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so for this video
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you had some homework
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right remember
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I asked you to copy and paste the summary equations
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the summary functions that we used
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for daily average low temperature
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copy and paste those over to the right
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into the daily high temperature column column d
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so I'm gonna do that right now
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I'm gonna do this one at a time at first
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and then I'll do a whole bunch of them all at once
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so control C to copy
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moving over to the right
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control V to paste
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and something different clearly happens here
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right so in fact
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let's just pause right here
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and as I asked you to do
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I suggested that you click up here
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so that we can get that color coded information
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on what the arguments are for the sum function
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what information is in that sum function
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and we see that instead
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of maintaining the reference to cells C2 through C13
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as soon as we move that information over to the right
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the cell referencing information
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moved over to the right by the same amount
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and what it did is it kept the same
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number of rows
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the same vertical sequence essentially right
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and so it took that rectangle
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and when we shifted the function over to the right
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Excel shifted everything over to the right
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and if we do that with these other functions as well
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and we can do the all three at once
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just using copy and paste
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we see the same thing happened every single time
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every single time is
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updated to that new range of data so
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this is one of those things that Excel does to help you
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but whenever
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remember I've said this before
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whenever Excel does a shortcut to help you
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it's making an Assumption
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Excel makes an Assumption that if you move a function
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if you move an equation over to the right
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or if you move it anywhere
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that you want to change what cells
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that equation or function is referring to
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what this example shows is
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it shows the principle of relative cell references
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and this is the default
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cell referencing system that Excel uses
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what we mean by relative cell references is in this
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some function
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for example
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it looks like we're asking Excel to some cells
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C2 through C13
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and in fact
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that's what we've told Excel to do
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but what Excel is actually thinking is
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it's going to sum 12 cells that are four cells above
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the cell where the function is 1
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2 3 4 and then it's going to sum all 12 of those cells
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over here that works
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one two three
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four and then it's going to sum all of those cells
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relative cell referencing is what this is called
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and again it's a fantastic shortcut
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we can do it again
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what if we want to know the average of um
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the average precipitation that we get in my area
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every months
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right across a year
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average precipitation per month
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really quickly
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we just copy and paste that one cell
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it brings over the equation with it
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it brings the function with it
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and it updates to that new location
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it has the same relative location
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this is thinking sort of spatially
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it has the same spatial distance
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to where we entered in that equation
