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<div class="section" id="the-goodies">
<h1>The Goodies<a class="headerlink" href="#the-goodies" title="Permalink to this headline">¶</a></h1>
<p>One of my goals for this book has been to teach you as little Python as
possible. When there were two ways to do something, I picked one and
avoided mentioning the other. Or sometimes I put the second one into an
exercise.</p>
<p>Now I want to go back for some of the good bits that got left behind.
Python provides a number of features that are not really necessary—you
can write good code without them—but with them you can sometimes write
code that’s more concise, readable or efficient, and sometimes all
three.</p>
<div class="section" id="conditional-expressions">
<h2>Conditional expressions<a class="headerlink" href="#conditional-expressions" title="Permalink to this headline">¶</a></h2>
<p>We saw conditional statements in Section [conditional.execution].
Conditional statements are often used to choose one of two values; for
example:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">y</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s">'nan'</span><span class="p">)</span>
</pre></div>
</div>
<p>This statement checks whether x is positive. If so, it computes
math.log. If not, math.log would raise a ValueError. To avoid stopping
the program, we generate a “NaN”, which is a special floating-point
value that represents “Not a Number”.</p>
<p>We can write this statement more concisely using a <strong>conditional
expression</strong>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">y</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">float</span><span class="p">(</span><span class="s">'nan'</span><span class="p">)</span>
</pre></div>
</div>
<p>You can almost read this line like English: “y gets log-x if x is
greater than 0; otherwise it gets NaN”.</p>
<p>Recursive functions can sometimes be rewritten using conditional
expressions. For example, here is a recursive version of factorial:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">factorial</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="n">factorial</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>We can rewrite it like this:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">factorial</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">n</span> <span class="o">*</span> <span class="n">factorial</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>Another use of conditional expressions is handling optional arguments.
For example, here is the init method from GoodKangaroo (see
Exercise [kangaroo]):</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">contents</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
<span class="k">if</span> <span class="n">contents</span> <span class="o">==</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">contents</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pouch_contents</span> <span class="o">=</span> <span class="n">contents</span>
</pre></div>
</div>
<p>We can rewrite this one like this:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">contents</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pouch_contents</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="n">contents</span> <span class="o">==</span> <span class="bp">None</span> <span class="k">else</span> <span class="n">contents</span>
</pre></div>
</div>
<p>In general, you can replace a conditional statement with a conditional
expression if both branches contain simple expressions that are either
returned or assigned to the same variable.</p>
</div>
<div class="section" id="list-comprehensions">
<h2>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">¶</a></h2>
<p>In Section [filter] we saw the map and filter patterns. For example,
this function takes a list of strings, maps the string method capitalize
to the elements, and returns a new list of strings:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">capitalize_all</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">t</span><span class="p">:</span>
<span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">capitalize</span><span class="p">())</span>
<span class="k">return</span> <span class="n">res</span>
</pre></div>
</div>
<p>We can write this more concisely using a <strong>list comprehension</strong>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">capitalize_all</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">capitalize</span><span class="p">()</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">t</span><span class="p">]</span>
</pre></div>
</div>
<p>The bracket operators indicate that we are constructing a new list. The
expression inside the brackets specifies the elements of the list, and
the for clause indicates what sequence we are traversing.</p>
<p>The syntax of a list comprehension is a little awkward because the loop
variable, s in this example, appears in the expression before we get to
the definition.</p>
<p>List comprehensions can also be used for filtering. For example, this
function selects only the elements of t that are upper case, and returns
a new list:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">only_upper</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">t</span><span class="p">:</span>
<span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">isupper</span><span class="p">():</span>
<span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
</pre></div>
</div>
<p>We can rewrite it using a list comprehension</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">only_upper</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">t</span> <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">isupper</span><span class="p">()]</span>
</pre></div>
</div>
<p>List comprehensions are concise and easy to read, at least for simple
expressions. And they are usually faster than the equivalent for loops,
sometimes much faster. So if you are mad at me for not mentioning them
earlier, I understand.</p>
<p>But, in my defense, list comprehensions are harder to debug because you
can’t put a print statement inside the loop. I suggest that you use them
only if the computation is simple enough that you are likely to get it
right the first time. And for beginners that means never.</p>
</div>
<div class="section" id="generator-expressions">
<h2>Generator expressions<a class="headerlink" href="#generator-expressions" title="Permalink to this headline">¶</a></h2>
<p><strong>Generator expressions</strong> are similar to list comprehensions, but with
parentheses instead of square brackets:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">g</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">g</span>
<span class="go"><generator object <genexpr> at 0x7f4c45a786c0></span>
</pre></div>
</div>
<p>The result is a generator object that knows how to iterate through a
