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8 <body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><div class="chapter" title="Chapter 1. "> |
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9 <div class="titlepage"></div> |
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10 <div class="toc"> |
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11 <p><b>Table of Contents</b></p> |
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12 <dl> |
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13 <dt><span class="article"><a href="#id2872631">Functional Approach</a></span></dt> |
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14 <dd><dl> |
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15 <dt><span class="section"><a href="#id2923650">1. Function scope</a></span></dt> |
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16 <dt><span class="section"><a href="#id2923702">2. Default Arguments</a></span></dt> |
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17 <dt><span class="section"><a href="#id2923738">3. Keyword Arguments</a></span></dt> |
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18 <dt><span class="section"><a href="#id2923841">4. Parameter Packing and Unpacking</a></span></dt> |
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19 <dt><span class="section"><a href="#id2923935">5. Nested Functions and Scopes</a></span></dt> |
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20 <dt><span class="section"><a href="#id2923984">6. map, reduce and filter functions</a></span></dt> |
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21 <dd><dl><dt><span class="section"><a href="#id2924165">6.1. List Comprehensions</a></span></dt></dl></dd> |
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22 </dl></dd> |
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23 </dl> |
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24 </div> |
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25 <div class="article" title="Functional Approach"> |
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26 <div class="titlepage"> |
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27 <div><div><h2 class="title"> |
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28 <a name="id2872631"></a>Functional Approach</h2></div></div> |
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29 <hr> |
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30 </div> |
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31 <div class="toc"> |
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32 <p><b>Table of Contents</b></p> |
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33 <dl> |
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34 <dt><span class="section"><a href="#id2923650">1. Function scope</a></span></dt> |
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35 <dt><span class="section"><a href="#id2923702">2. Default Arguments</a></span></dt> |
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36 <dt><span class="section"><a href="#id2923738">3. Keyword Arguments</a></span></dt> |
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37 <dt><span class="section"><a href="#id2923841">4. Parameter Packing and Unpacking</a></span></dt> |
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38 <dt><span class="section"><a href="#id2923935">5. Nested Functions and Scopes</a></span></dt> |
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39 <dt><span class="section"><a href="#id2923984">6. map, reduce and filter functions</a></span></dt> |
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40 <dd><dl><dt><span class="section"><a href="#id2924165">6.1. List Comprehensions</a></span></dt></dl></dd> |
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41 </dl> |
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42 </div> |
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43 <p><span class="emphasis"><em>Functions</em></span> allow us to enclose a set of statements and call the function again |
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44 and again instead of repeating the group of statements everytime. Functions also |
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45 allow us to isolate a piece of code from all the other code and provides the |
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46 convenience of not polluting the global variables.</p> |
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47 <p><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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48 of the function, in turn followed by a pair of parenthesis which encloses the |
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49 list of parameters to the function. The definition line ends with a ':'. The |
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50 definition line is followed by the body of the function intended by one block. |
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51 The <span class="emphasis"><em>Function</em></span> must return a value:</p> |
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52 <pre class="programlisting"> def factorial(n): |
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53 fact = 1 |
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54 for i in range(2, n): |
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55 fact *= i |
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56 |
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57 return fact</pre> |
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58 <p>The code snippet above defines a function with the name factorial, takes the |
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59 number for which the factorial must be computed, computes the factorial and |
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60 returns the value.</p> |
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61 <p>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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62 call a fucntion with its name followed by a pair of parenthesis which encloses |
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63 the arguments to the function.</p> |
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64 <p>The value that function returns can be assigned to a variable. Let's call the |
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65 above function and store the factorial in a variable:</p> |
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66 <pre class="programlisting"> fact5 = factorial(5)</pre> |
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67 <p>The value of fact5 will now be 120, which is the factorial of 5. Note that we |
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68 passed 5 as the argument to the function.</p> |
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69 <p>It may be necessary to document what the function does, for each of the function |
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70 to help the person who reads our code to understand it better. In order to do |
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71 this Python allows the first line of the function body to be a string. This |
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72 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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73 to be very handy since there are number of tools which can pull out all the |
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74 docstrings from Python functions and generate the documentation automatically |
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75 from it. <span class="emphasis"><em>docstrings</em></span> for functions can be written as follows:</p> |
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76 <pre class="programlisting"> def factorial(n): |
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77 'Returns the factorial for the number n.' |
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78 fact = 1 |
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79 for i in range(2, n): |
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80 fact *= i |
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81 |
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82 return fact</pre> |
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83 <p>An important point to note at this point is that, a function can return any |
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84 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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85 just a collection of values and those values themselves can be of any other |
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86 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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87 things. So effectively, if a function can return a tuple, it can return any |
