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Python 2.7.13 Beginner's Guide

Hey Guys!I will be providing you the Python 2.7.13 tutorial in 3 parts!
Here is the First Part!

The Python 2.7.13 Tutorial

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site,https://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.
The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.
This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.
For a description of standard objects and modules, see The Python Standard Library. The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual. There are also several books covering Python in depth.
This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library.
The Glossary is also worth going through.

1.Stimulating your Appetite 

                               If you do much work on computers, eventually you find that there’s some task you’d like to automate. For example, you may wish to perform a search-and-replace over a large number of text files, or rename and rearrange a bunch of photo files in a complicated way. Perhaps you’d like to write a small custom database, or a specialized GUI application, or a simple game.
If you’re a professional software developer, you may have to work with several C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is too slow. Perhaps you’re writing a test suite for such a library and find writing the testing code a tedious task. Or maybe you’ve written a program that could use an extension language, and you don’t want to design and implement a whole new language for your application.
Python is just the language for you.
You could write a Unix shell script or Windows batch files for some of these tasks, but shell scripts are best at moving around files and changing text data, not well-suited for GUI applications or games. You could write a C/C++/Java program, but it can take a lot of development time to get even a first-draft program. Python is simpler to use, available on Windows, Mac OS X, and Unix operating systems, and will help you get the job done more quickly.
Python is simple to use, but it is a real programming language, offering much more structure and support for large programs than shell scripts or batch files can offer. On the other hand, Python also offers much more error checking than C, and, being a very-high-level language, it has high-level data types built in, such as flexible arrays and dictionaries. Because of its more general data types Python is applicable to a much larger problem domain than Awk or even Perl, yet many things are at least as easy in Python as in those languages.
Python allows you to split your program into modules that can be reused in other Python programs. It comes with a large collection of standard modules that you can use as the basis of your programs — or as examples to start learning to program in Python. Some of these modules provide things like file I/O, system calls, sockets, and even interfaces to graphical user interface toolkits like Tk.
Python is an interpreted language, which can save you considerable time during program development because no compilation and linking is necessary. The interpreter can be used interactively, which makes it easy to experiment with features of the language, to write throw-away programs, or to test functions during bottom-up program development. It is also a handy desk calculator.
Python enables programs to be written compactly and readably. Programs written in Python are typically much shorter than equivalent C, C++, or Java programs, for several reasons:
  • the high-level data types allow you to express complex operations in a single statement;
  • statement grouping is done by indentation instead of beginning and ending brackets;
  • no variable or argument declarations are necessary.
Python is extensible: if you know how to program in C it is easy to add a new built-in function or module to the interpreter, either to perform critical operations at maximum speed, or to link Python programs to libraries that may only be available in binary form (such as a vendor-specific graphics library). Once you are really hooked, you can link the Python interpreter into an application written in C and use it as an extension or command language for that application.
By the way, the language is named after the BBC show “Monty Python’s Flying Circus” and has nothing to do with reptiles. Making references to Monty Python skits in documentation is not only allowed, it is encouraged!
Now that you are all excited about Python, you’ll want to examine it in some more detail. Since the best way to learn a language is to use it, the tutorial invites you to play with the Python interpreter as you read.
In the next chapter, the mechanics of using the interpreter are explained. This is rather mundane information, but essential for trying out the examples shown later.
The rest of the tutorial introduces various features of the Python language and system through examples, beginning with simple expressions, statements and data types, through functions and modules, and finally touching upon advanced concepts like exceptions and user-defined classes.

2. Using the Python Interpreter


2.1. Invoking the Interpreter

The Python interpreter is usually installed as /usr/local/bin/python on those machines where it is available; putting /usr/local/bin in your Unix shell’s search path makes it possible to start it by typing the command
python
to the shell. Since the choice of the directory where the interpreter lives is an installation option, other places are possible; check with your local Python guru or system administrator. (E.g., /usr/local/python is a popular alternative location.)
On Windows machines, the Python installation is usually placed in C:\Python27, though you can change this when you’re running the installer. To add this directory to your path, you can type the following command into the command prompt in a DOS box:
set path=%path%;C:\python27
Typing an end-of-file character (Control-D on Unix, Control-Z on Windows) at the primary prompt causes the interpreter to exit with a zero exit status. If that doesn’t work, you can exit the interpreter by typing the following command: quit().
The interpreter’s line-editing features usually aren’t very sophisticated. On Unix, whoever installed the interpreter may have enabled support for the GNU readline library, which adds more elaborate interactive editing and history features. Perhaps the quickest check to see whether command line editing is supported is typing Control-P to the first Python prompt you get. If it beeps, you have command line editing; see Appendix Interactive Input Editing and History Substitution for an introduction to the keys. If nothing appears to happen, or if ^P is echoed, command line editing isn’t available; you’ll only be able to use backspace to remove characters from the current line.
The interpreter operates somewhat like the Unix shell: when called with standard input connected to a tty device, it reads and executes commands interactively; when called with a file name argument or with a file as standard input, it reads and executes a script from that file.
A second way of starting the interpreter is python -c command [arg] ..., which executes the statement(s) in command, analogous to the shell’s -c option. Since Python statements often contain spaces or other characters that are special to the shell, it is usually advised to quote command in its entirety with single quotes.
Some Python modules are also useful as scripts. These can be invoked using python -m module [arg] ..., which executes the source file for module as if you had spelled out its full name on the command line.
When a script file is used, it is sometimes useful to be able to run the script and enter interactive mode afterwards. This can be done by passing -ibefore the script.
All command-line options are described in Command line and environment.