sequence of values. But unlike a list comprehension, it does not compute
the values all at once; it waits to be asked. The built-in function next
gets the next value from the generator:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="nb">next</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
<span class="go">0</span>
<span class="gp">>>> </span><span class="nb">next</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
<span class="go">1</span>
</pre></div>
</div>
<p>When you get to the end of the sequence, next raises a StopIteration
exception. You can also use a for loop to iterate through the values:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">g</span><span class="p">:</span>
<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
<span class="go">4</span>
<span class="go">9</span>
<span class="go">16</span>
</pre></div>
</div>
<p>The generator object keeps track of where it is in the sequence, so the
for loop picks up where next left off. Once the generator is exhausted,
it continues to raise StopException:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="nb">next</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
<span class="go">StopIteration</span>
</pre></div>
</div>
<p>Generator expressions are often used with functions like sum, max, and
min:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
<span class="go">30</span>
</pre></div>
</div>
</div>
<div class="section" id="any-and-all">
<h2>any and all<a class="headerlink" href="#any-and-all" title="Permalink to this headline">¶</a></h2>
<p>Python provides a built-in function, any, that takes a sequence of
boolean values and returns True if any of the values are True. It works
on lists:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="nb">any</span><span class="p">([</span><span class="bp">False</span><span class="p">,</span> <span class="bp">False</span><span class="p">,</span> <span class="bp">True</span><span class="p">])</span>
<span class="go">True</span>
</pre></div>
</div>
<p>But it is often used with generator expressions:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="nb">any</span><span class="p">(</span><span class="n">letter</span> <span class="o">==</span> <span class="s">'t'</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">'monty'</span><span class="p">)</span>
<span class="go">True</span>
</pre></div>
</div>
<p>That example isn’t very useful because it does the same thing as the in
operator. But we could use any to rewrite some of the search functions
we wrote in Section [search]. For example, we could write avoids like
this:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">avoids</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">forbidden</span><span class="p">):</span>
<span class="k">return</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="n">letter</span> <span class="ow">in</span> <span class="n">forbidden</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="n">word</span><span class="p">)</span>
</pre></div>
</div>
<p>The function almost reads like English, “word avoids forbidden if there
are not any forbidden letters in word.”</p>
<p>Using any with a generator expression is efficient because it stops
immediately if it finds a True value, so it doesn’t have to evaluate the
whole sequence.</p>
<p>Python provides another built-in function, all, that returns True if
every element of the sequence is True. As an exercise, use all to
re-write <code class="docutils literal"><span class="pre">uses_all</span></code> from Section [search].</p>
</div>
<div class="section" id="sets">
<h2>Sets<a class="headerlink" href="#sets" title="Permalink to this headline">¶</a></h2>
<p>In Section [dictsub] I use dictionaries to find the words that appear in
a document but not in a word list. The function I wrote takes d1, which
contains the words from the document as keys, and d2, which contains the
list of words. It returns a dictionary that contains the keys from d1
that are not in d2.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">subtract</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span> <span class="n">d2</span><span class="p">):</span>
<span class="n">res</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">d1</span><span class="p">:</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">d2</span><span class="p">:</span>
<span class="n">res</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">return</span> <span class="n">res</span>
</pre></div>
</div>
<p>In all of these dictionaries, the values are None because we never use
them. As a result, we waste some storage space.</p>
<p>Python provides another built-in type, called a set, that behaves like a
collection of dictionary keys with no values. Adding elements to a set
is fast; so is checking membership. And sets provide methods and
operators to compute common set operations.</p>
<p>For example, set subtraction is available as a method called difference
or as an operator, -. So we can rewrite subtract like this:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">subtract</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span> <span class="n">d2</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">set</span><span class="p">(</span><span class="n">d1</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">d2</span><span class="p">)</span>
</pre></div>
</div>
<p>The result is a set instead of a dictionary, but for operations like
iteration, the behavior is the same.</p>
<p>Some of the exercises in this book can be done concisely and efficiently
with sets. For example, here is a solution to <code class="docutils literal"><span class="pre">has_duplicates</span></code>, from
Exercise [duplicate], that uses a dictionary:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">has_duplicates</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">t</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">d</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="n">d</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="o">=</span> <span class="bp">True</span>
<span class="k">return</span> <span class="bp">False</span>
</pre></div>
</div>
<p>When an element appears for the first time, it is added to the
dictionary. If the same element appears again, the function returns
True.</p>
<p>Using sets, we can write the same function like this:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">has_duplicates</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">t</span><span class="p">))</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
</pre></div>
</div>
<p>An element can only appear in a set once, so if an element in t appears
more than once, the set will be smaller than t. If there are no
duplicates, the set will be the same size as t.</p>
<p>We can also use sets to do some of the exercises in Chapter [wordplay].
