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Python Tutorial |  |
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There are several ways to present the output of a program; data can be printed in a human-readable form, or written to a file for future use. This chapter will discuss some of the possibilities.
So far we've encountered two ways of writing values: expression
statements and the print statement. (A third way is using
the write() method of file objects; the standard output file
can be referenced as sys.stdout. See the Library Reference for
more information on this.)
Often you'll want more control over the formatting of your output than
simply printing space-separated values. There are two ways to format
your output; the first way is to do all the string handling yourself;
using string slicing and concatenation operations you can create any
lay-out you can imagine. The standard module
string contains some useful operations
for padding strings to a given column width; these will be discussed
shortly. The second way is to use the % operator with a
string as the left argument. The % operator interprets the
left argument as a C much like a sprintf()-style format
string to be applied to the right argument, and returns the string
resulting from this formatting operation.
One question remains, of course: how do you convert values to strings?
Luckily, Python has a way to convert any value to a string: pass it to
the repr() function, or just write the value between
reverse quotes (``). Some examples:
>>> x = 10 * 3.14
>>> y = 200*200
>>> s = 'The value of x is ' + `x` + ', and y is ' + `y` + '...'
>>> print s
The value of x is 31.4, and y is 40000...
>>> # Reverse quotes work on other types besides numbers:
... p = [x, y]
>>> ps = repr(p)
>>> ps
'[31.4, 40000]'
>>> # Converting a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = `hello`
>>> print hellos
'hello, world\012'
>>> # The argument of reverse quotes may be a tuple:
... `x, y, ('spam', 'eggs')`
"(31.4, 40000, ('spam', 'eggs'))"
Here are two ways to write a table of squares and cubes:
>>> import string >>> for x in range(1, 11): ... print string.rjust(`x`, 2), string.rjust(`x*x`, 3), ... # Note trailing comma on previous line ... print string.rjust(`x*x*x`, 4) ... 1 1 1 2 4 8 3 9 27 4 16 64 5 25 125 6 36 216 7 49 343 8 64 512 9 81 729 10 100 1000 >>> for x in range(1,11): ... print'%2d %3d %4d' % (x, x*x, x*x*x) ... 1 1 1 2 4 8 3 9 27 4 16 64 5 25 125 6 36 216 7 49 343 8 64 512 9 81 729 10 100 1000
(Note that one space between each column was added by the way print works: it always adds spaces between its arguments.)
This example demonstrates the function string.rjust(), which right-justifies a string in a field of a given width by padding it with spaces on the left. There are similar functions string.ljust() and string.center(). These functions do not write anything, they just return a new string. If the input string is too long, they don't truncate it, but return it unchanged; this will mess up your column lay-out but that's usually better than the alternative, which would be lying about a value. (If you really want truncation you can always add a slice operation, as in "string.ljust(x, n)[0:n]".)
There is another function, string.zfill(), which pads a numeric string on the left with zeros. It understands about plus and minus signs:
>>> string.zfill('12', 5)
'00012'
>>> string.zfill('-3.14', 7)
'-003.14'
>>> string.zfill('3.14159265359', 5)
'3.14159265359'
% operator looks like this:
>>> import math >>> print 'The value of PI is approximately %5.3f.' % math.pi The value of PI is approximately 3.142.
If there is more than one format in the string you pass a tuple as right operand, e.g.
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print'%-10s ==> %10d' % (name, phone)
...
Jack ==> 4098
Dcab ==> 8637678
Sjoerd ==> 4127
Most formats work exactly as in C and require that you pass the proper
type; however, if you don't you get an exception, not a core dump.
The %s format is more relaxed: if the corresponding argument is
not a string object, it is converted to string using the
str() built-in function. Using * to pass the width
or precision in as a separate (integer) argument is supported. The
C formats %n and %p are not supported.
If you have a really long format string that you don't want to split
up, it would be nice if you could reference the variables to be
formatted by name instead of by position. This can be done by using
an extension of C formats using the form %(name)format, e.g.
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print 'Jack: %(Jack)d; Sjoerd: %(Sjoerd)d; Dcab: %(Dcab)d' % table
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This is particularly useful in combination with the new built-in vars() function, which returns a dictionary containing all local variables.
open() returns a file object, and is most commonly used with two arguments: "open(filename, mode)".
