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<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /><title>Chapter. func</title><link rel="stylesheet" href="/review/support/styles.css" type="text/css" /><meta name="generator" content="DocBook XSL Stylesheets V1.74.3" /><link rel="shortcut icon" type="image/png" href="/review/support/figs/favicon.png" /><script type="text/javascript" src="/review/support/jquery-min.js"></script><script type="text/javascript" src="/review/support/form.js"></script><script type="text/javascript" src="/review/support/hsbook.js"></script></head> |
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<body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><div class="chapter" id="ch5func"> |
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<div class="titlepage"></div> |
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<div class="toc"> |
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<p><b>Table of Contents</b></p> |
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<dl> |
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<dt><span class="article"><a href="#id2803272">Functional Approach</a></span></dt> |
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<dd><dl> |
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<dt><span class="section"><a href="#id2854310">1. Function scope</a></span></dt> |
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<dt><span class="section"><a href="#id2854368">2. Default Arguments</a></span></dt> |
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<dt><span class="section"><a href="#id2854408">3. Keyword Arguments</a></span></dt> |
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<dt><span class="section"><a href="#id2854524">4. Parameter Packing and Unpacking</a></span></dt> |
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<dt><span class="section"><a href="#id2854624">5. Nested Functions and Scopes</a></span></dt> |
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<dt><span class="section"><a href="#id2854673">6. map, reduce and filter functions</a></span></dt> |
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<dd><dl><dt><span class="section"><a href="#id2854863">6.1. List Comprehensions</a></span></dt></dl></dd> |
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</dl></dd> |
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</dl> |
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</div> |
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<div class="article" title="Functional Approach"> |
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<div class="titlepage"> |
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<div><div><h2 class="title"> |
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<a name="id2803272"></a>Functional Approach</h2></div></div> |
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<hr /> |
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</div> |
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<div class="toc"> |
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<p><b>Table of Contents</b></p> |
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<dl> |
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<dt><span class="section"><a href="#id2854310">1. Function scope</a></span></dt> |
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<dt><span class="section"><a href="#id2854368">2. Default Arguments</a></span></dt> |
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<dt><span class="section"><a href="#id2854408">3. Keyword Arguments</a></span></dt> |
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<dt><span class="section"><a href="#id2854524">4. Parameter Packing and Unpacking</a></span></dt> |
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<dt><span class="section"><a href="#id2854624">5. Nested Functions and Scopes</a></span></dt> |
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<dt><span class="section"><a href="#id2854673">6. map, reduce and filter functions</a></span></dt> |
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<dd><dl><dt><span class="section"><a href="#id2854863">6.1. List Comprehensions</a></span></dt></dl></dd> |
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</dl> |
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</div> |
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<p id="ch5func_1"><span class="emphasis"><em>Functions</em></span> allow us to enclose a set of statements and call the function again |
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and again instead of repeating the group of statements everytime. Functions also |
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allow us to isolate a piece of code from all the other code and provides the |
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convenience of not polluting the global variables.</p> |
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<p id="ch5func_2"><span class="emphasis"><em>Function</em></span> in python is defined with the keyword <span class="strong"><strong>def</strong></span> followed by the name |
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of the function, in turn followed by a pair of parenthesis which encloses the |
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list of parameters to the function. The definition line ends with a ':'. The |
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definition line is followed by the body of the function intended by one block. |
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The <span class="emphasis"><em>Function</em></span> must return a value:</p> |
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<pre class="programlisting"> |
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def factorial(n): |
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fact = 1 |
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for i in range(2, n): |
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fact *= i |
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return fact</pre> |
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<p id="ch5func_3">The code snippet above defines a function with the name factorial, takes the |
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number for which the factorial must be computed, computes the factorial and |
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returns the value.</p> |
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<p id="ch5func_4">A <span class="emphasis"><em>Function</em></span> once defined can be used or called anywhere else in the program. We |
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call a fucntion with its name followed by a pair of parenthesis which encloses |
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the arguments to the function.</p> |
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<p id="ch5func_5">The value that function returns can be assigned to a variable. Let's call the |
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above function and store the factorial in a variable:</p> |
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<pre class="programlisting"> |
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fact5 = factorial(5)</pre> |
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<p id="ch5func_6">The value of fact5 will now be 120, which is the factorial of 5. Note that we |
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passed 5 as the argument to the function.</p> |
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<p id="ch5func_7">It may be necessary to document what the function does, for each of the function |