2.1.1. Argument Passing

When known to the interpreter, the script name and additional arguments thereafter are turned into a list of strings and assigned to the argv variable in the sys module. You can access this list by executing import sys. The length of the list is at least one; when no script and no arguments are given, sys.argv[0] is an empty string. When the script name is given as '-' (meaning standard input), sys.argv[0] is set to '-'. When -c command is used, sys.argv[0] is set to '-c'. When -m module is used, sys.argv[0] is set to the full name of the located module. Options found after -c command or -mmodule are not consumed by the Python interpreter’s option processing but left in sys.argv for the command or module to handle.

2.1.2. Interactive Mode

When commands are read from a tty, the interpreter is said to be in interactive mode. In this mode it prompts for the next command with the primary prompt, usually three greater-than signs (>>>); for continuation lines it prompts with the secondary prompt, by default three dots (...). The interpreter prints a welcome message stating its version number and a copyright notice before printing the first prompt:
python
Python 2.7 (#1, Feb 28 2010, 00:02:06)
Type "help", "copyright", "credits" or "license" for more information.
>>>
Continuation lines are needed when entering a multi-line construct. As an example, take a look at this if statement:
>>>
>>> the_world_is_flat = 1
>>> if the_world_is_flat:
...     print "Be careful not to fall off!"
...
Be careful not to fall off!
For more on interactive mode, see Interactive Mode.

2.2. The Interpreter and Its Environment


2.2.1. Source Code Encoding

By default, Python source files are treated as encoded in UTF-8. In that encoding, characters of most languages in the world can be used simultaneously in string literals, identifiers and comments — although the standard library only uses ASCII characters for identifiers, a convention that any portable code should follow. To display all these characters properly, your editor must recognize that the file is UTF-8, and it must use a font that supports all the characters in the file.
To declare an encoding other than the default one, a special comment line should be added as the first line of the file. The syntax is as follows:
# -*- coding: encoding -*-
where encoding is one of the valid codecs supported by Python.
For example, to declare that Windows-1252 encoding is to be used, the first line of your source code file should be:
# -*- coding: cp-1252 -*-
One exception to the first line rule is when the source code starts with a UNIX “shebang” line. In this case, the encoding declaration should be added as the second line of the file. For example:
#!/usr/bin/env python
# -*- coding: cp-1252 -*-



3. An Informal Introduction to Python



In the following examples, input and output are distinguished by the presence or absence of prompts (>>> and ...): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command.



Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, #, and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples.



Some examples:






# this is the first comment
spam = 1  # and this is the second comment
          # ... and now a third!
text = "# This is not a comment because it's inside quotes."












3.1. Using Python as a Calculator



Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>>. (It shouldn’t take long.)







3.1.1. Numbers



The interpreter acts as a simple calculator: you can type an expression at it and it will write the value. Expression syntax is straightforward: the operators +, -, * and / work just like in most other languages (for example, Pascal or C); parentheses (()) can be used for grouping. For example:





>>>


>>> 2 + 2
4
>>> 50 - 5*6
20
>>> (50 - 5.0*6) / 4
5.0
>>> 8 / 5.0
1.6








The integer numbers (e.g. 2, 4, 20) have type int, the ones with a fractional part (e.g. 5.0, 1.6) have type float. We will see more about numeric types later in the tutorial.



The return type of a division (/) operation depends on its operands. If both operands are of type int, floor division is performed and an int is returned. If either operand is a float, classic division is performed and a float is returned. The // operator is also provided for doing floor division no matter what the operands are. The remainder can be calculated with the % operator:





>>>


>>> 17 / 3  # int / int -> int
5
>>> 17 / 3.0  # int / float -> float
5.666666666666667
>>> 17 // 3.0  # explicit floor division discards the fractional part
5.0
>>> 17 % 3  # the % operator returns the remainder of the division
2
>>> 5 * 3 + 2  # result * divisor + remainder
17








With Python, it is possible to use the ** operator to calculate powers [1]:





>>>


>>> 5 ** 2  # 5 squared
25
>>> 2 ** 7  # 2 to the power of 7
128








The equal sign (=) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt:





>>>


>>> width = 20
>>> height = 5 * 9
>>> width * height
900








If a variable is not “defined” (assigned a value), trying to use it will give you an error:





>>>


>>> n  # try to access an undefined variable
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'n' is not defined








There is full support for floating point; operators with mixed type operands convert the integer operand to floating point:





>>>


>>> 3 * 3.75 / 1.5
7.5
>>> 7.0 / 2
3.5








In interactive mode, the last printed expression is assigned to the variable _. This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example:





>>>


>>> tax = 12.5 / 100
>>> price = 100.50
>>> price * tax
12.5625
>>> price + _
113.0625
>>> round(_, 2)
113.06








This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior.



In addition to int and float, Python supports other types of numbers, such as Decimal and Fraction. Python also has built-in support for complex numbers, and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j).









3.1.2. Strings



Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes ('...') or double quotes ("...") with the same result [2]. \ can be used to escape quotes:





>>>


>>> 'spam eggs'  # single quotes
'spam eggs'
>>> 'doesn\'t'  # use \' to escape the single quote...
"doesn't"
>>> "doesn't"  # ...or use double quotes instead
"doesn't"
>>> '"Yes," he said.'
'"Yes," he said.'
>>> "\"Yes,\" he said."
'"Yes," he said.'
>>> '"Isn\'t," she said.'
'"Isn\'t," she said.'








