Input and Output¶

Old string formatting¶

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.

Methods of File Objects¶

The rest of the examples in this section will assume that a file object called f has already been created.

To read a file’s contents, call f.read(size), which reads some quantity of data and returns it as a string 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.

The pickle Module¶

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.