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    <p>I mostly use Python using object-oriented and procedural styles. Python is actually not particularly well-suited to functional programming.</p> <p>A lot of people think they are writing functional Python code by using lots of <code>lambda</code>, <code>map</code>, <code>filter</code>, and <code>reduce</code>, but this is a bit over-simplified. The hallmark feature of functional programming is a lack of state or side effects. Important elements of a functional style are pure functions, recursive algorithms, and first class functions.</p> <p>Here are my thoughts on functional programming and Python:</p> <ul> <li><p><strong>Pure functions are great.</strong> I do my best to make my module-level functions pure.</p> <ul> <li>Pure functions can be tested. Since they do not depend on outside state, they are much easier to test.</li> <li>Pure functions are able to support other optimizations, such as memoization and trivial parallelization.</li> </ul></li> <li><p><strong>Class-based programming can be pure.</strong> If you want an equivalent to pure functions using Python classes (which is sometimes but not always what you want), </p> <ul> <li>Make your instances immutable. In particular, this mainly means to make your methods always return new instances of your class rather than changing the current one.</li> <li>Use dependency injection rather than getting stuff (like imported module) from global scope.</li> <li>This might not always be exactly what you want.</li> </ul></li> <li><p><strong>Don't try to avoid state all together.</strong> This isn't a reasonable strategy in Python. For example, use <code>some_list.append(foo)</code> rather than <code>new_list = some_list + [foo]</code>, the former of which is more idiomatic and efficient. (Indeed, a ton of the "functional" solutions I see people use in Python are algorithmically suboptimal compared to just-as-simple or simpler solutions that are not functional or are just as functional but don't use the functional-looking tools.)</p></li> <li><p>Learn the best lessons from functional programming, for example <strong>mutable state is dangerous.</strong> Ask yourself, <em>Do I really want to change this X or do I want a new X?</em></p> <ul> <li><p>One really common place this comes up is when processing a list. I would use</p> <pre><code>foo = [bar(item.baz()) for item in foo] </code></pre> <p>rather than</p> <pre><code>for index, _ in enumerate(foo): foo[index] = bar(foo[index].baz()) </code></pre> <p>and stuff like it. This avoids confusing bugs where the same list object is stored elsewhere and shouldn't be changed. (If it <em>should</em> be changed, then there is a decent chance you have a design error. Mutating some list you have referenced multiple places isn't a great way to share state.)</p></li> </ul></li> <li><p><strong>Don't use <code>map</code> and friends gratuitously.</strong> There is nothing more functional about doing this.</p> <ul> <li><code>map</code>/<code>filter</code> are <strong>not</strong> more functional than list comprehensions. List comprehensions were borrowed from Haskell, a pure functional language. <code>map</code> and especially <code>filter</code> can be harder to understand than a list comprehension. I would never use <code>map</code> or <code>filter</code> with a lambda but might if I had a function that already existed; I use <code>map</code> a decent bit.</li> <li>The same goes for <code>itertools.imap</code>/<code>ifilter</code> compared to generator expressions. (These things are somewhat lazy, which is something great we can borrow from the functional world.)</li> <li>Don't use <code>map</code> and <code>filter</code> for side effects. I see this with <code>map</code> a lot, which both makes hard-to-understand code, unneeded lists, and is decidedly not functional (despite people thinking it must be because of <code>map</code>.) Just use a for loop.</li> <li><code>reduce</code> is confusing except for very simple cases. Python has for loops and there is no hurt in using them.</li> </ul></li> <li><p><strong>Don't use recursive algorithms.</strong> This is one part of functional programming Python just does not support well. CPython (and I think all other Pythons) do not support tail call optimization. <strong>Use iteration instead.</strong></p></li> <li><p><strong>Only use <code>lambda</code> when you are defining functions on the fly.</strong> Anonymous functions aren't better than named functions, the latter of which are often more robust, maintainable, and documented.</p></li> </ul>
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