In the interactive interpreter, the output string is enclosed in quotes and special characters are escaped with backslashes. While this might sometimes look different from the input (the enclosing quotes could change), the two strings are equivalent. The string is enclosed in double quotes if the string contains a single quote and no double quotes, otherwise it is enclosed in single quotes. The print statement produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters:





>>>


>>> '"Isn\'t," she said.'
'"Isn\'t," she said.'
>>> print '"Isn\'t," she said.'
"Isn't," she said.
>>> s = 'First line.\nSecond line.'  # \n means newline
>>> s  # without print, \n is included in the output
'First line.\nSecond line.'
>>> print s  # with print, \n produces a new line
First line.
Second line.








If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote:





>>>


>>> print 'C:\some\name'  # here \n means newline!
C:\some
ame
>>> print r'C:\some\name'  # note the r before the quote
C:\some\name








String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...'''. End of lines are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. The following example:






print """\
Usage: thingy [OPTIONS]
     -h                        Display this usage message
     -H hostname               Hostname to connect to
"""








produces the following output (note that the initial newline is not included):






Usage: thingy [OPTIONS]
     -h                        Display this usage message
     -H hostname               Hostname to connect to








Strings can be concatenated (glued together) with the + operator, and repeated with *:





>>>


>>> # 3 times 'un', followed by 'ium'
>>> 3 * 'un' + 'ium'
'unununium'








Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated.





>>>


>>> 'Py' 'thon'
'Python'








This only works with two literals though, not with variables or expressions:





>>>


>>> prefix = 'Py'
>>> prefix 'thon'  # can't concatenate a variable and a string literal
  ...
SyntaxError: invalid syntax
>>> ('un' * 3) 'ium'
  ...
SyntaxError: invalid syntax








If you want to concatenate variables or a variable and a literal, use +:





>>>


>>> prefix + 'thon'
'Python'








This feature is particularly useful when you want to break long strings:





>>>


>>> text = ('Put several strings within parentheses '
...         'to have them joined together.')
>>> text
'Put several strings within parentheses to have them joined together.'








Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one:





>>>


>>> word = 'Python'
>>> word[0]  # character in position 0
'P'
>>> word[5]  # character in position 5
'n'








Indices may also be negative numbers, to start counting from the right:





>>>


>>> word[-1]  # last character
'n'
>>> word[-2]  # second-last character
'o'
>>> word[-6]
'P'








Note that since -0 is the same as 0, negative indices start from -1.



In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring:





>>>


>>> word[0:2]  # characters from position 0 (included) to 2 (excluded)
'Py'
>>> word[2:5]  # characters from position 2 (included) to 5 (excluded)
'tho'








Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s:





>>>


>>> word[:2] + word[2:]
'Python'
>>> word[:4] + word[4:]
'Python'








Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced.





>>>


>>> word[:2]   # character from the beginning to position 2 (excluded)
'Py'
>>> word[4:]   # characters from position 4 (included) to the end
'on'
>>> word[-2:]  # characters from the second-last (included) to the end
'on'








One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n, for example:






 +---+---+---+---+---+---+
 | P | y | t | h | o | n |
 +---+---+---+---+---+---+
 0   1   2   3   4   5   6
-6  -5  -4  -3  -2  -1








The first row of numbers gives the position of the indices 0...6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j, respectively.



For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2.



Attempting to use an index that is too large will result in an error:





>>>


>>> word[42]  # the word only has 6 characters
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: string index out of range








However, out of range slice indexes are handled gracefully when used for slicing:





>>>


>>> word[4:42]
'on'
>>> word[42:]
''








Python strings cannot be changed — they are immutable. Therefore, assigning to an indexed position in the string results in an error:





>>>


>>> word[0] = 'J'
  ...
TypeError: 'str' object does not support item assignment
>>> word[2:] = 'py'
  ...
TypeError: 'str' object does not support item assignment








If you need a different string, you should create a new one:





>>>


>>> 'J' + word[1:]
'Jython'
>>> word[:2] + 'py'
'Pypy'








The built-in function len() returns the length of a string:





>>>


>>> s = 'supercalifragilisticexpialidocious'
>>> len(s)
34










See also




Sequence Types — str, unicode, list, tuple, bytearray, buffer, xrange


Strings, and the Unicode strings described in the next section, are examples of sequence types, and support the common operations supported by such types.


String Methods


Both strings and Unicode strings support a large number of methods for basic transformations and searching.


Format String Syntax


Information about string formatting with str.format().


String Formatting Operations


The old formatting operations invoked when strings and Unicode strings are the left operand of the % operator are described in more detail here.











3.1.3. Unicode Strings



Starting with Python 2.0 a new data type for storing text data is available to the programmer: the Unicode object. It can be used to store and manipulate Unicode data (see http://www.unicode.org/) and integrates well with the existing string objects, providing auto-conversions where necessary.



Unicode has the advantage of providing one ordinal for every character in every script used in modern and ancient texts. Previously, there were only 256 possible ordinals for script characters. Texts were typically bound to a code page which mapped the ordinals to script characters. This lead to very much confusion especially with respect to internationalization (usually written as i18n — 'i' + 18 characters + 'n') of software. Unicode solves these problems by defining one code page for all scripts.



Creating Unicode strings in Python is just as simple as creating normal strings:





>>>


>>> u'Hello World !'
u'Hello World !'








The small 'u' in front of the quote indicates that a Unicode string is supposed to be created. If you want to include special characters in the string, you can do so by using the Python Unicode-Escape encoding. The following example shows how:





>>>


>>> u'Hello\u0020World !'
u'Hello World !'








The escape sequence \u0020 indicates to insert the Unicode character with the ordinal value 0x0020 (the space character) at the given position.