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88 number of values through a tuple</p> |
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89 <p>Let us write a small function to swap two values:</p> |
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90 <pre class="programlisting"> def swap(a, b): |
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91 return b, a |
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92 |
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93 c, d = swap(a, b)</pre> |
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94 <div class="section" title="1. Function scope"> |
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95 <div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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96 <a name="id2923650"></a>1. Function scope</h2></div></div></div> |
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97 <p>The variables used inside the function are confined to the function's scope |
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98 and doesn't pollute the variables of the same name outside the scope of the |
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99 function. Also the arguments passed to the function are passed by-value if |
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100 it is of basic Python data type:</p> |
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101 <pre class="programlisting"> def cant_change(n): |
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102 n = 10 |
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103 |
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104 n = 5 |
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105 cant_change(n)</pre> |
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106 <p>Upon running this code, what do you think would have happened to value of n |
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107 which was assigned 5 before the function call? If you have already tried out |
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108 that snippet on the interpreter you already know that the value of n is not |
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109 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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110 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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111 the values are manipulated even outside the function:</p> |
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112 <pre class="programlisting"> >>> def can_change(n): |
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113 ... n[1] = James |
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114 ... |
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115 |
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116 >>> name = ['Mr.', 'Steve', 'Gosling'] |
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117 >>> can_change(name) |
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118 >>> name |
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119 ['Mr.', 'James', 'Gosling']</pre> |
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120 <p>If nothing is returned by the function explicitly, Python takes care to return |
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121 None when the funnction is called.</p> |
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122 </div> |
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123 <div class="section" title="2. Default Arguments"> |
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124 <div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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125 <a name="id2923702"></a>2. Default Arguments</h2></div></div></div> |
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126 <p>There may be situations where we need to allow the functions to take the |
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127 arguments optionally. Python allows us to define function this way by providing |
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128 a facility called <span class="emphasis"><em>Default Arguments</em></span>. For example, we need to write a function |
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129 that returns a list of fibonacci numbers. Since our function cannot generate an |
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130 infinite list of fibonacci numbers, we need to specify the number of elements |
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131 that the fibonacci sequence must contain. Suppose, additionally, we want to the |
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132 function to return 10 numbers in the sequence if no option is specified we can |
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133 define the function as follows:</p> |
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134 <pre class="programlisting"> def fib(n=10): |
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135 fib_list = [0, 1] |
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136 for i in range(n - 2): |
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137 next = fib_list[-2] + fib_list[-1] |
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138 fib_list.append(next) |
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139 return fib_list</pre> |
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140 <p>When we call this function, we can optionally specify the value for the |
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141 parameter n, during the call as an argument. Calling with no argument and |
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142 argument with n=5 returns the following fibonacci sequences:</p> |
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143 <pre class="programlisting"> fib() |
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144 [0, 1, 1, 2, 3, 5, 8, 13, 21, 34] |
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145 fib(5) |
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146 [0, 1, 1, 2, 3]</pre> |
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147 </div> |
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148 <div class="section" title="3. Keyword Arguments"> |
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149 <div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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150 <a name="id2923738"></a>3. Keyword Arguments</h2></div></div></div> |
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151 <p>When a function takes a large number of arguments, it may be difficult to |
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152 remember the order of the parameters in the function definition or it may |
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153 be necessary to pass values to only certain parameters since others take |
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154 the default value. In either of these cases, Python provides the facility |
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155 of passing arguments by specifying the name of the parameter as defined in |
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156 the function definition. This is known as <span class="emphasis"><em>Keyword Arguments</em></span>.</p> |
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157 <p>In a function call, <span class="emphasis"><em>Keyword arguments</em></span> can be used for each argument, in the |
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158 following fashion:</p> |
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159 <pre class="programlisting"> argument_name=argument_value |
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160 Also denoted as: keyword=argument |
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161 |
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162 def wish(name='World', greetings='Hello'): |
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163 print "%s, %s!" % (greetings, name)</pre> |
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164 <p>This function can be called in one of the following ways. It is important to |
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165 note that no restriction is imposed in the order in which <span class="emphasis"><em>Keyword arguments</em></span> |
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166 can be specified. Also note, that we have combined <span class="emphasis"><em>Keyword arguments</em></span> with |
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167 <span class="emphasis"><em>Default arguments</em></span> in this example, however it is not necessary:</p> |
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168 <pre class="programlisting"> wish(name='Guido', greetings='Hey') |
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169 wish(greetings='Hey', name='Guido')</pre> |