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to help the person who reads our code to understand it better. In order to do |
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this Python allows the first line of the function body to be a string. This |
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string is called as <span class="emphasis"><em>Documentation String</em></span> or <span class="emphasis"><em>docstring</em></span>. <span class="emphasis"><em>docstrings</em></span> prove |
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to be very handy since there are number of tools which can pull out all the |
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docstrings from Python functions and generate the documentation automatically |
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from it. <span class="emphasis"><em>docstrings</em></span> for functions can be written as follows:</p> |
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<pre class="programlisting"> |
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def factorial(n): |
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'Returns the factorial for the number n.' |
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fact = 1 |
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for i in range(2, n): |
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fact *= i |
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return fact</pre> |
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<p id="ch5func_8">An important point to note at this point is that, a function can return any |
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Python value or a Python object, which also includes a <span class="emphasis"><em>Tuple</em></span>. A <span class="emphasis"><em>Tuple</em></span> is |
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just a collection of values and those values themselves can be of any other |
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valid Python datatypes, including <span class="emphasis"><em>Lists</em></span>, <span class="emphasis"><em>Tuples</em></span>, <span class="emphasis"><em>Dictionaries</em></span> among other |
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things. So effectively, if a function can return a tuple, it can return any |
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number of values through a tuple</p> |
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<p id="ch5func_9">Let us write a small function to swap two values:</p> |
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<pre class="programlisting"> |
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def swap(a, b): |
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return b, a |
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c, d = swap(a, b)</pre> |
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<div class="section" title="1.Function scope"> |
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<div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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<a name="id2854310"></a>1.Function scope</h2></div></div></div> |
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<p id="ch5func_a">The variables used inside the function are confined to the function's scope |
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and doesn't pollute the variables of the same name outside the scope of the |
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function. Also the arguments passed to the function are passed by-value if |
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it is of basic Python data type:</p> |
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<pre class="programlisting"> |
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def cant_change(n): |
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n = 10 |
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n = 5 |
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cant_change(n)</pre> |
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<p id="ch5func_b">Upon running this code, what do you think would have happened to value of n |
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which was assigned 5 before the function call? If you have already tried out |
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that snippet on the interpreter you already know that the value of n is not |
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changed. This is true of any immutable types of Python like <span class="emphasis"><em>Numbers</em></span>, <span class="emphasis"><em>Strings</em></span> |
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and <span class="emphasis"><em>Tuples</em></span>. But when you pass mutable objects like <span class="emphasis"><em>Lists</em></span> and <span class="emphasis"><em>Dictionaries</em></span> |
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the values are manipulated even outside the function:</p> |
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<pre class="programlisting"> |
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>>> def can_change(n): |
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... n[1] = James |
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... |
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>>> name = ['Mr.', 'Steve', 'Gosling'] |
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>>> can_change(name) |
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>>> name |
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['Mr.', 'James', 'Gosling']</pre> |
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<p id="ch5func_c">If nothing is returned by the function explicitly, Python takes care to return |
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None when the funnction is called.</p> |
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</div> |
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<div class="section" title="2.Default Arguments"> |
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<div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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<a name="id2854368"></a>2.Default Arguments</h2></div></div></div> |
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<p id="ch5func_d">There may be situations where we need to allow the functions to take the |
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arguments optionally. Python allows us to define function this way by providing |
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a facility called <span class="emphasis"><em>Default Arguments</em></span>. For example, we need to write a function |
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that returns a list of fibonacci numbers. Since our function cannot generate an |
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infinite list of fibonacci numbers, we need to specify the number of elements |
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that the fibonacci sequence must contain. Suppose, additionally, we want to the |
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function to return 10 numbers in the sequence if no option is specified we can |
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define the function as follows:</p> |
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<pre class="programlisting"> |
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def fib(n=10): |
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fib_list = [0, 1] |
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for i in range(n - 2): |
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next = fib_list[-2] + fib_list[-1] |