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this is fantastic
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it saves a lot of time
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it doesn't always work
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so for example
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what if we actually want to move the value 89.96
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repeating if I copy and paste that
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I'll just go over one more cell there
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now we're going to get an error
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because what's happening is
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now Excel is looking at an empty column
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to try to do a calculation
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when really we don't want it to do that calculation
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really all we want is to try to move that number over
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and that's where things
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can get a little bit complicated
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is if you want to do what Excel thinks you want to do
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then it's going to work beautifully
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this relative cell referencing system
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but for other types of operations
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other things we might wanna do
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we're gonna need to make some changes
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and so what we're going to explore next
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now that we understand this concept of relative
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cell referencing
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you're going to explore how we can move values around
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without that equation updating
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how we can just move that value by itself
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the number the answer by itself
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or how we can move that equation and function
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and have it still refer back to where it
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was referring original
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so we can break that relative cell referencing system
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with something called absolute cell referencing
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so that's what we're going to tackle next
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and this is really going to help you
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master spreadsheets
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because both of these tools
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relative cell referencing and absolute cell referencing
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are going to come in very handy
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especially when you're using equations and functions
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so we just learns that cell references in Excel
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are always relative cell references right
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they're always spatial references
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and so just like I have highlighted here as a reminder
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we're looking at the cells in this range above
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where our equation is
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every time we move that
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that range of cells is gonna move with it
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how do we change things to be absolute cell references
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and what I mean by absolute cell references is
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for example
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this average equation
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where we're averaging the
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daily low temperatures for every month across the year
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how can we make sure that we're always
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absolutely always referring to that set of cells
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and it's not gonna change
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to make our cell references absolute cell references
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we add dollar signs in between each column and row
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in the arguments of the equation
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and so right up here
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I can do it manually
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I'll show you another way to do this
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we can just
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add in dollar signs before each column and row
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and now magically
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this has become an absolute cell reference
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because of those dollar signs
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I can now move this equation
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move this function around wherever I want to
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and it's always going to point back
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to these original cells
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so we'll test this right now
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you see over here on the right
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I've created a new location for these summaries
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so this will be a good way to test this
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so if I copy and paste
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remember when I copy the cell
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it brings the underlying equation
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the underlying formula with it
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if I copy that over here
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oh look at that
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click up here