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170 <p>Calling functions by specifying arguments in the order of parameters specified |
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171 in the function definition is called as <span class="emphasis"><em>Positional arguments</em></span>, as opposed to |
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172 <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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173 <span class="emphasis"><em>Keyword arguments</em></span> in a single function call. But Python doesn't allow us to |
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174 bungle up both of them. The arguments to the function, in the call, must always |
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175 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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176 arguments</em></span>:</p> |
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177 <pre class="programlisting"> def my_func(x, y, z, u, v, w): |
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178 # initialize variables. |
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179 ... |
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180 # do some stuff |
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181 ... |
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182 # return the value</pre> |
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183 <p>It is valid to call the above functions in the following ways:</p> |
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184 <pre class="programlisting"> my_func(10, 20, 30, u=1.0, v=2.0, w=3.0) |
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185 my_func(10, 20, 30, 1.0, 2.0, w=3.0) |
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186 my_func(10, 20, z=30, u=1.0, v=2.0, w=3.0) |
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187 my_func(x=10, y=20, z=30, u=1.0, v=2.0, w=3.0)</pre> |
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188 <p>Following lists some of the invalid calls:</p> |
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189 <pre class="programlisting"> my_func(10, 20, z=30, 1.0, 2.0, 3.0) |
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190 my_func(x=10, 20, z=30, 1.0, 2.0, 3.0) |
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191 my_func(x=10, y=20, z=30, u=1.0, v=2.0, 3.0)</pre> |
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192 </div> |
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193 <div class="section" title="4. Parameter Packing and Unpacking"> |
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194 <div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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195 <a name="id2923841"></a>4. Parameter Packing and Unpacking</h2></div></div></div> |
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196 <p>The positional arguments passed to a function can be collected in a tuple |
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197 parameter and keyword arguments can be collected in a dictionary. Since keyword |
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198 arguments must always be the last set of arguments passed to a function, the |
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199 keyword dictionary parameter must be the last parameter. The function definition |
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200 must include a list explicit parameters, followed by tuple paramter collecting |
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201 parameter, whose name is preceded by a <span class="strong"><strong>*</strong></span>, for collecting positional |
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202 parameters, in turn followed by the dictionary collecting parameter, whose name |
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203 is preceded by a <span class="strong"><strong>**</strong></span></p> |
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204 <pre class="programlisting"> def print_report(title, *args, **name): |
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205 """Structure of *args* |
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206 (age, email-id) |
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207 Structure of *name* |
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208 { |
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209 'first': First Name |
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210 'middle': Middle Name |
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211 'last': Last Name |
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212 } |
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213 """ |
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214 |
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215 print "Title: %s" % (title) |
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216 print "Full name: %(first)s %(middle)s %(last)s" % name |
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217 print "Age: %d\nEmail-ID: %s" % args</pre> |
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218 <p>The above function can be called as. Note, the order of keyword parameters can |
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219 be interchanged:</p> |
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220 <pre class="programlisting"> >>> print_report('Employee Report', 29, 'johny@example.com', first='Johny', |
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221 last='Charles', middle='Douglas') |
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222 Title: Employee Report |
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223 Full name: Johny Douglas Charles |
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224 Age: 29 |
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225 Email-ID: johny@example.com</pre> |
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226 <p>The reverse of this can also be achieved by using a very identical syntax while |
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227 calling the function. A tuple or a dictionary can be passed as arguments in |
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228 place of a list of <span class="emphasis"><em>Positional arguments</em></span> or <span class="emphasis"><em>Keyword arguments</em></span> respectively |
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229 using <span class="strong"><strong>*</strong></span> or <span class="strong"><strong>**</strong></span></p> |
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230 <pre class="programlisting"> def print_report(title, age, email, first, middle, last): |
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231 print "Title: %s" % (title) |
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232 print "Full name: %s %s %s" % (first, middle, last) |
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233 print "Age: %d\nEmail-ID: %s" % (age, email) |
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234 |
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235 >>> args = (29, 'johny@example.com') |
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236 >>> name = { |
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237 'first': 'Johny', |
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238 'middle': 'Charles', |
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239 'last': 'Douglas' |
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240 } |
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241 >>> print_report('Employee Report', *args, **name) |
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242 Title: Employee Report |
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243 Full name: Johny Charles Douglas |
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244 Age: 29 |
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245 Email-ID: johny@example.com</pre> |
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246 </div> |
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247 <div class="section" title="5. Nested Functions and Scopes"> |
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248 <div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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249 <a name="id2923935"></a>5. Nested Functions and Scopes</h2></div></div></div> |
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250 <p>Python allows nesting one function inside another. This style of programming |
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251 turns out to be extremely flexible and powerful features when we use <span class="emphasis"><em>Python |
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252 decorators</em></span>. We will not talk about decorators is beyond the scope of this |