Other characters are interpreted by using their respective ordinal values directly as Unicode ordinals. If you have literal strings in the standard Latin-1 encoding that is used in many Western countries, you will find it convenient that the lower 256 characters of Unicode are the same as the 256 characters of Latin-1.



For experts, there is also a raw mode just like the one for normal strings. You have to prefix the opening quote with ‘ur’ to have Python use the Raw-Unicode-Escape encoding. It will only apply the above \uXXXX conversion if there is an uneven number of backslashes in front of the small ‘u’.





>>>


>>> ur'Hello\u0020World !'
u'Hello World !'
>>> ur'Hello\\u0020World !'
u'Hello\\\\u0020World !'








The raw mode is most useful when you have to enter lots of backslashes, as can be necessary in regular expressions.



Apart from these standard encodings, Python provides a whole set of other ways of creating Unicode strings on the basis of a known encoding.



The built-in function unicode() provides access to all registered Unicode codecs (COders and DECoders). Some of the more well known encodings which these codecs can convert are Latin-1, ASCII, UTF-8, and UTF-16. The latter two are variable-length encodings that store each Unicode character in one or more bytes. The default encoding is normally set to ASCII, which passes through characters in the range 0 to 127 and rejects any other characters with an error. When a Unicode string is printed, written to a file, or converted with str(), conversion takes place using this default encoding.





>>>


>>> u"abc"
u'abc'
>>> str(u"abc")
'abc'
>>> u"äöü"
u'\xe4\xf6\xfc'
>>> str(u"äöü")
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-2: ordinal not in range(128)








To convert a Unicode string into an 8-bit string using a specific encoding, Unicode objects provide an encode() method that takes one argument, the name of the encoding. Lowercase names for encodings are preferred.





>>>


>>> u"äöü".encode('utf-8')
'\xc3\xa4\xc3\xb6\xc3\xbc'








If you have data in a specific encoding and want to produce a corresponding Unicode string from it, you can use the unicode() function with the encoding name as the second argument.





>>>


>>> unicode('\xc3\xa4\xc3\xb6\xc3\xbc', 'utf-8')
u'\xe4\xf6\xfc'














3.1.4. Lists



Python knows a number of compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type.





>>>


>>> squares = [1, 4, 9, 16, 25]
>>> squares
[1, 4, 9, 16, 25]








Like strings (and all other built-in sequence type), lists can be indexed and sliced:





>>>


>>> squares[0]  # indexing returns the item
1
>>> squares[-1]
25
>>> squares[-3:]  # slicing returns a new list
[9, 16, 25]








All slice operations return a new list containing the requested elements. This means that the following slice returns a new (shallow) copy of the list:





>>>


>>> squares[:]
[1, 4, 9, 16, 25]








Lists also supports operations like concatenation:





>>>


>>> squares + [36, 49, 64, 81, 100]
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]








Unlike strings, which are immutable, lists are a mutable type, i.e. it is possible to change their content:





>>>


>>> cubes = [1, 8, 27, 65, 125]  # something's wrong here
>>> 4 ** 3  # the cube of 4 is 64, not 65!
64
>>> cubes[3] = 64  # replace the wrong value
>>> cubes
[1, 8, 27, 64, 125]








You can also add new items at the end of the list, by using the append() method (we will see more about methods later):





>>>


>>> cubes.append(216)  # add the cube of 6
>>> cubes.append(7 ** 3)  # and the cube of 7
>>> cubes
[1, 8, 27, 64, 125, 216, 343]








Assignment to slices is also possible, and this can even change the size of the list or clear it entirely:





>>>


>>> letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> letters
['a', 'b', 'c', 'd', 'e', 'f', 'g']
>>> # replace some values
>>> letters[2:5] = ['C', 'D', 'E']
>>> letters
['a', 'b', 'C', 'D', 'E', 'f', 'g']
>>> # now remove them
>>> letters[2:5] = []
>>> letters
['a', 'b', 'f', 'g']
>>> # clear the list by replacing all the elements with an empty list
>>> letters[:] = []
>>> letters
[]








The built-in function len() also applies to lists:





>>>


>>> letters = ['a', 'b', 'c', 'd']
>>> len(letters)
4








It is possible to nest lists (create lists containing other lists), for example:





>>>


>>> a = ['a', 'b', 'c']
>>> n = [1, 2, 3]
>>> x = [a, n]
>>> x
[['a', 'b', 'c'], [1, 2, 3]]
>>> x[0]
['a', 'b', 'c']
>>> x[0][1]
'b'
















3.2. First Steps Towards Programming



Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows:





>>>


>>> # Fibonacci series:
... # the sum of two elements defines the next
... a, b = 0, 1
>>> while b < 10:
...     print b
...     a, b = b, a+b
...
1
1
2
3
5
8








This example introduces several new features.


    

  • 
The first line contains a multiple assignment: the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right.
    

    • 

  • 
The while loop executes as long as the condition (here: b < 10) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), ==(equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to).
    

    • 

  • 
The body of the loop is indented: indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount.
    

    • 

  • 
The print statement writes the value of the expression(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple expressions and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this:
    

    

    
>>>
    

    >>> i = 256*256
>>> print 'The value of i is', i
The value of i is 65536

    

    

    

    
A trailing comma avoids the newline after the output:
    

    

    
>>>
    

    >>> a, b = 0, 1
>>> while b < 1000:
...     print b,
...     a, b = b, a+b
...
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987

    

    

    

    
Note that the interpreter inserts a newline before it prints the next prompt if the last line was not completed.
    

