Python's syntax is relatively convenient and easy to work with, but aside from the basic structure of the language Python is also sprinkled with small syntax structures that make certain tasks especially convenient. The `lambda`

keyword/function construct is one of these, where the creators call it "syntactical candy". Here we'll examine how to use them.

To understand the `lambda`

keyword/function and their constructs we must first review how regular function definitions work in Python. The following is the basic structure of a function definition:

1 2 3 4 5 | def basic(arg): # Or whatever the function does pass # Or whatever the function returns return arg |

What we've defined here is essentially an empty function named *basic*, which receives one argument, does nothing (the pass statement is essentially a valid "do nothing" statement), and then returns the argument it was given. This is the most basic structure of most functions, but the *simplest* function definition would only include one of these statements - in one statement it would either do something (or pass), or return something or `None`

(a `return`

statement always returns at least the `None`

instance, so it can't actually return nothing). Here is how these two cases would look:

1 2 | def simple1(arg): pass def simple2(arg): return arg |

Notice how we also saved a line by putting the function body statements on the same line as the definition statement. A `lambda`

function structure is exactly the latter of these two structures, i.e. a one-statement function which returns something, the difference being that the "return" keyword is implied and doesn't show up in the construct. Now that we know how `lambda`

functions work (identically to a one-statement function), let's construct the *simple2* function in `lambda`

form. To do this, we simply use the `lambda`

construct - if we call our `lambda`

function *simplest*, it would be like so:

1 | simplest = lambda arg: arg |

And that's it! Now we have *simplest* and *simple2* which behave the exact same way, but the former we constructed with much less jargon; however, technically speaking we only saved two characters in this case, so what's the big deal?

If we look in detail at the `lambda`

statement we find that it is actually a statement that *returns* a function (which is why we assign it to a variable), and this is where the real power of the `lambda`

construct is, so let's look at an example where we harness this power.

Let's say we have a list of 3-tuples which looks like this:

1 2 3 4 5 6 7 | tups = [ (1, 3, -2), (3, 2, 1), (-1, 0, 4), (0, -1, 3), (-2, 6, -5) ] |

If we want a sorted version of tups we'd simply have to give it to the built-in `sorted`

method:

1 2 | >>> sorted(tups) [(-2, 6, -5), (-1, 0, 4), (0, -1, 3), (1, 3, -2), (3, 2, 1)] |

This is fine if we *only* want to sort by the **first** value in each tuple, but what if we want to sort by the second or third value? Knowing how the built-in `sorted`

method works, we can use the *key* argument to define a function that will return the desired value (see How to Sort Python Dictionaries by Key or Value for some background on `sorted`

). Here's how it would look for sorting with the 2nd value in each tuple:

1 2 3 4 | >>> def sortkey(tup): ... return tup[1] >>> sorted(tups, key=sortkey) [(0, -1, 3), (-1, 0, 4), (3, 2, 1), (1, 3, -2), (-2, 6, -5)] |

This works perfectly, but we've used up extra space/lines (if in a script file), and extra memory for a one-statement function that only helps to sort our list. If using a different language, we might have no choice but to do this (or worse), but fortunately we have the `lambda`

function construct :-). With it we can define the same function in a simpler form:

1 2 3 | # Returns a function >>> lambda tup: tup[1] <function <lambda> at 0x02BD1230> |

In the case of *simplest* we assigned the lambda function returned by the `lambda`

construct/statement to a value, but the power of the lambda construct is we *don't* have to do that. Instead we can simply use the statement directly in `sorted`

for the key:

1 2 | >>> sorted(tups, key=lambda tup: tup[1]) [(0, -1, 3), (-1, 0, 4), (3, 2, 1), (1, 3, -2), (-2, 6, -5)] |

It works the same way as with `sortkey`

, but by using this form the process is simpler and saves lines and memory!

This is the power of `lambda`

functions and their construct, but the best part is it will also work for any part of your script that needs such a function. That includes the built-in sorting/iterating functions (e.g. `sorted`

, `map`

, and `filter`

), class methods such as `re.sub`

(with the `repl`

argument as a function), instance methods (as with `list.sort`

), callback functions for `Tkinter`

widgets, etc. So don't hesitate to put `lambda`

functions to work and explore all that they are capable of doing!