s wrong with saying |
Q: | How might you use the |
2. Quiz Answers | |
Q: | |
A: | You can use multiple-target assignments ( |
A: | If you assign them this way: A = B = C = [] all three names reference the same object, so changing it
in-place from one (e.g., |
Q: | |
A: | The list |
Q: | |
A: | You can assign |
[26] * C/C++ programmers take note:
although Python now supports statements like X += Y
, it still does not have C’s
auto-increment/decrement operators (e.g., X++
, −−X
). These don’t quite map to the Python
object model because Python has no notion of in-place changes to
immutable objects like numbers.
[27] † As suggested in Chapter 6, we can also use slice
assignment (e.g., L[len(L):] =
[11,12,13]
), but this works roughly the same as the
simpler list extend
method.
[28] * In the Jython Java-based implementation of Python, though, user-defined variable names can sometimes be the same as Python reserved words.
[29] * If you’ve used C++, you may be
interested to know that there is no notion of C++’s const
declaration in Python; certain
objects may be immutable, but names can always be assigned. Python
also has ways to hide names in classes and modules, but they’re
not the same as C++’s declarations.
[30] * You can also use the relatively
new _ _stdout_ _
attribute in the
sys
module, which refers to the
original value sys.stdout
had at
program startup time. You still need to restore sys.stdout
to sys._ _stdout_ _
to go back to this
original stream value, though. See the sys
module in the library manual for more
details.