    • 




Footnotes




[1]Since ** has higher precedence than -, -3**2 will be interpreted as -(3**2) and thus result in -9. To avoid this and get 9, you can use (-3)**2.






[2]Unlike other languages, special characters such as \n have the same meaning with both single ('...') and double ("...") quotes. The only difference between the two is that within single quotes you don’t need to escape " (but you have to escape \') and vice versa.





4. More Control Flow Tools



Besides the while statement just introduced, Python knows the usual control flow statements known from other languages, with some twists.







4.1. if Statements



Perhaps the most well-known statement type is the if statement. For example:





>>>


>>> x = int(raw_input("Please enter an integer: "))
Please enter an integer: 42
>>> if x < 0:
...     x = 0
...     print 'Negative changed to zero'
... elif x == 0:
...     print 'Zero'
... elif x == 1:
...     print 'Single'
... else:
...     print 'More'
...
More








There can be zero or more elif parts, and the else part is optional. The keyword ‘elif‘ is short for ‘else if’, and is useful to avoid excessive indentation. An if ... elif ... elif ... sequence is a substitute for the switch or case statements found in other languages.









4.2. for Statements



The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):





>>>


>>> # Measure some strings:
... words = ['cat', 'window', 'defenestrate']
>>> for w in words:
...     print w, len(w)
...
cat 3
window 6
defenestrate 12








If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy. Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:





>>>


>>> for w in words[:]:  # Loop over a slice copy of the entire list.
...     if len(w) > 6:
...         words.insert(0, w)
...
>>> words
['defenestrate', 'cat', 'window', 'defenestrate']














4.3. The range() Function



If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates lists containing arithmetic progressions:





>>>


>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]








The given end point is never part of the generated list; range(10) generates a list of 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):





>>>


>>> range(5, 10)
[5, 6, 7, 8, 9]
>>> range(0, 10, 3)
[0, 3, 6, 9]
>>> range(-10, -100, -30)
[-10, -40, -70]








To iterate over the indices of a sequence, you can combine range() and len() as follows:





>>>


>>> a = ['Mary', 'had', 'a', 'little', 'lamb']
>>> for i in range(len(a)):
...     print i, a[i]
...
0 Mary
1 had
2 a
3 little
4 lamb








In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques.









4.4. break and continue Statements, and else Clauses on Loops



The break statement, like in C, breaks out of the smallest enclosing for or while loop.



Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the list (with for) or when the condition becomes false (with while), but not when the loop is terminated by a break statement. This is exemplified by the following loop, which searches for prime numbers:





>>>


>>> for n in range(2, 10):
...     for x in range(2, n):
...         if n % x == 0:
...             print n, 'equals', x, '*', n/x
...             break
...     else:
...         # loop fell through without finding a factor
...         print n, 'is a prime number'
...
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3








(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)



When used with a loop, the else clause has more in common with the else clause of a try statement than it does that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, seeHandling Exceptions.



The continue statement, also borrowed from C, continues with the next iteration of the loop:





>>>


>>> for num in range(2, 10):
...     if num % 2 == 0:
...         print "Found an even number", num
...         continue
...     print "Found a number", num
Found an even number 2
Found a number 3
Found an even number 4
Found a number 5
Found an even number 6
Found a number 7
Found an even number 8
Found a number 9














4.5. pass Statements



The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:





>>>


>>> while True:
...     pass  # Busy-wait for keyboard interrupt (Ctrl+C)
...








This is commonly used for creating minimal classes:





>>>


>>> class MyEmptyClass:
...     pass
...








Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:





>>>


>>> def initlog(*args):
...     pass   # Remember to implement this!
...














4.6. Defining Functions



We can create a function that writes the Fibonacci series to an arbitrary boundary:





>>>


>>> def fib(n):    # write Fibonacci series up to n
...     """Print a Fibonacci series up to n."""
...     a, b = 0, 1
...     while a < n:
...         print a,
...         a, b = b, a+b
...
>>> # Now call the function we just defined:
... fib(2000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597








The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.



The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings.) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.



The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables cannot be directly assigned a value within a function (unless named in a global statement), although they may be referenced.



The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). [1] When a function calls another function, a new local symbol table is created for that call.



A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function. This serves as a general renaming mechanism:





>>>


>>> fib
<function fib at 10042ed0>
>>> f = fib
>>> f(100)
0 1 1 2 3 5 8 13 21 34 55 89








Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without areturn statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print:





>>>


>>> fib(0)
>>> print fib(0)
None








It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:





>>>


>>> def fib2(n):  # return Fibonacci series up to n
...     """Return a list containing the Fibonacci series up to n."""
...     result = []
...     a, b = 0, 1
...     while a < n:
...         result.append(a)    # see below
...         a, b = b, a+b
...     return result
...
>>> f100 = fib2(100)    # call it
>>> f100                # write the result
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]








This example, as usual, demonstrates some new Python features:


    

  • The return statement returns with a value from a function. return without an expression argument returns None. Falling off the end of a function also returns None.
    • 

  • The statement result.append(a) calls a method of the list object result. A method is a function that ‘belongs’ to an object and is namedobj.methodname, where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a], but more efficient.
    • 










4.7. More on Defining Functions



It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.







4.7.1. Default Argument Values



The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:






def ask_ok(prompt, retries=4, complaint='Yes or no, please!'):
    while True:
        ok = raw_input(prompt)
        if ok in ('y', 'ye', 'yes'):
            return True
        if ok in ('n', 'no', 'nop', 'nope'):
            return False
        retries = retries - 1
        if retries < 0:
            raise IOError('refusenik user')
        print complaint








This function can be called in several ways:


    

  • giving only the mandatory argument: ask_ok('Do you really want to quit?')
    • 

  • giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2)
    • 

  • or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')
    • 




This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.