For example, here’s a version of <code class="docutils literal"><span class="pre">uses_only</span></code> with a loop:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">uses_only</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">available</span><span class="p">):</span>
<span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="n">word</span><span class="p">:</span>
<span class="k">if</span> <span class="n">letter</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">available</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">return</span> <span class="bp">True</span>
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">uses_only</span></code> checks whether all letters in word are in available. We
can rewrite it like this:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">uses_only</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">available</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">set</span><span class="p">(</span><span class="n">word</span><span class="p">)</span> <span class="o"><=</span> <span class="nb">set</span><span class="p">(</span><span class="n">available</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal"><span class="pre"><=</span></code> operator checks whether one set is a subset or another,
including the possibility that they are equal, which is true if all the
letters in word appear in available.</p>
<p>As an exercise, rewrite <code class="docutils literal"><span class="pre">avoids</span></code> using sets.</p>
</div>
<div class="section" id="counters">
<h2>Counters<a class="headerlink" href="#counters" title="Permalink to this headline">¶</a></h2>
<p>A Counter is like a set, except that if an element appears more than
once, the Counter keeps track of how many times it appears. If you are
familiar with the mathematical idea of a <strong>multiset</strong>, a Counter is a
natural way to represent a multiset.</p>
<p>Counter is defined in a standard module called collections, so you have
to import it. You can initialize a Counter with a string, list, or
anything else that supports iteration:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="gp">>>> </span><span class="n">count</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="s">'parrot'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">count</span>
<span class="go">Counter({'r': 2, 't': 1, 'o': 1, 'p': 1, 'a': 1})</span>
</pre></div>
</div>
<p>Counters behave like dictionaries in many ways; they map from each key
to the number of times it appears. As in dictionaries, the keys have to
be hashable.</p>
<p>Unlike dictionaries, Counters don’t raise an exception if you access an
element that doesn’t appear. Instead, they return 0:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">count</span><span class="p">[</span><span class="s">'d'</span><span class="p">]</span>
<span class="go">0</span>
</pre></div>
</div>
<p>We can use Counters to rewrite <code class="docutils literal"><span class="pre">is_anagram</span></code> from Exercise [anagram]:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">is_anagram</span><span class="p">(</span><span class="n">word1</span><span class="p">,</span> <span class="n">word2</span><span class="p">):</span>
<span class="k">return</span> <span class="n">Counter</span><span class="p">(</span><span class="n">word1</span><span class="p">)</span> <span class="o">==</span> <span class="n">Counter</span><span class="p">(</span><span class="n">word2</span><span class="p">)</span>
</pre></div>
</div>
<p>If two words are anagrams, they contain the same letters with the same
counts, so their Counters are equivalent.</p>
<p>Counters provide methods and operators to perform set-like operations,
including addition, subtraction, union and intersection. And they
provide an often-useful method, <code class="docutils literal"><span class="pre">most_common</span></code>, which returns a list of
value-frequency pairs, sorted from most common to least:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">count</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="s">'parrot'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">val</span><span class="p">,</span> <span class="n">freq</span> <span class="ow">in</span> <span class="n">count</span><span class="o">.</span><span class="n">most_common</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">freq</span><span class="p">)</span>
<span class="go">r 2</span>
<span class="go">p 1</span>
<span class="go">a 1</span>
</pre></div>
</div>
</div>
<div class="section" id="defaultdict">
<h2>defaultdict<a class="headerlink" href="#defaultdict" title="Permalink to this headline">¶</a></h2>
<p>The collections module also provides defaultdict, which is like a
dictionary except that if you access a key that doesn’t exist, it can
generate a new value on the fly.</p>
<p>When you create a defaultdict, you provide a function that’s used to
create new values. A function used to create objects is sometimes called
a <strong>factory</strong>. The built-in functions that create lists, sets, and other
types can be used as factories:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
</pre></div>
</div>
<p>Notice that the argument is list, which is a class object, not list(),
which is a new list. The function you provide doesn’t get called unless
you access a key that doesn’t exist.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="s">'new key'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">[]</span>
</pre></div>
</div>
<p>The new list, which we’re calling t, is also added to the dictionary. So
if we modify t, the change appears in d:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">t</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s">'new value'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">d</span>
<span class="go">defaultdict(<class 'list'>, {'new key': ['new value']})</span>
