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Lambda functions by dint allows us to create anonymous functions meaning functions having no names.

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Using the facility known as lambda function and lambda functions small functions usually not more than

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in line.

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And this is the syntax for a lambda function.

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Here to have define a normal function and a normal function is defined using the keyword death.

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And then we have the name of the function followed by the parameters parsed in parentheses and function

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header and the body are separated by a colon and then this particular function.

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We are returning the sum of these parameters are the arguments pass to this function.

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Now if we want to return the same result using a lambda function here we have the lambda function when

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you want to use a lambda function you start with D key word lambda and we know lambda functions do not

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have a name.

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So next we pass in the argument by separating them with commas and the argument and the function body

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are separated by a colon and thus the function body is also known as an expression and this expression

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is evaluated by Python and the result put it on.

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And when Python executes a lambda function it evaluates this expression and automatically it dawned

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it results lambda.

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Definition does not include that or don't statement it always contains an expression which is done.

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So there's always an implicit return statement.

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A lambda function can have any number of arguments unlimited number of arguments like V bars to a normal

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function but it should contain only one expression which is evaluated.

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So here we have a lambda expression which evaluates the expression explicit why and how do you parse

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arguments to a lambda function.

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You can parse arguments to a lambda function see if you want to parse the arguments 2 and 3 you can

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parse like this and this expression has Red Dawn the result 5 or you can also assign the lambda function

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to a variable R and identify you like this.

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And if you want to parse arguments to this lambda function you can do this like we do for the normal

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function say the argument at 2 3 and 4 for this lambda function and it returns D result 24 without further

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down statement because lambda functions automatically evaluate the expression and return the result.

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The the lambda function can be used along with built in functions such as map filter and reduce.

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So now let's talk about these three functions beginning with the map function the map function in Python

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takes in a function and a sequence such as a list as arguments.

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So we have a function and then a sequence parsed as arguments to the map function and this function

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is applied on each element of the sequence and a map object is done.

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So let us see how we can use the lambda function along with the map function.

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So we we will define a lambda function which checks if an element from this list is less than or greater

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than zero.

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The argument to this is X which denotes each element in this list and the argument is separated with

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the expression using a colon.

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And this is the expression to be evaluated.

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So for every element for which x is greater than zero the map function returns a true for this particular

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element and then you passed the list as one of the arguments to the map function.

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So here you have your map function containing a lambda function as the first argument and the lambda

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function checks if each element in the list is greater than 0.

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And the second argument is the list itself.

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So when you execute this line of code a Map object is written and this map object needs to be passed

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to a list constructor or a set constructor to create a list.

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Now let us pass this map function to a list constructor like this and then execute this line.

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And here we have boolean values returned after checking this condition.

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Boolean values return in the form of a list after checking the condition for each element in the list.

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Here's another example where we are multiplying each element in the list with two with the number two.

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So when you execute this line of code a Map object is done.

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Now if you want to create a list you can parse this map function to a list constructor and then execute.

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Here you have the list.

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The map function can be applied to more than one list.

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And in this example I have defined a lambda function which and the element in both the so the lambda

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function is applied on board the list so it first applies these function on the element at the 0 8 index.

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Then it goes to the element in the first index and then the second and so on in our example the length

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of the list L1 and L2 are different.

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So one list has less elements than the other.

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So in that case in Python 3 the map function will stop after finishing with the shortest list.

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That means the map function will stop when the shortest list has been consumed.

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So let's look at this example in the Jupiter notebook.

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And here I have defined to list L1 and L2 using the map and the lambda functions.

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I want to add the element in the list that is first add the element in the 0 8 index.

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Then add the elements in the first index and so on and then finally generate a list.

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I have parsed this lambda function to the map function as an argument as the first argument.

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The second argument is the list L 1 and the third argument is the list L 2.

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Now let's execute the code in this cell and then we have the element from list L1 and L2 added.

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Now if you'll see the length of the output list is only for that because until the shortest list has

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been consumed the map function get executed or the map function will stop when the shortest list has

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been consumed.