The default values are evaluated at the point of function definition in the defining scope, so that






i = 5

def f(arg=i):
    print arg

i = 6
f()








will print 5.



Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls:






def f(a, L=[]):
    L.append(a)
    return L

print f(1)
print f(2)
print f(3)








This will print






[1]
[1, 2]
[1, 2, 3]








If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:






def f(a, L=None):
    if L is None:
        L = []
    L.append(a)
    return L














4.7.2. Keyword Arguments



Functions can also be called using keyword arguments of the form kwarg=value. For instance, the following function:






def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
    print "-- This parrot wouldn't", action,
    print "if you put", voltage, "volts through it."
    print "-- Lovely plumage, the", type
    print "-- It's", state, "!"








accepts one required argument (voltage) and three optional arguments (state, action, and type). This function can be called in any of the following ways:






parrot(1000)                                          # 1 positional argument
parrot(voltage=1000)                                  # 1 keyword argument
parrot(voltage=1000000, action='VOOOOOM')             # 2 keyword arguments
parrot(action='VOOOOOM', voltage=1000000)             # 2 keyword arguments
parrot('a million', 'bereft of life', 'jump')         # 3 positional arguments
parrot('a thousand', state='pushing up the daisies')  # 1 positional, 1 keyword








but all the following calls would be invalid:






parrot()                     # required argument missing
parrot(voltage=5.0, 'dead')  # non-keyword argument after a keyword argument
parrot(110, voltage=220)     # duplicate value for the same argument
parrot(actor='John Cleese')  # unknown keyword argument








In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:





>>>


>>> def function(a):
...     pass
...
>>> function(0, a=0)
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
TypeError: function() got multiple values for keyword argument 'a'








When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (*name must occur before **name.) For example, if we define a function like this:






def cheeseshop(kind, *arguments, **keywords):
    print "-- Do you have any", kind, "?"
    print "-- I'm sorry, we're all out of", kind
    for arg in arguments:
        print arg
    print "-" * 40
    keys = sorted(keywords.keys())
    for kw in keys:
        print kw, ":", keywords[kw]








It could be called like this:






cheeseshop("Limburger", "It's very runny, sir.",
           "It's really very, VERY runny, sir.",
           shopkeeper='Michael Palin',
           client="John Cleese",
           sketch="Cheese Shop Sketch")








and of course it would print:






-- Do you have any Limburger ?
-- I'm sorry, we're all out of Limburger
It's very runny, sir.
It's really very, VERY runny, sir.
----------------------------------------
client : John Cleese
shopkeeper : Michael Palin
sketch : Cheese Shop Sketch








Note that the list of keyword argument names is created by sorting the result of the keywords dictionary’s keys() method before printing its contents; if this is not done, the order in which the arguments are printed is undefined.









4.7.3. Arbitrary Argument Lists



Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences). Before the variable number of arguments, zero or more normal arguments may occur.






def write_multiple_items(file, separator, *args):
    file.write(separator.join(args))














4.7.4. Unpacking Argument Lists



The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the *-operator to unpack the arguments out of a list or tuple:





>>>


>>> range(3, 6)             # normal call with separate arguments
[3, 4, 5]
>>> args = [3, 6]
>>> range(*args)            # call with arguments unpacked from a list
[3, 4, 5]








In the same fashion, dictionaries can deliver keyword arguments with the **-operator:





>>>


>>> def parrot(voltage, state='a stiff', action='voom'):
...     print "-- This parrot wouldn't", action,
...     print "if you put", voltage, "volts through it.",
...     print "E's", state, "!"
...
>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
>>> parrot(**d)
-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !














4.7.5. Lambda Expressions



Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b. Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:





>>>


>>> def make_incrementor(n):
...     return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43








The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:





>>>


>>> pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
>>> pairs.sort(key=lambda pair: pair[1])
>>> pairs
[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]














4.7.6. Documentation Strings



There are emerging conventions about the content and formatting of documentation strings.



The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.



If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.



The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).



Here is an example of a multi-line docstring:





>>>


>>> def my_function():
...     """Do nothing, but document it.
...
...     No, really, it doesn't do anything.
...     """
...     pass
...
>>> print my_function.__doc__
Do nothing, but document it.

    No, really, it doesn't do anything.
















4.8. Intermezzo: Coding Style



Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.



For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:


    

  • 
Use 4-space indentation, and no tabs.
    

    
4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.
    

    • 

  • 
Wrap lines so that they don’t exceed 79 characters.
    

    
This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.
    

    • 

  • 
Use blank lines to separate functions and classes, and larger blocks of code inside functions.
    

    • 

  • 
When possible, put comments on a line of their own.
    

    • 

  • 
Use docstrings.
    

    • 

  • 
Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4).
    

    • 

  • 
Name your classes and functions consistently; the convention is to use CamelCase for classes and lower_case_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).
    

    • 

  • 
Don’t use fancy encodings if your code is meant to be used in international environments. Plain ASCII works best in any case.
    

    • 




Footnotes




[1]Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list).







5. Data Structures



This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.







5.1. More on Lists



The list data type has some more methods. Here are all of the methods of list objects:




list.append(x)



Add an item to the end of the list; equivalent to a[len(a):] = [x].






list.extend(L)



Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.






list.insert(i, x)



Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).






list.remove(x)



Remove the first item from the list whose value is x. It is an error if there is no such item.






list.pop([i])



Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)






list.index(x)



Return the index in the list of the first item whose value is x. It is an error if there is no such item.






list.count(x)



Return the number of times x appears in the list.






list.sort(cmp=None, key=None, reverse=False)



Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).






list.reverse()



Reverse the elements of the list, in place.