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fib_list.append(next) |
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return fib_list</pre> |
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<p id="ch5func_e">When we call this function, we can optionally specify the value for the |
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parameter n, during the call as an argument. Calling with no argument and |
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argument with n=5 returns the following fibonacci sequences:</p> |
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<pre class="programlisting"> |
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fib() |
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[0, 1, 1, 2, 3, 5, 8, 13, 21, 34] |
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fib(5) |
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[0, 1, 1, 2, 3]</pre> |
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</div> |
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<div class="section" title="3.Keyword Arguments"> |
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<div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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<a name="id2854408"></a>3.Keyword Arguments</h2></div></div></div> |
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<p id="ch5func_f">When a function takes a large number of arguments, it may be difficult to |
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remember the order of the parameters in the function definition or it may |
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be necessary to pass values to only certain parameters since others take |
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the default value. In either of these cases, Python provides the facility |
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of passing arguments by specifying the name of the parameter as defined in |
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the function definition. This is known as <span class="emphasis"><em>Keyword Arguments</em></span>.</p> |
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<p id="ch5func_10">In a function call, <span class="emphasis"><em>Keyword arguments</em></span> can be used for each argument, in the |
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following fashion:</p> |
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<pre class="programlisting"> |
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argument_name=argument_value |
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Also denoted as: keyword=argument |
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def wish(name='World', greetings='Hello'): |
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print "%s, %s!" % (greetings, name)</pre> |
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<p id="ch5func_11">This function can be called in one of the following ways. It is important to |
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note that no restriction is imposed in the order in which <span class="emphasis"><em>Keyword arguments</em></span> |
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can be specified. Also note, that we have combined <span class="emphasis"><em>Keyword arguments</em></span> with |
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<span class="emphasis"><em>Default arguments</em></span> in this example, however it is not necessary:</p> |
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<pre class="programlisting"> |
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wish(name='Guido', greetings='Hey') |
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wish(greetings='Hey', name='Guido')</pre> |
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<p id="ch5func_12">Calling functions by specifying arguments in the order of parameters specified |
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in the function definition is called as <span class="emphasis"><em>Positional arguments</em></span>, as opposed to |
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<span class="emphasis"><em>Keyword arguments</em></span>. It is possible to use both <span class="emphasis"><em>Positional arguments</em></span> and |
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<span class="emphasis"><em>Keyword arguments</em></span> in a single function call. But Python doesn't allow us to |
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bungle up both of them. The arguments to the function, in the call, must always |
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start with <span class="emphasis"><em>Positional arguments</em></span> which is in turn followed by <span class="emphasis"><em>Keyword |
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arguments</em></span>:</p> |
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<pre class="programlisting"> |
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def my_func(x, y, z, u, v, w): |
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# initialize variables. |
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... |
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# do some stuff |
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... |
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# return the value</pre> |
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<p id="ch5func_13">It is valid to call the above functions in the following ways:</p> |
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<pre class="programlisting"> |
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my_func(10, 20, 30, u=1.0, v=2.0, w=3.0) |
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my_func(10, 20, 30, 1.0, 2.0, w=3.0) |
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my_func(10, 20, z=30, u=1.0, v=2.0, w=3.0) |
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my_func(x=10, y=20, z=30, u=1.0, v=2.0, w=3.0)</pre> |
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<p id="ch5func_14">Following lists some of the invalid calls:</p> |
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<pre class="programlisting"> |
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my_func(10, 20, z=30, 1.0, 2.0, 3.0) |
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my_func(x=10, 20, z=30, 1.0, 2.0, 3.0) |
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my_func(x=10, y=20, z=30, u=1.0, v=2.0, 3.0)</pre> |
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</div> |
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<div class="section" title="4.Parameter Packing and Unpacking"> |
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<div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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<a name="id2854524"></a>4.Parameter Packing and Unpacking</h2></div></div></div> |
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<p id="ch5func_15">The positional arguments passed to a function can be collected in a tuple |
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parameter and keyword arguments can be collected in a dictionary. Since keyword |
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arguments must always be the last set of arguments passed to a function, the |
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keyword dictionary parameter must be the last parameter. The function definition |
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must include a list explicit parameters, followed by tuple paramter collecting |
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parameter, whose name is preceded by a <span class="strong"><strong>*</strong></span>, for collecting positional |