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and it's referring to those cells
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those original cells
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and we get the same answer
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we get 48.7
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just like we had before
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remember what it would be doing if it was still using
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relative cell references dude
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actually be looking way up off the top of the sheet
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right would be looking up for cells
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and then for a range of 12 cells up above that
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but we fix that now
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let's do this
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also with mean daily high temperature for months
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so we'll come over here
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I'll show you a shortcut for doing this
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we can just highlight that equation in the formula bar
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instead of typing in the dollar signs
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we'll just hit F4
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and F4 automatically
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converts that relative cell reference
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to absolute cell reference
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now this is not gonna change
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we can do the same things
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we can copy
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we can paste that to the daily high
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and it's going to update just like that
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so this is a very important thing to understand
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is you start working in more advanced tasks in Excel
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I'm working with other people's spreadsheets
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where they're probably referring to different cells
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and you need to be able to know
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if you're dealing with an absolute or a relative
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cell reference very
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very important in more advanced applications in Excel
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I'm gonna show you a couple other ways
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that we can solve the same problem though
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let's now look at this
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average precipitation per month
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88 points 9 6 ish millimeters per month
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around here
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and so what we wanna do now
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is we wanna look at different ways
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that we can move this value over
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so one way we can do is
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we can take advantage of something that's called
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paste special
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so I just copied
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and I'm going to right click
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and you may have noticed
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that we have these paste options
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these paste special options
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and one of these options is
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we can just paste the values
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so let's do that
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click on that
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and what happens here
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is now only the value has been moved over
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so if we click
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we see that there's no longer an equation
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there's no formula underlying that
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there's only the value
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so if we only wanna move the value
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the result of an equation
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we can do that
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paste special
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paste values
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there's a drawback to that
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so let's say we
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let's say we delete June
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from our data set
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you can see temperature updates
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but over here
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on the right
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precipitation does not update
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it changed here
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because we lost the value for June
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it does not update over here
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because we only pasted that number
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so this isn't necessarily the best option
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I'm going to undo those
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I'm hitting control Z to undo those deletions I did
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pasting those values by themselves
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is not always the best option
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because now that value will never update
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maybe that's how we wanna do it sometimes
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but we need to be careful
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and we need to make sure that's really what we wanna do