>>> f=open('/tmp/workfile', 'w')
>>> print f
<open file '/tmp/workfile', mode 'w' at 80a0960>
The first argument is a string containing the filename. The second
argument is another string containing a few characters describing the
way in which the file will be used. mode can be 'r' when
the file will only be read, 'w' for only writing (an existing
file with the same name will be erased), and 'a' opens the file
for appending; any data written to the file is automatically added to
the end. 'r+' opens the file for both reading and writing.
The mode argument is optional; 'r' will be assumed if
it's omitted.
On Windows and the Macintosh, 'b' appended to the
mode opens the file in binary mode, so there are also modes like
'rb', 'wb', and 'r+b'. Windows makes a
distinction between text and binary files; the end-of-line characters
in text files are automatically altered slightly when data is read or
written. This behind-the-scenes modification to file data is fine for
ASCII text files, but it'll corrupt binary data like that in JPEGs or
.EXE files. Be very careful to use binary mode when reading and
writing such files. (Note that the precise semantics of text mode on
the Macintosh depends on the underlying C library being used.)
The rest of the examples in this section will assume that a file
object called f has already been created.
To read a file's contents, call f.read(size), which reads
some quantity of data and returns it as a string. size is an
optional numeric argument. When size is omitted or negative,
the entire contents of the file will be read and returned; it's your
problem if the file is twice as large as your machine's memory.
Otherwise, at most size bytes are read and returned. If the end
of the file has been reached, f.read() will return an empty
string ("").
>>> f.read() 'This is the entire file.\012' >>> f.read() ''
f.readline() reads a single line from the file; a newline
character (\n) is left at the end of the string, and is only
omitted on the last line of the file if the file doesn't end in a
newline. This makes the return value unambiguous; if
f.readline() returns an empty string, the end of the file has
been reached, while a blank line is represented by '\n', a
string containing only a single newline.
>>> f.readline() 'This is the first line of the file.\012' >>> f.readline() 'Second line of the file\012' >>> f.readline() ''
f.readlines() uses f.readline() repeatedly, and returns
a list containing all the lines of data in the file.
>>> f.readlines() ['This is the first line of the file.\012', 'Second line of the file\012']
f.write(string) writes the contents of string to
the file, returning None.
>>> f.write('This is a test\n')
f.tell() returns an integer giving the file object's current
position in the file, measured in bytes from the beginning of the
file. To change the file object's position, use
"f.seek(offset, from_what)". The position is
computed from adding offset to a reference point; the reference
point is selected by the from_what argument. A
from_what value of 0 measures from the beginning of the file, 1
uses the current file position, and 2 uses the end of the file as the
reference point. from_what can be omitted and defaults to 0,
using the beginning of the file as the reference point.
>>> f=open('/tmp/workfile', 'r+')
>>> f.write('0123456789abcdef')
>>> f.seek(5) # Go to the 5th byte in the file
>>> f.read(1)
'5'
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
>>> f.read(1)
'd'
When you're done with a file, call f.close() to close it and
free up any system resources taken up by the open file. After calling
f.close(), attempts to use the file object will automatically fail.
>>> f.close() >>> f.read() Traceback (innermost last): File "<stdin>", line 1, in ? ValueError: I/O operation on closed file
File objects have some additional methods, such as isatty() and truncate() which are less frequently used; consult the Library Reference for a complete guide to file objects.
Strings can easily be written to and read from a file. Numbers take a
bit more effort, since the read() method only returns
strings, which will have to be passed to a function like
string.atoi(), which takes a string like '123' and
returns its numeric value 123. However, when you want to save more
complex data types like lists, dictionaries, or class instances,
things get a lot more complicated.
Rather than have users be constantly writing and debugging code to save complicated data types, Python provides a standard module called pickle. This is an amazing module that can take almost any Python object (even some forms of Python code!), and convert it to a string representation; this process is called pickling. Reconstructing the object from the string representation is called unpickling. Between pickling and unpickling, the string representing the object may have been stored in a file or data, or sent over a network connection to some distant machine.
If you have an object x, and a file object f that's been
opened for writing, the simplest way to pickle the object takes only
one line of code:
pickle.dump(x, f)
To unpickle the object again, if f is a file object which has
been opened for reading:
x = pickle.load(f)
(There are other variants of this, used when pickling many objects or when you don't want to write the pickled data to a file; consult the complete documentation for pickle in the Library Reference.)
pickle is the standard way to make Python objects which can be stored and reused by other programs or by a future invocation of the same program; the technical term for this is a persistent object. Because pickle is so widely used, many authors who write Python extensions take care to ensure that new data types such as matrices can be properly pickled and unpickled.