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copied!<blockquote> <p>What functionality does functools.partial offer that you can't get through lambdas?</p> </blockquote> <p>Not much in terms of extra <em>functionality</em> (but, see later) -- and, readability is in the eye of the beholder.<br> Most people who are familiar with functional programming languages (those in the Lisp/Scheme families in particular) appear to like <code>lambda</code> just fine -- I say "most", definitely <em>not</em> all, because Guido and I assuredly are among those "familiar with" (etc) yet think of <code>lambda</code> as an eyesore anomaly in Python...<br> He was repentant of ever having accepted it into Python whereas planned to remove it from Python 3, as one of "Python's glitches".<br> I fully supported him in that. (I love <code>lambda</code> <em>in Scheme</em>... while its limitations <em>in Python</em>, and the weird way it <em>just doesn't fit in</em> with the rest of the language, make my skin crawl).</p> <p>Not so, however, for the hordes of <code>lambda</code> lovers -- who staged one of the closest things to a rebellion ever seen in Python's history, until Guido backtracked and decided to leave <code>lambda</code> in.<br> Several possible additions to <code>functools</code> (to make functions returning constants, identity, etc) didn't happen (to avoid explicitly duplicating more of <code>lambda</code>'s functionality), though <code>partial</code> did of course remain (it's no <em>total</em> duplication, nor is it an eyesore).</p> <p>Remember that <code>lambda</code>'s body is limited to be an <em>expression</em>, so it's got limitations. For example...:</p> <pre><code>>>> import functools >>> f = functools.partial(int, base=2) >>> f.args () >>> f.func <type 'int'> >>> f.keywords {'base': 2} >>> </code></pre> <p><code>functools.partial</code>'s returned function is decorated with attributes useful for introspection -- the function it's wrapping, and what positional and named arguments it fixes therein. Further, the named arguments can be overridden right back (the "fixing" is rather, in a sense, the setting of defaults):</p> <pre><code>>>> f('23', base=10) 23 </code></pre> <p>So, as you see, it's <em>definely</em> not as simplistic as <code>lambda s: int(s, base=2)</code>!-)</p> <p>Yes, you <em>could</em> contort your lambda to give you some of this -- e.g., for the keyword-overriding,</p> <pre><code>>>> f = lambda s, **k: int(s, **dict({'base': 2}, **k)) </code></pre> <p>but I dearly <strong>hope</strong> that even the most ardent <code>lambda</code>-lover doesn't consider <em>this</em> horror more readable than the <code>partial</code> call!-). The "attribute setting" part is even harder, because of the "body's a single expression" limitation of Python's <code>lambda</code> (plus the fact that assignment can never be part of a Python expression)... you end up "faking assignments within an expression" by stretching list comprehension well beyond its design limits...:</p> <pre><code>>>> f = [f for f in (lambda f: int(s, base=2),) if setattr(f, 'keywords', {'base': 2}) is None][0] </code></pre> <p>Now combine the named-arguments overridability, plus the setting of three attributes, into a single expression, and tell me just how readable <em>that</em> is going to be...!-)</p>

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