An example that uses most of the list methods:





>>>


>>> a = [66.25, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.25), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.25, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
>>> a.remove(333)
>>> a
[66.25, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.25]
>>> a.sort()
>>> a
[-1, 1, 66.25, 333, 333, 1234.5]
>>> a.pop()
1234.5
>>> a
[-1, 1, 66.25, 333, 333]








You might have noticed that methods like insert, remove or sort that only modify the list have no return value printed – they return the default None. This is a design principle for all mutable data structures in Python.







5.1.1. Using Lists as Stacks



The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:





>>>


>>> stack = [3, 4, 5]
>>> stack.append(6)
>>> stack.append(7)
>>> stack
[3, 4, 5, 6, 7]
>>> stack.pop()
7
>>> stack
[3, 4, 5, 6]
>>> stack.pop()
6
>>> stack.pop()
5
>>> stack
[3, 4]














5.1.2. Using Lists as Queues



It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).



To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:





>>>


>>> from collections import deque
>>> queue = deque(["Eric", "John", "Michael"])
>>> queue.append("Terry")           # Terry arrives
>>> queue.append("Graham")          # Graham arrives
>>> queue.popleft()                 # The first to arrive now leaves
'Eric'
>>> queue.popleft()                 # The second to arrive now leaves
'John'
>>> queue                           # Remaining queue in order of arrival
deque(['Michael', 'Terry', 'Graham'])














5.1.3. Functional Programming Tools



There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().



filter(function, sequence) returns a sequence consisting of those items from the sequence for which function(item) is true. If sequence is a str, unicodeor tuple, the result will be of the same type; otherwise, it is always a list. For example, to compute a sequence of numbers divisible by 3 or 5:





>>>


>>> def f(x): return x % 3 == 0 or x % 5 == 0
...
>>> filter(f, range(2, 25))
[3, 5, 6, 9, 10, 12, 15, 18, 20, 21, 24]








map(function, sequence) calls function(item) for each of the sequence’s items and returns a list of the return values. For example, to compute some cubes:





>>>


>>> def cube(x): return x*x*x
...
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]








More than one sequence may be passed; the function must then have as many arguments as there are sequences and is called with the corresponding item from each sequence (or None if some sequence is shorter than another). For example:





>>>


>>> seq = range(8)
>>> def add(x, y): return x+y
...
>>> map(add, seq, seq)
[0, 2, 4, 6, 8, 10, 12, 14]








reduce(function, sequence) returns a single value constructed by calling the binary function function on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:





>>>


>>> def add(x,y): return x+y
...
>>> reduce(add, range(1, 11))
55








If there’s only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.



A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,





>>>


>>> def sum(seq):
...     def add(x,y): return x+y
...     return reduce(add, seq, 0)
...
>>> sum(range(1, 11))
55
>>> sum([])
0








Don’t use this example’s definition of sum(): since summing numbers is such a common need, a built-in function sum(sequence) is already provided, and works exactly like this.









5.1.4. List Comprehensions



List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.



For example, assume we want to create a list of squares, like:





>>>


>>> squares = []
>>> for x in range(10):
...     squares.append(x**2)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]








We can obtain the same result with:






squares = [x**2 for x in range(10)]








This is also equivalent to squares = map(lambda x: x**2, range(10)), but it’s more concise and readable.



A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:





>>>


>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]








and it’s equivalent to:





>>>


>>> combs = []
>>> for x in [1,2,3]:
...     for y in [3,1,4]:
...         if x != y:
...             combs.append((x, y))
...
>>> combs
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]








Note how the order of the for and if statements is the same in both these snippets.



If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.





>>>


>>> vec = [-4, -2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[-8, -4, 0, 4, 8]
>>> # filter the list to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> # apply a function to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create a list of 2-tuples like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> # the tuple must be parenthesized, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
  File "<stdin>", line 1
    [x, x**2 for x in range(6)]
               ^
SyntaxError: invalid syntax
>>> # flatten a list using a listcomp with two 'for'
>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]








List comprehensions can contain complex expressions and nested functions:





>>>


>>> from math import pi
>>> [str(round(pi, i)) for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']










5.1.4.1. Nested List Comprehensions



The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.



Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:





>>>


>>> matrix = [
...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12],
... ]








The following list comprehension will transpose rows and columns:





>>>


>>> [[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]








As we saw in the previous section, the nested listcomp is evaluated in the context of the for that follows it, so this example is equivalent to:





>>>


>>> transposed = []
>>> for i in range(4):
...     transposed.append([row[i] for row in matrix])
...
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]








which, in turn, is the same as:





>>>


>>> transposed = []
>>> for i in range(4):
...     # the following 3 lines implement the nested listcomp
...     transposed_row = []
...     for row in matrix:
...         transposed_row.append(row[i])
...     transposed.append(transposed_row)
...
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]








In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:





>>>


>>> zip(*matrix)
[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]








See Unpacking Argument Lists for details on the asterisk in this line.













5.2. The del statement



There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:





>>>


>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> del a[:]
>>> a
[]








del can also be used to delete entire variables:





>>>


>>> del a








Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.









5.3. Tuples and Sequences



We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — str, unicode, list, tuple, bytearray, buffer, xrange). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.