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so I'm gonna show you one other option here
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we can do another copy
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and we're gonna do another paste special
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what we can do is we can paste a link
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and what we've just done is
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we now are linking to this cell
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we don't have the equation in there anymore
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we're saying it is equal to cell E20
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so cell M4 is always going to equal E20
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and we know it's always going to equal E20
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because we're using an absolute cell reference there
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because we seal those dollar size
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as I mentioned
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understanding this distinction between relative
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and absolute cell references is very
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very important
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as you start working in more advanced spreadsheets
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and especially when
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you start working with other people's spreadsheets
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and you need to figure out how those cells
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are referring to each other
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and what people are doing in their equations
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functions and formulas
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we'll have some applications of this very soon
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but now you know the difference between
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00:11:01,000 --> 00:11:03,566
relative and absolute cell references in Excel
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most recently
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we've been working with functions
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and getting a more advanced
284
00:11:08,466 --> 00:11:11,066
understanding of cell references in Excel
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and right now
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we're gonna take a little bit of a break
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from working on formulas and functions
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and we're gonna start looking at some different ways
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00:11:17,933 --> 00:11:19,766
to manipulate and reorganize
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00:11:19,766 --> 00:11:22,433
and pull meaning from our spreadsheets
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don't worry
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we're going to be coming back to functions
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and learning more advanced functions
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that we can use in formulas but these
295
00:11:30,366 --> 00:11:32,899
these sets of tools that I'm gonna be talking about now
296
00:11:32,900 --> 00:11:34,233
sorting and filtering
297
00:11:34,333 --> 00:11:36,733
are some of the most powerful tools that we have
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00:11:36,733 --> 00:11:37,933
and they're just a few things that
299
00:11:37,933 --> 00:11:39,799
you need to make sure you think about and master
300
00:11:39,800 --> 00:11:42,933
before you start engaging in sorting and filtering
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00:11:42,933 --> 00:11:45,266
to make sure you're doing what you think you are
302
00:11:45,300 --> 00:11:47,466
and to maintain the integrity
303
00:11:47,466 --> 00:11:49,433
of the data you have in your spreadsheet
304
00:11:49,733 --> 00:11:50,866
for sorting and filtering
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00:11:50,866 --> 00:11:52,533
we're gonna start with our weather data
306
00:11:52,533 --> 00:11:53,999
and then we're gonna move on to some larger
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more advanced data sets
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00:11:55,466 --> 00:11:57,466
again it's useful to have a small data set
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00:11:57,466 --> 00:11:59,166
where we can see everything that's going on
310
00:11:59,166 --> 00:12:00,933
and a data set where we're familiar with it
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so we know when something is going wrong
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00:12:03,366 --> 00:12:06,199
now the sort function in Excel
313
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as I've said
314
00:12:06,800 --> 00:12:09,833
is one of the most useful things in Excel
315
00:12:09,900 --> 00:12:12,900
sometimes we want to be able to identify the largest
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00:12:12,900 --> 00:12:13,766
smallest value
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the earliest or latest value
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00:12:15,333 --> 00:12:16,966
organize things alphabetically
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or any other way
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00:12:18,200 --> 00:12:20,966
and we can do all of that through sorting
321
00:12:21,100 --> 00:12:23,200
there are few key things to keep in mind
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00:12:23,200 --> 00:12:24,566
and that's the point of this video
323
00:12:24,566 --> 00:12:26,766
on some of the exercises you're going to be doing
324
00:12:27,100 --> 00:12:28,366
now we're on the home menu
325
00:12:28,366 --> 00:12:29,333
we're on the home tab
326
00:12:29,333 --> 00:12:30,899
home tab and we see our ribbon
327
00:12:30,900 --> 00:12:32,366
of all our different commands here
328
00:12:32,366 --> 00:12:33,899
if we look way on the right here
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00:12:33,900 --> 00:12:35,633
we have a sort and filter button
330
00:12:35,966 --> 00:12:39,366
I think it's more useful to go into the data tab
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00:12:39,566 --> 00:12:42,599
where we have more details
332
00:12:42,600 --> 00:12:45,333
more complete sort and filter functions here
333
00:12:45,333 --> 00:12:47,233
so that's what we're going to be working with
334
00:12:47,500 --> 00:12:48,666
our first job
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00:12:48,666 --> 00:12:51,699
is that we're going to sort our weather data
336
00:12:51,733 --> 00:12:53,766
by the daily high temperature
