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() function. (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 layout 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 str.format() method.
The string module contains a class Template which offers yet another way to substitute values into strings.
One question remains, of course: how do you convert values to strings? Luckily, Python has ways to convert any value to a string: pass it to the repr() or str() functions.
The str() function is meant to return representations of values which are fairly human-readable, while repr() is meant to generate representations which can be read by the interpreter (or will force a SyntaxError if there is not equivalent syntax). For objects which don’t have a particular representation for human consumption, str() will return the same value as repr(). Many values, such as numbers or structures like lists and dictionaries, have the same representation using either function. Strings and floating point numbers, in particular, have two distinct representations.
Some examples:
>>> s = 'Hello, world.'
>>> str(s)
'Hello, world.'
>>> repr(s)
"'Hello, world.'"
>>> str(0.1)
'0.1'
>>> repr(0.1)
'0.10000000000000001'
>>> x = 10 * 3.25
>>> y = 200 * 200
>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
>>> print(s)
The value of x is 32.5, and y is 40000...
>>> # The repr() of a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = repr(hello)
>>> print(hellos)
'hello, world\n'
>>> # The argument to repr() may be any Python object:
... repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"
Here are two ways to write a table of squares and cubes:
>>> for x in range(1, 11):
... print(repr(x).rjust(2), repr(x*x).rjust(3), end=' ')
... # Note use of 'end' on previous line
... print(repr(x*x*x).rjust(4))
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
>>> for x in range(1, 11):
... print('{0:2d} {1:3d} {2:4d}'.format(x, x*x, x*x*x))
...
1 1 1
2 4 8
3 9 27
4 16 64
5 25 125
6 36 216
7 49 343
8 64 512
9 81 729
10 100 1000
(Note that in the first example, one space between each column was added by the way print() works: it always adds spaces between its arguments.)
This example demonstrates the rjust() method of string objects, which right-justifies a string in a field of a given width by padding it with spaces on the left. There are similar methods ljust() and center(). These methods do not write anything, they just return a new string. If the input string is too long, they don’t truncate it, but return it unchanged; this will mess up your column lay-out but that’s usually better than the alternative, which would be lying about a value. (If you really want truncation you can always add a slice operation, as in x.ljust(n)[:n].)
There is another method, zfill(), which pads a numeric string on the left with zeros. It understands about plus and minus signs:
>>> '12'.zfill(5)
'00012'
>>> '-3.14'.zfill(7)
'-003.14'
>>> '3.14159265359'.zfill(5)
'3.14159265359'
Basic usage of the str.format() method looks like this:
>>> print('We are the {0} who say "{1}!"'.format('knights', 'Ni'))
We are the knights who say "Ni!"
The brackets and characters within them (called format fields) are replaced with the objects passed into the format method. The number in the brackets refers to the position of the object passed into the format method.
>>> print('{0} and {1}'.format('spam', 'eggs'))
spam and eggs
>>> print('{1} and {0}'.format('spam', 'eggs'))
eggs and spam
If keyword arguments are used in the format method, their values are referred to by using the name of the argument.
>>> print('This {food} is {adjective}.'.format(
... food='spam', adjective='absolutely horrible'))
This spam is absolutely horrible.
Positional and keyword arguments can be arbitrarily combined:
>>> print('The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred',
other='Georg'))
The story of Bill, Manfred, and Georg.
An optional ':' and format specifier can follow the field name. This also greater control over how the value is formatted. The following example truncates the Pi to three places after the decimal.
>>> import math
>>> print('The value of PI is approximately {0:.3f}.'.format(math.pi))
The value of PI is approximately 3.142.
Passing an integer after the ':' will cause that field to be a minimum number of characters wide. This is useful for making tables pretty.:
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print('{0:10} ==> {1:10d}'.format(name, phone))
...
Jack ==> 4098
Dcab ==> 7678
Sjoerd ==> 4127
If you have a really long format string that you don’t want to split up, it would be nice if you could reference the variables to be formatted by name instead of by position. This can be done by simply passing the dict and using square brackets '[]' to access the keys
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print('Jack: {0[Jack]:d}; Sjoerd: {0[Sjoerd]:d}; '
'Dcab: {0[Dcab]:d}'.format(table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This could also be done by passing the table as keyword arguments with the ‘**’ notation.:
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
>>> print('Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table))
Jack: 4098; Sjoerd: 4127; Dcab: 8637678
This is particularly useful in combination with the new built-in vars() function, which returns a dictionary containing all local variables.