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parameters, in turn followed by the dictionary collecting parameter, whose name |
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is preceded by a <span class="strong"><strong>**</strong></span></p> |
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<pre class="programlisting"> |
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def print_report(title, *args, **name): |
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"""Structure of *args* |
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(age, email-id) |
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Structure of *name* |
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{
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'first': First Name |
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'middle': Middle Name |
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'last': Last Name |
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} |
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""" |
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|
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print "Title: %s" % (title) |
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print "Full name: %(first)s %(middle)s %(last)s" % name |
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print "Age: %d nEmail-ID: %s" % args</pre> |
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<p id="ch5func_16">The above function can be called as. Note, the order of keyword parameters can |
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be interchanged:</p> |
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<pre class="programlisting"> |
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>>> print_report('Employee Report', 29, 'johny@example.com', first='Johny',
|
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last='Charles', middle='Douglas') |
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Title: Employee Report |
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Full name: Johny Douglas Charles |
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Age: 29 |
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Email-ID: johny@example.com</pre> |
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<p id="ch5func_17">The reverse of this can also be achieved by using a very identical syntax while |
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calling the function. A tuple or a dictionary can be passed as arguments in |
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place of a list of <span class="emphasis"><em>Positional arguments</em></span> or <span class="emphasis"><em>Keyword arguments</em></span> respectively |
239 |
using <span class="strong"><strong>*</strong></span> or <span class="strong"><strong>**</strong></span></p> |
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<pre class="programlisting"> |
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def print_report(title, age, email, first, middle, last): |
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print "Title: %s" % (title) |
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print "Full name: %s %s %s" % (first, middle, last) |
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print "Age: %d nEmail-ID: %s" % (age, email) |
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|
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>>> args = (29, 'johny@example.com') |
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>>> name = {
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'first': 'Johny', |
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'middle': 'Charles', |
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'last': 'Douglas' |
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} |
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>>> print_report('Employee Report', *args, **name)
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Title: Employee Report |
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Full name: Johny Charles Douglas |
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Age: 29 |
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Email-ID: johny@example.com</pre> |
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</div> |
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<div class="section" title="5.Nested Functions and Scopes"> |
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<div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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<a name="id2854624"></a>5.Nested Functions and Scopes</h2></div></div></div> |
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<p id="ch5func_18">Python allows nesting one function inside another. This style of programming |
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turns out to be extremely flexible and powerful features when we use <span class="emphasis"><em>Python |
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decorators</em></span>. We will not talk about decorators is beyond the scope of this |
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course. If you are interested in knowing more about <span class="emphasis"><em>decorator programming</em></span> in |
|
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Python you are suggested to read:</p> |
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<div class="reference"> |
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<div class="titlepage"><hr /></div>http://avinashv.net/2008/04/python-decorators-syntactic-sugar/</div> |
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<div class="reference"> |
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<div class="titlepage"><hr /></div>http://personalpages.tds.net/~kent37/kk/00001.html</div> |
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<p id="ch5func_19">However, the following is an example for nested functions in Python:</p> |
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<pre class="programlisting"> |
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def outer(): |
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print "Outer..." |
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def inner(): |
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print "Inner..." |
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print "Outer..." |
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inner() |
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>>> outer()</pre> |
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</div> |
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<div class="section" title="6.map, reduce and filter functions"> |
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<div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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<a name="id2854673"></a>6.map, reduce and filter functions</h2></div></div></div> |
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<p id="ch5func_1a">Python provides several built-in functions for convenience. The <span class="strong"><strong>map()</strong></span>, |
|
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<span class="strong"><strong>reduce()</strong></span> and <span class="strong"><strong>filter()</strong></span> functions prove to be very useful with sequences like |
|
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<span class="emphasis"><em>Lists</em></span>.</p> |