A tuple consists of a number of values separated by commas, for instance:





>>>


>>> t = 12345, 54321, 'hello!'
>>> t[0]
12345
>>> t
(12345, 54321, 'hello!')
>>> # Tuples may be nested:
... u = t, (1, 2, 3, 4, 5)
>>> u
((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
>>> # Tuples are immutable:
... t[0] = 88888
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
>>> # but they can contain mutable objects:
... v = ([1, 2, 3], [3, 2, 1])
>>> v
([1, 2, 3], [3, 2, 1])








As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.



Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable, and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples). Lists are mutable, and their elements are usually homogeneous and are accessed by iterating over the list.



A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:





>>>


>>> empty = ()
>>> singleton = 'hello',    # <-- note trailing comma
>>> len(empty)
0
>>> len(singleton)
1
>>> singleton
('hello',)








The statement t = 12345, 54321, 'hello!' is an example of tuple packing: the values 12345, 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:





>>>


>>> x, y, z = t








This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires the list of variables on the left to have the same number of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.









5.4. Sets



Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.



Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set(), not {}; the latter creates an empty dictionary, a data structure that we discuss in the next section.



Here is a brief demonstration:





>>>


>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> fruit = set(basket)               # create a set without duplicates
>>> fruit
set(['orange', 'pear', 'apple', 'banana'])
>>> 'orange' in fruit                 # fast membership testing
True
>>> 'crabgrass' in fruit
False

>>> # Demonstrate set operations on unique letters from two words
...
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a                                  # unique letters in a
set(['a', 'r', 'b', 'c', 'd'])
>>> a - b                              # letters in a but not in b
set(['r', 'd', 'b'])
>>> a | b                              # letters in either a or b
set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
>>> a & b                              # letters in both a and b
set(['a', 'c'])
>>> a ^ b                              # letters in a or b but not both
set(['r', 'd', 'b', 'm', 'z', 'l'])








Similarly to list comprehensions, set comprehensions are also supported:





>>>


>>> a = {x for x in 'abracadabra' if x not in 'abc'}
>>> a
set(['r', 'd'])














5.5. Dictionaries



Another useful data type built into Python is the dictionary (see Mapping Types — dict). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().



It is best to think of a dictionary as an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.



The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.



The keys() method of a dictionary object returns a list of all the keys used in the dictionary, in arbitrary order (if you want it sorted, just apply the sorted()function to it). To check whether a single key is in the dictionary, use the in keyword.



Here is a small example using a dictionary:





>>>


>>> tel = {'jack': 4098, 'sape': 4139}
>>> tel['guido'] = 4127
>>> tel
{'sape': 4139, 'guido': 4127, 'jack': 4098}
>>> tel['jack']
4098
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'guido': 4127, 'irv': 4127, 'jack': 4098}
>>> tel.keys()
['guido', 'irv', 'jack']
>>> 'guido' in tel
True








The dict() constructor builds dictionaries directly from sequences of key-value pairs:





>>>


>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}








In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:





>>>


>>> {x: x**2 for x in (2, 4, 6)}
{2: 4, 4: 16, 6: 36}








When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:





>>>


>>> dict(sape=4139, guido=4127, jack=4098)
{'sape': 4139, 'jack': 4098, 'guido': 4127}














5.6. Looping Techniques



When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.





>>>


>>> for i, v in enumerate(['tic', 'tac', 'toe']):
...     print i, v
...
0 tic
1 tac
2 toe








To loop over two or more sequences at the same time, the entries can be paired with the zip() function.





>>>


>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in zip(questions, answers):
...     print 'What is your {0}?  It is {1}.'.format(q, a)
...
What is your name?  It is lancelot.
What is your quest?  It is the holy grail.
What is your favorite color?  It is blue.








To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.





>>>


>>> for i in reversed(xrange(1,10,2)):
...     print i
...
9
7
5
3
1








To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.





>>>


>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
...     print f
...
apple
banana
orange
pear








When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the iteritems() method.





>>>


>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.iteritems():
...     print k, v
...
gallahad the pure
robin the brave








It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.





>>>


>>> import math
>>> raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
>>> filtered_data = []
>>> for value in raw_data:
...     if not math.isnan(value):
...         filtered_data.append(value)
...
>>> filtered_data
[56.2, 51.7, 55.3, 52.5, 47.8]














5.7. More on Conditions



The conditions used in while and if statements can contain any operators, not just comparisons.



The comparison operators in and not in check whether a value occurs (does not occur) in a sequence. The operators is and is not compare whether two objects are really the same object; this only matters for mutable objects like lists. All comparison operators have the same priority, which is lower than that of all numerical operators.



Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c.



Comparisons may be combined using the Boolean operators and and or, and the outcome of a comparison (or of any other Boolean expression) may be negated with not. These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and notB or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.



The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.



It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,





>>>


>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
>>> non_null = string1 or string2 or string3
>>> non_null
'Trondheim'








Note that in Python, unlike C, assignment cannot occur inside expressions. C programmers may grumble about this, but it avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.









5.8. Comparing Sequences and Other Types



Sequence objects may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII ordering for individual characters. Some examples of comparisons between sequences of the same type:






(1, 2, 3)              < (1, 2, 4)
[1, 2, 3]              < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4)           < (1, 2, 4)
(1, 2)                 < (1, 2, -1)
(1, 2, 3)             == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab'))   < (1, 2, ('abc', 'a'), 4)








Note that comparing objects of different types is legal. The outcome is deterministic but arbitrary: the types are ordered by their name. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. [1] Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.



Footnotes




[1]The rules for comparing objects of different types should not be relied upon; they may change in a future version of the language.











Thanks 


                                                                  Nymgo


                                        

















































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