</pre></div>
</div>
<p>If you are making a dictionary of lists, you can often write simpler
code using defaultdict. In my solution to Exercise [anagrams], which you
can get from <a class="reference external" href="http://thinkpython2.com/code/anagram_sets.py">http://thinkpython2.com/code/anagram_sets.py</a>, I make a
dictionary that maps from a sorted string of letters to the list of
words that can be spelled with those letters. For example, ’opst’ maps
to the list .</p>
<p>Here’s the original code:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">all_anagrams</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">word</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">signature</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">d</span><span class="p">:</span>
<span class="n">d</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">word</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">d</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="k">return</span> <span class="n">d</span>
</pre></div>
</div>
<p>This can be simplified using setdefault, which you might have used in
Exercise [setdefault]:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">all_anagrams</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">word</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">signature</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="k">return</span> <span class="n">d</span>
</pre></div>
</div>
<p>This solution has the drawback that it makes a new list every time,
regardless of whether it is needed. For lists, that’s no big deal, but
if the factory function is complicated, it might be.</p>
<p>We can avoid this problem and simplify the code using a defaultdict:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">all_anagrams</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
<span class="n">word</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">signature</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="n">d</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="k">return</span> <span class="n">d</span>
</pre></div>
</div>
<p>My solution to Exercise [poker], which you can download from
<a class="reference external" href="http://thinkpython2.com/code/PokerHandSoln.py">http://thinkpython2.com/code/PokerHandSoln.py</a>, uses setdefault in the
function <code class="docutils literal"><span class="pre">has_straightflush</span></code>. This solution has the drawback of
creating a Hand object every time through the loop, whether it is needed
or not. As an exercise, rewrite it using a defaultdict.</p>
</div>
<div class="section" id="named-tuples">
<h2>Named tuples<a class="headerlink" href="#named-tuples" title="Permalink to this headline">¶</a></h2>
<p>Many simple objects are basically collections of related values. For
example, the Point object defined in Chapter [clobjects] contains two
numbers, x and y. When you define a class like this, you usually start
with an init method and a str method:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">class</span> <span class="nc">Point</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">y</span>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s">'(</span><span class="si">%g</span><span class="s">, </span><span class="si">%g</span><span class="s">)'</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p>This is a lot of code to convey a small amount of information. Python
provides a more concise way to say the same thing:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="n">Point</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s">'Point'</span><span class="p">,</span> <span class="p">[</span><span class="s">'x'</span><span class="p">,</span> <span class="s">'y'</span><span class="p">])</span>
</pre></div>
</div>
<p>The first argument is the name of the class you want to create. The
second is a list of the attributes Point objects should have, as
strings. The return value from namedtuple is a class object:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">Point</span>
<span class="go"><class '__main__.Point'></span>
</pre></div>
</div>
<p>Point automatically provides methods like <code class="docutils literal"><span class="pre">__init__</span></code> and <code class="docutils literal"><span class="pre">__str__</span></code>
so you don’t have to write them.</p>
<p>To create a Point object, you use the Point class as a function:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">p</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">p</span>
<span class="go">Point(x=1, y=2)</span>
</pre></div>
</div>
<p>The init method assigns the arguments to attributes using the names you
provided. The str method prints a representation of the Point object and
its attributes.</p>
<p>You can access the elements of the named tuple by name:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">p</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">y</span>
<span class="go">(1, 2)</span>
</pre></div>
</div>
<p>But you can also treat a named tuple as a tuple:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="go">(1, 2)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">p</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span>
<span class="go">(1, 2)</span>
</pre></div>
</div>
<p>Named tuples provide a quick way to define simple classes. The drawback
is that simple classes don’t always stay simple. You might decide later
that you want to add methods to a named tuple. In that case, you could
define a new class that inherits from the named tuple:</p>
<div class="highlight-python"><div class="highlight"><pre>class Pointier(Point):
# add more methods here
</pre></div>
</div>
<p>Or you could switch to a conventional class definition.</p>
</div>
<div class="section" id="gathering-keyword-args">
