7. Input and Output¶
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.
7.1. Fancier Output Formatting¶
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
string type has some methods that perform useful operations for padding
strings to a given column width; these will be discussed shortly. The second
way is to use formatted string literals, or the
str.format() method.
The string module contains a Template class 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 no 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, in
particular, have two distinct representations.
Some examples:
>>> s = 'Hello, world.'
>>> str(s)
'Hello, world.'
>>> repr(s)
"'Hello, world.'"
>>> str(1/7)
'0.14285714285714285'
>>> 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: by default it adds spaces between its arguments.)
This example demonstrates the str.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 str.ljust() and
str.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, str.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 {} who say "{}!"'.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 str.format() method. A number in the
brackets can be used to refer to the position of the object passed into the
str.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 str.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.
'!a' (apply ascii()), '!s' (apply str()) and '!r'
(apply repr()) can be used to convert the value before it is formatted:
>>> contents = 'eels'
>>> print('My hovercraft is full of {}.'.format(contents))
My hovercraft is full of eels.
>>> print('My hovercraft is full of {!r}.'.format(contents))
My hovercraft is full of 'eels'.
An optional ':' and format specifier can follow the field name. This allows
greater control over how the value is formatted. The following example
rounds 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 built-in function
vars(), which returns a dictionary containing all local variables.
For a complete overview of string formatting with str.format(), see
Format String Syntax.
7.1.1. 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.
More information can be found in the printf-style String Formatting section.
7.2. Reading and Writing Files¶
open() returns a file object, and is most commonly used with
two arguments: open(filename, mode).
>>> f = open('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. If
encoding is not specified, the default is platform dependent (see
open()). '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 when reading is to convert platform-specific line
endings (\n on Unix, \r\n on Windows) to just \n. When writing in
text mode, the default is to convert occurrences of \n back to
platform-specific line endings. 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.
It is good practice to use the with keyword when dealing
with file objects. The advantage is that the file is properly closed
after its suite finishes, even if an exception is raised at some
point. Using with is also much shorter than writing
equivalent try-finally blocks:
>>> with open('workfile') as f:
... read_data = f.read()
>>> f.closed
True
If you’re not using the with keyword, then you should call
f.close() to close the file and immediately free up any system
resources used by it. If you don’t explicitly close a file, Python’s
garbage collector will eventually destroy the object and close the
open file for you, but the file may stay open for a while. Another
risk is that different Python implementations will do this clean-up at
different times.
After a file object is closed, either by a with statement
or by 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 <module>
ValueError: I/O operation on closed file.
7.2.1. 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 (in text mode) or bytes object (in binary mode).
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()
''
For reading lines from a file, you can loop over the file object. This is memory efficient, fast, and leads to simple code:
>>> for line in f:
... print(line, end='')
...
This is the first line of the file.
Second line of the file
If you want to read all the lines of a file in a list you can also use
list(f) or f.readlines().
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
Other types of objects need to be converted – either to a string (in text mode) or a bytes object (in binary mode) – before writing them:
>>> value = ('the answer', 42)
>>> s = str(value) # convert the tuple to string
>>> f.write(s)
18
f.tell() returns an integer giving the file object’s current position in the file
represented as number of bytes from the beginning of the file when in binary mode and
an opaque number when in text mode.
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('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)) and the only valid offset values are
those returned from the f.tell(), or zero. Any other offset value produces
undefined behaviour.
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.
7.2.2. Saving structured data with json¶
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. When you want to save more complex data
types like nested lists and dictionaries, parsing and serializing by hand
becomes complicated.
Rather than having users constantly writing and debugging code to save
complicated data types to files, Python allows you to use the popular data
interchange format called JSON (JavaScript Object Notation). The standard module called json can take Python
data hierarchies, and convert them to string representations; this process is
called serializing. Reconstructing the data from the string representation
is called deserializing. Between serializing and deserializing, the
string representing the object may have been stored in a file or data, or
sent over a network connection to some distant machine.
Note
The JSON format is commonly used by modern applications to allow for data exchange. Many programmers are already familiar with it, which makes it a good choice for interoperability.
If you have an object x, you can view its JSON string representation with a
simple line of code:
>>> import json
>>> json.dumps([1, 'simple', 'list'])
'[1, "simple", "list"]'
Another variant of the dumps() function, called dump(),
simply serializes the object to a text file. So if f is a
text file object opened for writing, we can do this:
json.dump(x, f)
To decode the object again, if f is a text file object which has
been opened for reading:
x = json.load(f)
This simple serialization technique can handle lists and dictionaries, but
serializing arbitrary class instances in JSON requires a bit of extra effort.
The reference for the json module contains an explanation of this.
See also
pickle - the pickle module
Contrary to JSON, pickle is a protocol which allows the serialization of arbitrarily complex Python objects. As such, it is specific to Python and cannot be used to communicate with applications written in other languages. It is also insecure by default: deserializing pickle data coming from an untrusted source can execute arbitrary code, if the data was crafted by a skilled attacker.