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Welcome back.

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We've had a little bit of a play around with creating your own umpire race but if not that's okay because

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in this section what we're going to do now is have a look at how we can create an umpire race.

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And so in the previous section of video we went through creating some of our own arrays a two a three

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a one and we learnt about different attributes that these arrays had.

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But it was very tedious typing out all of these different numbers so let's have a look at some easier

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ways.

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First to begin with we'll just see it again just in case you sample array.

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How we did it the first time MP dot array and we passed it a list of numbers.

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Wonderful.

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We'll go here and just sell Prince out.

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We'll have a sample array here.

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So this is going to give us a one dimensional none pie array with the elements 1 2 and 3 in it so if

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we check this arrays data type There we go.

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Beautiful.

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Okay.

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Now for example what if we wanted to create an umpire right.

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That was already filled for us.

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What if we didn't want to pass any anything here.

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Let's have a look at how you could do that.

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Let's make one called once and you go empty ones actually.

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What does empty dot ones what do you think that will be.

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Well I've got a little trick I can show you to get a little preview of this let's hit shift and TAB

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walls were inside the brackets Oh what have we got here.

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Signature NDP dot ones shape dock string return a new array of given shape and type filled with ones

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beautiful.

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So what shift in Tab does remember will exit out of here come back into this if we type in any kind

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of functions are empty.

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Dot something brackets.

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If we push shift and tab while we're inside here what that's gonna tell duper to do is open up the functions

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dock string so this gives us information so this information here comes straight from the NUM pi documentation.

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This is going to tell us what this function does return a new array of given shape and type filled with

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ones.

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Well let's see here parameters shape into or sequence of ints e.g. 2 3.

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Let's use that example two three shift and enter.

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Okay.

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What does this look like ones are an array filled with ones that's exactly what it said shift in tab

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return a new array of given shape and type filled with ones and we haven't put in type here but we have

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given its shape.

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Okay.

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Let's have a look here.

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One stock D time that's D type float.

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So if we use one's pretty short float is the default data type.

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Now you could probably find that that's a little challenge for you is to find out what the default data

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type is by reading through the dock string.

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Let's keep going.

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We checked type of 1.

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We can see that it's our beautiful and favorite data type is Andy array.

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Wonderful.

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Now let's have a look.

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If we wanted another array but not with ones we wanted one with zeros.

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MP let's see empty zeros.

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Does it have this dock string return a new array of given shape and type filled with zeros.

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Okay let's do the same again.

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You don't zeros zeros wonderful.

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Okay.

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Now you might be thinking why would you want ones why would you want zeros.

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Well in that case you could imagine zeros is kind of like a place holder array maybe in machine learning

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you want to fill an array with some initial data or that data you don't know yet.

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So you create the zeros just to work out the shapes of data you're working with.

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So before you even implement any data you could make sure that your shapes lined up by using arrays

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filled with zeros.

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But let's keep going.

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Let's see how we could make a few more different kinds of arrays.

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Let's go.

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Range array equals empty.

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Got a range.

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Let's see shift and tab.

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That's what we're practicing at the moment.

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Return evenly spaced values within a given interval.

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Start Stop.

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Step OK.

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Let's have a look Mm hmm.

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Start.

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No stop.

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No.

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Step.

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No.

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Let's try it.

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If in doubt always try.

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0 10 2.

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Now this is gonna be start stop.

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Step what do we think that this will return let's have a look.

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Ranger Ray huh.

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So it's gonna start at 0 0.

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It's gonna stop at 10 but it's gonna step up by 2 0 2 4 6 8.

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Wonderful.

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Let's keep pushing forward.

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Well let's go random array.

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What if we didn't know what numbers we just want now.

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Paul and I just fill an array full of random information for us and beat out random.

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Let's do Rand int shift in town let's practice doctoring.

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Rand low.

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Hi.

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Sighs return random and taggers from low to high exclusive K return and taggers from the discreet uniform

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distribution of the specified daytime parameters low end or array of ints high int or array like of

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ints optional size.

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Okay so let's go 0 10.

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Size 3 by 5.

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What do you think this will return.

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Let's have a look random array size is not defined.

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What have we done wrong here.

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We haven't set size equal.

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Wonderful.

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So what this is done.

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They will see what the actual dock string return random I take is from low.

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This is our low 0 low.

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Inclusive to high exclusive 10 of size 3 by 5.

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So now if we go random array dot size we have 15.

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We want random array dot shape 3 5.

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Wonderful.

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And we can see that in our array the low is 0 and the high is 10.

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Wonderful.

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Let's see one more way of creating random arrays random dot random num Pi has a fair few different ways

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of creating random arrays don't need to know them all.

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Now we go so random dot random what does this do shift tab return random floats in the half open interval

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0 1.

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So this is going to return and it takes a parameter size.

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Let's save this to random array to and then we want to show it.

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Random array 2 wonderful random array 2.

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Let's check the shape.

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Shape.

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There we go.

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Shape is 5 and 3 wonderful SO THAT MEANS IT'S ALWAYS rows then columns so 5 rows 3 columns 1 2 3 4 5

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3 columns.

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And if we check this it's going to be between the interval 0 and 1.

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Wonderful.

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Got a random array and let's see one more.

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So we want random array 3.

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Now this is where an umpire can get a bit confusing is that these dot Rand or random and Dot random

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5 3 do very similar things.

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Random random will go to shift tab.

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We can see what the doc string says here.

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Return values in a given shape.

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So that's what I've done past D 0 and d 1 so dimensions 0 is 5 and dimension 1 is 3.

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Remember always goes to mentioned 0 dimension 1.

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If we go back to the anatomy of our num pi array axis 0 is the same as dimension 0 which is the row

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here and access 1 is the same as dimension one which is the columns here.

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Wonderful Now the thing you should know about the random numbers in num pies they aren't actually random.

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They're set with something called a random seed pie not random seed.

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What could this mean they're pseudo random numbers and pseudo random numbers.

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We'll have a look at this in the next video.

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But this is an important concept to remember going forward is that when num pi generates random numbers

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they're actually pseudo random numbers.

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So take a quick break try practice creating some of your own random arrays using the DOT random or Rand

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end.

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Have a look at the docs strings by pressing shift tab and then the next video will have a look at MP

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dot random seed and see how we can generate some non random random numbers.