<h2>Gathering keyword args<a class="headerlink" href="#gathering-keyword-args" title="Permalink to this headline">¶</a></h2>
<p>In Section [gather], we saw how to write a function that gathers its
arguments into a tuple:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">printall</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
</pre></div>
</div>
<p>You can call this function with any number of positional arguments (that
is, arguments that don’t have keywords):</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">printall</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="s">'3'</span><span class="p">)</span>
<span class="go">(1, 2.0, '3')</span>
</pre></div>
</div>
<p>But the operator doesn’t gather keyword arguments:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">printall</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">third</span><span class="o">=</span><span class="s">'3'</span><span class="p">)</span>
<span class="go">TypeError: printall() got an unexpected keyword argument 'third'</span>
</pre></div>
</div>
<p>To gather keyword arguments, you can use the * operator:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">printall</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>You can call the keyword gathering parameter anything you want, but
kwargs is a common choice. The result is a dictionary that maps keywords
to values:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">printall</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">third</span><span class="o">=</span><span class="s">'3'</span><span class="p">)</span>
<span class="go">(1, 2.0) {'third': '3'}</span>
</pre></div>
</div>
<p>If you have a dictionary of keywords and values, you can use the scatter
operator, * to call a function:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">Point</span><span class="p">(</span><span class="o">**</span><span class="n">d</span><span class="p">)</span>
<span class="go">Point(x=1, y=2)</span>
</pre></div>
</div>
<p>Without the scatter operator, the function would treat d as a single
positional argument, so it would assign d to x and complain because
there’s nothing to assign to y:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">d</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">Point</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
File <span class="nb">"<stdin>"</span>, line <span class="m">1</span>, in <span class="n"><module></span>
<span class="gr">TypeError</span>: <span class="n">__new__() missing 1 required positional argument: 'y'</span>
</pre></div>
</div>
<p>When you are working with functions that have a large number of
parameters, it is often useful to create and pass around dictionaries
that specify frequently used options.</p>
</div>
<div class="section" id="glossary">
<span id="glossary19"></span><h2>Glossary<a class="headerlink" href="#glossary" title="Permalink to this headline">¶</a></h2>
<dl class="docutils">
<dt>expressão condicional (<em>conditional expression</em>)</dt>
<dd>An expression that has one of two values, depending on a condition.</dd>
<dt>listcomp (<em>list comprehension</em>)</dt>
<dd>An expression with a for loop in square brackets that yields a new list.</dd>
<dt>expressão geradora (<em>generator expression</em>)</dt>
<dd>An expression with a for loop in parentheses that yields a generator object.</dd>
<dt><em>multiset</em> (“multi-conjunto”)</dt>
<dd>A mathematical entity that represents a mapping between the elements of a set and the number of times they appear.</dd>
<dt><em>factory</em> (fábrica)</dt>
<dd>A function, usually passed as a parameter, used to create objects.</dd>
</dl>
</div>
<div class="section" id="exercises">
<h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this headline">¶</a></h2>
<p>The following is a function computes the binomial coefficient
recursively.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">def</span> <span class="nf">binomial_coeff</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="sd">"""Compute the binomial coefficient "n choose k".</span>
<span class="sd"> n: number of trials</span>
<span class="sd"> k: number of successes</span>
<span class="sd"> returns: int</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">binomial_coeff</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="o">+</span> <span class="n">binomial_coeff</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
</pre></div>
</div>
<p>Rewrite the body of the function using nested conditional expressions.</p>
<p>One note: this function is not very efficient because it ends up
computing the same values over and over. You could make it more
efficient by memoizing (see Section [memoize]). But you will find that
it’s harder to memoize if you write it using conditional expressions.</p>
</div>
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<h3><a href="index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">The Goodies</a><ul>
<li><a class="reference internal" href="#conditional-expressions">Conditional expressions</a></li>
<li><a class="reference internal" href="#list-comprehensions">List comprehensions</a></li>
<li><a class="reference internal" href="#generator-expressions">Generator expressions</a></li>
<li><a class="reference internal" href="#any-and-all">any and all</a></li>
<li><a class="reference internal" href="#sets">Sets</a></li>
<li><a class="reference internal" href="#counters">Counters</a></li>
<li><a class="reference internal" href="#defaultdict">defaultdict</a></li>
<li><a class="reference internal" href="#named-tuples">Named tuples</a></li>
<li><a class="reference internal" href="#gathering-keyword-args">Gathering keyword args</a></li>
<li><a class="reference internal" href="#glossary">Glossary</a></li>
<li><a class="reference internal" href="#exercises">Exercises</a></li>
</ul>
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