For a complete overview of string formatting with str.format(), see Format String Syntax.
The % operator can also be used for string formatting. It interprets the left argument much like a sprintf-style format string to be applied to the right argument, and returns the string resulting from this formatting operation. For example:
>>> import math
>>> print('The value of PI is approximately %5.3f.' % math.pi)
The value of PI is approximately 3.142.
Since str.format() is quite new, a lot of Python code still uses the % operator. However, because this old style of formatting will eventually removed from the language str.format() should generally be used.
More information can be found in the Old String Formatting Operations section.
open() returns a file object, and is most commonly used with two arguments: open(filename, mode).
>>> f = open('/tmp/workfile', 'w')
The first argument is a string containing the filename. The second argument is another string containing a few characters describing the way in which the file will be used. mode can be 'r' when the file will only be read, 'w' for only writing (an existing file with the same name will be erased), and 'a' opens the file for appending; any data written to the file is automatically added to the end. 'r+' opens the file for both reading and writing. The mode argument is optional; 'r' will be assumed if it’s omitted.
Normally, files are opened in text mode, that means, you read and write strings from and to the file, which are encoded in a specific encoding (the default being UTF-8). 'b' appended to the mode opens the file in binary mode: now the data is read and written in the form of bytes objects. This mode should be used for all files that don’t contain text.
In text mode, the default is to convert platform-specific line endings (\n on Unix, \r\n on Windows) to just \n on reading and \n back to platform-specific line endings on writing. This behind-the-scenes modification to file data is fine for text files, but will corrupt binary data like that in JPEG or EXE files. Be very careful to use binary mode when reading and writing such files.
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 or bytes object. size is an optional numeric argument. When size is omitted or negative, the entire contents of the file will be read and returned; it’s your problem if the file is twice as large as your machine’s memory. Otherwise, at most size bytes are read and returned. If the end of the file has been reached, f.read() will return an empty string ('').
>>> f.read()
'This is the entire file.\n'
>>> f.read()
''
f.readline() reads a single line from the file; a newline character (\n) is left at the end of the string, and is only omitted on the last line of the file if the file doesn’t end in a newline. This makes the return value unambiguous; if f.readline() returns an empty string, the end of the file has been reached, while a blank line is represented by '\n', a string containing only a single newline.
>>> f.readline()
'This is the first line of the file.\n'
>>> f.readline()
'Second line of the file\n'
>>> f.readline()
''
f.readlines() returns a list containing all the lines of data in the file. If given an optional parameter sizehint, it reads that many bytes from the file and enough more to complete a line, and returns the lines from that. This is often used to allow efficient reading of a large file by lines, but without having to load the entire file in memory. Only complete lines will be returned.
>>> f.readlines()
['This is the first line of the file.\n', 'Second line of the file\n']
An alternative approach to reading lines is to loop over the file object. This is memory efficient, fast, and leads to simpler code:
>>> for line in f:
... print(line, end='')
...
This is the first line of the file.
Second line of the file
The alternative approach is simpler but does not provide as fine-grained control. Since the two approaches manage line buffering differently, they should not be mixed.
f.write(string) writes the contents of string to the file, returning the number of characters written.
>>> f.write('This is a test\n')
15
To write something other than a string, it needs to be converted to a string first:
>>> value = ('the answer', 42)
>>> s = str(value)
>>> f.write(s)
18
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', 'rb+')
>>> f.write(b'0123456789abcdef')
16
>>> f.seek(5) # Go to the 6th byte in the file
5
>>> f.read(1)
b'5'
>>> f.seek(-3, 2) # Go to the 3rd byte before the end
13
>>> f.read(1)
b'd'
In text files (those opened without a b in the mode string), only seeks relative to the beginning of the file are allowed (the exception being seeking to the very file end with seek(0, 2)).
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 (most recent call last):
File "<stdin>", line 1, in ?
ValueError: I/O operation on closed file
It is good practice to use the with keyword when dealing with file objects. This has the advantage that the file is properly closed after its suite finishes, even if an exception is raised on the way. It is also much shorter than writing equivalent try-finally blocks:
>>> with open('/tmp/workfile', 'r') as f:
... read_data = f.read()
>>> f.closed
True
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 int(), 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 Python 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.