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<p id="ch5func_1b">The <span class="strong"><strong>map</strong></span> (<span class="emphasis"><em>function</em></span>, <span class="emphasis"><em>sequence</em></span>) function takes two arguments: <span class="emphasis"><em>function</em></span> |
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and a <span class="emphasis"><em>sequence</em></span> argument. The <span class="emphasis"><em>function</em></span> argument must be the name of the |
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function which in turn takes a single argument, the individual element of the |
| 28 | 290 |
<span class="emphasis"><em>sequence</em></span>. The <span class="strong"><strong>map</strong></span> function calls <span class="emphasis"><em>function(item)</em></span>, for each item in the |
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sequence and returns a list of values, where each value is the value returned |
| 28 | 292 |
by each call to <span class="emphasis"><em>function(item)</em></span>. <span class="strong"><strong>map()</strong></span> function allows to pass more than |
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one sequence. In this case, the first argument, <span class="emphasis"><em>function</em></span> must take as many |
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arguments as the number of sequences passed. This function is called with each |
| 28 | 295 |
corresponding element in the each of the sequences, or <span class="strong"><strong>None</strong></span> if one of the |
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sequence is exhausted:</p> |
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<pre class="programlisting"> |
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def square(x): |
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return x*x |
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|
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>>> map(square, [1, 2, 3, 4]) |
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[1, 4, 9, 16] |
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def mul(x, y): |
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return x*y |
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>>> map(mul, [1, 2, 3, 4], [6, 7, 8, 9])</pre> |
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<p id="ch5func_1c">The <span class="strong"><strong>filter</strong></span> (<span class="emphasis"><em>function</em></span>, <span class="emphasis"><em>sequence</em></span>) function takes two arguments, similar to |
|
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the <span class="strong"><strong>map()</strong></span> function. The <span class="strong"><strong>filter</strong></span> function calls <span class="emphasis"><em>function(item)</em></span>, for each |
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item in the sequence and returns all the elements in the sequence for which |
| 28 | 311 |
<span class="emphasis"><em>function(item)</em></span> returned True:</p> |
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<pre class="programlisting"> |
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def even(x): |
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if x % 2: |
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return True |
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else: |
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return False |
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|
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>>> filter(even, range(1, 10)) |
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[1, 3, 5, 7, 9]</pre> |
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<p id="ch5func_1d">The <span class="strong"><strong>reduce</strong></span> (<span class="emphasis"><em>function</em></span>, <span class="emphasis"><em>sequence</em></span>) function takes two arguments, similar to |
|
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<span class="strong"><strong>map</strong></span> function, however multiple sequences are not allowed. The <span class="strong"><strong>reduce</strong></span> |
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function calls <span class="emphasis"><em>function</em></span> with first two consecutive elements in the sequence, |
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obtains the result, calls <span class="emphasis"><em>function</em></span> with the result and the subsequent element |
|
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in the sequence and so on until the end of the list and returns the final result:</p> |
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<pre class="programlisting"> |
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def mul(x, y): |
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return x*y |
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|
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>>> reduce(mul, [1, 2, 3, 4]) |
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24</pre> |
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<div class="section" title="6.1.List Comprehensions"> |
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<div class="titlepage"><div><div><h3 class="title"> |
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<a name="id2854863"></a>6.1.List Comprehensions</h3></div></div></div> |
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<p id="ch5func_1e">List Comprehension is a convenvience utility provided by Python. It is a |
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syntatic sugar to create <span class="emphasis"><em>Lists</em></span>. Using <span class="emphasis"><em>List Comprehensions</em></span> one can create |
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<span class="emphasis"><em>Lists</em></span> from other type of sequential data structures or other <span class="emphasis"><em>Lists</em></span> itself. |
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The syntax of <span class="emphasis"><em>List Comprehensions</em></span> consists of a square brackets to indicate |
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the result is a <span class="emphasis"><em>List</em></span> within which we include at least one <span class="strong"><strong>for</strong></span> clause and |
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multiple <span class="strong"><strong>if</strong></span> clauses. It will be more clear with an example:</p> |
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<pre class="programlisting"> |
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>>> num = [1, 2, 3] |
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>>> sq = [x*x for x in num] |
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>>> sq |
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[1, 4, 9] |
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>>> all_num = [1, 2, 3, 4, 5, 6, 7, 8, 9] |
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>>> even = [x for x in all_num if x%2 == 0]</pre> |
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<p id="ch5func_1f">The syntax used here is very clear from the way it is written. It can be |
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translated into english as, "for each element x in the list all_num, |
| 28 | 350 |
if remainder of x divided by 2 is 0, add x to the list."</p> |
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</div> |
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</div> |
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</div> |
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</div></body> |
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</html> |
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