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253 course. If you are interested in knowing more about <span class="emphasis"><em>decorator programming</em></span> in |
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254 Python you are suggested to read:</p> |
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255 <span style="color: red"><line_block><span style="color: red"><line><div class="reference"> |
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256 <div class="titlepage"><hr></div>http://avinashv.net/2008/04/python-decorators-syntactic-sugar/</div></line></span><span style="color: red"><line><div class="reference"> |
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257 <div class="titlepage"><hr></div>http://personalpages.tds.net/~kent37/kk/00001.html</div></line></span></line_block></span><p>However, the following is an example for nested functions in Python:</p> |
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258 <pre class="programlisting"> def outer(): |
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259 print "Outer..." |
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260 def inner(): |
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261 print "Inner..." |
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262 print "Outer..." |
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263 inner() |
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264 |
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265 >>> outer()</pre> |
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266 </div> |
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267 <div class="section" title="6. map, reduce and filter functions"> |
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268 <div class="titlepage"><div><div><h2 class="title" style="clear: both"> |
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269 <a name="id2923984"></a>6. map, reduce and filter functions</h2></div></div></div> |
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270 <p>Python provides several built-in functions for convenience. The <span class="strong"><strong>map()</strong></span>, |
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271 <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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272 <span class="emphasis"><em>Lists</em></span>.</p> |
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273 <p>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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274 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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275 function which in turn takes a single argument, the individual element of the |
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276 <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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277 sequence and returns a list of values, where each value is the value returned |
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278 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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279 one sequence. In this case, the first argument, <span class="emphasis"><em>function</em></span> must take as many |
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280 arguments as the number of sequences passed. This function is called with each |
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281 corresponding element in the each of the sequences, or <span class="strong"><strong>None</strong></span> if one of the |
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282 sequence is exhausted:</p> |
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283 <pre class="programlisting"> def square(x): |
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284 return x*x |
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285 |
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286 >>> map(square, [1, 2, 3, 4]) |
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287 [1, 4, 9, 16] |
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288 |
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289 def mul(x, y): |
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290 return x*y |
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291 |
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292 >>> map(mul, [1, 2, 3, 4], [6, 7, 8, 9])</pre> |
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293 <p>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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294 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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295 item in the sequence and returns all the elements in the sequence for which |
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296 <span class="emphasis"><em>function(item)</em></span> returned True:</p> |
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297 <pre class="programlisting"> def even(x): |
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298 if x % 2: |
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299 return True |
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300 else: |
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301 return False |
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302 |
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303 >>> filter(even, range(1, 10)) |
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304 [1, 3, 5, 7, 9]</pre> |
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305 <p>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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306 <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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307 function calls <span class="emphasis"><em>function</em></span> with first two consecutive elements in the sequence, |
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308 obtains the result, calls <span class="emphasis"><em>function</em></span> with the result and the subsequent element |
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309 in the sequence and so on until the end of the list and returns the final result:</p> |
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310 <pre class="programlisting"> def mul(x, y): |
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311 return x*y |
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312 |
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313 >>> reduce(mul, [1, 2, 3, 4]) |
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314 24</pre> |
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315 <div class="section" title="6.1. List Comprehensions"> |
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316 <div class="titlepage"><div><div><h3 class="title"> |
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317 <a name="id2924165"></a>6.1. List Comprehensions</h3></div></div></div> |
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318 <p>List Comprehension is a convenvience utility provided by Python. It is a |
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319 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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320 <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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321 The syntax of <span class="emphasis"><em>List Comprehensions</em></span> consists of a square brackets to indicate |
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322 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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323 multiple <span class="strong"><strong>if</strong></span> clauses. It will be more clear with an example:</p> |
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324 <pre class="programlisting"> >>> num = [1, 2, 3] |
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325 >>> sq = [x*x for x in num] |
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326 >>> sq |
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327 [1, 4, 9] |
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328 >>> all_num = [1, 2, 3, 4, 5, 6, 7, 8, 9] |
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329 >>> even = [x for x in all_num if x%2 == 0]</pre> |
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330 <p>The syntax used here is very clear from the way it is written. It can be |
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331 translated into english as, "for each element x in the list all_num, |
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332 if remainder of x divided by 2 is 0, add x to the list."</p> |
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333 </div> |
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334 </div> |
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335 </div> |
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336 </div></body> |
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337 </html> |