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00:12:53,766 --> 00:12:54,533
and basically
338
00:12:54,533 --> 00:12:56,466
we're going to put this in order
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00:12:56,466 --> 00:12:57,599
where we're looking at
340
00:12:57,600 --> 00:12:58,466
which months have
341
00:12:58,466 --> 00:13:00,666
the lowest daily high temperature on average
342
00:13:00,666 --> 00:13:03,099
and which months have the highest daily
343
00:13:03,133 --> 00:13:04,566
temperature on average
344
00:13:04,566 --> 00:13:07,266
so we wanna sort our data set
345
00:13:07,400 --> 00:13:10,333
so that temperature is ascending
346
00:13:10,333 --> 00:13:11,633
so it's climbing
347
00:13:12,100 --> 00:13:14,066
we're gonna do that by sorting on
348
00:13:14,066 --> 00:13:16,999
by clicking on the sort button here now
349
00:13:17,000 --> 00:13:20,000
this is one of the most important things to think about
350
00:13:20,400 --> 00:13:22,300
if we select our column of interest
351
00:13:22,300 --> 00:13:24,800
as I have our daily high temperature
352
00:13:24,866 --> 00:13:26,599
and then we choose to sort it
353
00:13:26,600 --> 00:13:29,200
Excel immediately gives us a warning here
354
00:13:29,266 --> 00:13:30,966
it's suggesting we expand
355
00:13:30,966 --> 00:13:32,966
the selection from this one column
356
00:13:33,166 --> 00:13:35,566
because it's suggesting that we probably wanna sort
357
00:13:35,566 --> 00:13:36,766
all of our days together
358
00:13:36,766 --> 00:13:38,566
and not just this one column
359
00:13:38,566 --> 00:13:40,099
and this is absolutely correct
360
00:13:40,100 --> 00:13:41,600
this is a place where we do wanna
361
00:13:41,600 --> 00:13:43,166
listen to Excel's advice
362
00:13:43,400 --> 00:13:45,766
think about what will happen if we simply sort
363
00:13:45,766 --> 00:13:46,633
temperature
364
00:13:46,733 --> 00:13:48,299
we only sort temperature
365
00:13:48,400 --> 00:13:49,500
then those temperatures
366
00:13:49,500 --> 00:13:51,300
are no longer going to be associated with
367
00:13:51,300 --> 00:13:52,400
the correct month
368
00:13:52,400 --> 00:13:53,800
they're not going to be associated with
369
00:13:53,800 --> 00:13:55,366
the correct precipitation
370
00:13:55,400 --> 00:13:56,066
and basically
371
00:13:56,066 --> 00:13:57,499
we've messed up our data set
372
00:13:57,800 --> 00:14:00,000
and so this is a really critical thing to think about
373
00:14:00,000 --> 00:14:01,966
even when we wanna sort
374
00:14:01,966 --> 00:14:03,799
buy one column
375
00:14:03,933 --> 00:14:07,099
we want to sort our entire data set usually
376
00:14:07,100 --> 00:14:09,100
so we're gonna take Excel's advice
377
00:14:09,100 --> 00:14:11,400
and we're going to expand the selection
378
00:14:11,733 --> 00:14:13,066
once we do that
379
00:14:13,600 --> 00:14:16,800
now Excel is going to ask us what we want to sort by
380
00:14:16,900 --> 00:14:18,600
we're going to sort by
381
00:14:18,600 --> 00:14:20,000
daily high temperature
382
00:14:20,266 --> 00:14:22,266
we're gonna use the cell values
383
00:14:22,266 --> 00:14:23,466
the numbers in the cells
384
00:14:23,466 --> 00:14:25,266
as our sort criteria
385
00:14:25,600 --> 00:14:27,300
and we're gonna go from smallest to largest
386
00:14:27,300 --> 00:14:28,566
we're going to increase
387
00:14:28,566 --> 00:14:30,699
temperature as we go down in the spreadsheet
388
00:14:30,700 --> 00:14:32,500
it's gonna be an ascending sort
389
00:14:33,400 --> 00:14:34,333
the data has headers
390
00:14:34,333 --> 00:14:36,333
Excel recognize this and so yes
391
00:14:36,333 --> 00:14:37,666
we are not sorting
392
00:14:37,666 --> 00:14:40,199
we're not including that first row of data
393
00:14:40,300 --> 00:14:41,266
that's fantastic
394
00:14:41,266 --> 00:14:42,799
because that would be confusing also
395
00:14:42,800 --> 00:14:44,166
so let's go ahead and click okay
396
00:14:44,166 --> 00:14:45,299
and see what we have here
397
00:14:45,900 --> 00:14:47,100
we know sort
398
00:14:47,100 --> 00:14:47,700
did something
399
00:14:47,700 --> 00:14:50,566
because we see our index values are now out of order
400
00:14:50,566 --> 00:14:52,766
this is where having an index column
401
00:14:52,766 --> 00:14:54,199
can be really useful
402
00:14:54,300 --> 00:14:56,766
it tells us when our data are in some order
403
00:14:56,766 --> 00:14:58,999
other than the original order that we had them in
404
00:14:59,133 --> 00:15:01,399
we could tell that by looking at once as well
405
00:15:01,600 --> 00:15:03,366
but it's not quite as intuitive as looking
406
00:15:03,366 --> 00:15:04,366
at those numbers
407
00:15:04,400 --> 00:15:05,333
and we do indeed
408
00:15:05,333 --> 00:15:06,666
see that our
409
00:15:06,700 --> 00:15:08,666
month with the lowest daily high temperature
410
00:15:08,666 --> 00:15:10,133
around here is January
411
00:15:10,133 --> 00:15:12,199
about 51 degrees Fahrenheit
412
00:15:12,400 --> 00:15:14,166
and our highest is August
413
00:15:14,166 --> 00:15:15,366
I'm sorry it's July
414
00:15:15,366 --> 00:15:17,799
with about 89 degrees Fahrenheit
415
00:15:17,800 --> 00:15:19,566
so this worked very effectively
416
00:15:19,566 --> 00:15:21,499
and every other data point
417
00:15:21,600 --> 00:15:23,166
sorted along with that
418
00:15:23,166 --> 00:15:24,066
so our data
419
00:15:24,066 --> 00:15:25,266
still has integrity
420
00:15:25,266 --> 00:15:27,199
we can say that this first row
421
00:15:27,200 --> 00:15:29,433
is all associated with January
422
00:15:29,600 --> 00:15:32,400
last row is all associated with July
423
00:15:32,400 --> 00:15:33,666
and that's really critical
424
00:15:33,666 --> 00:15:35,133
when we're thinking about sorting
425
00:15:35,133 --> 00:15:36,899
now often when we sort
426
00:15:36,900 --> 00:15:39,966
this is a temporary way of manipulating our data
427
00:15:39,966 --> 00:15:41,299
we wanna make sure we can
428
00:15:41,300 --> 00:15:43,400
get it back to the original format
429
00:15:43,400 --> 00:15:45,966
we want everything to get back into order
430
00:15:46,066 --> 00:15:47,399
there are several ways we can do this
431
00:15:47,400 --> 00:15:49,600
if sorting was the last thing we did
432
00:15:49,600 --> 00:15:50,500
then we can just
433
00:15:50,500 --> 00:15:52,233
use our undo
434
00:15:52,300 --> 00:15:53,400
keyboard shortcut
435
00:15:53,400 --> 00:15:55,533
control Z to really quickly
436
00:15:55,533 --> 00:15:57,666
put everything back into the proper order
437
00:15:57,666 --> 00:15:58,733
so that's one option
438
00:15:58,733 --> 00:15:59,733
is using Control Z
439
00:15:59,733 --> 00:16:01,299
just to undo that sort
440
00:16:01,466 --> 00:16:02,333
but that won't work
441
00:16:02,333 --> 00:16:03,466
if you sorted your data
442
00:16:03,466 --> 00:16:04,266
a while ago
443
00:16:04,266 --> 00:16:05,966
and did a whole bunch of things in between
444
00:16:06,166 --> 00:16:07,966
so I've just re sorted it
445
00:16:07,966 --> 00:16:09,766
so we can look at some other ways
446
00:16:09,766 --> 00:16:10,499
what are some ways
447
00:16:10,500 --> 00:16:11,100
you can think of
448
00:16:11,100 --> 00:16:11,900
where we can
449
00:16:11,900 --> 00:16:14,400
resort these data back into the original order
450
00:16:16,133 --> 00:16:18,099
I can think of at least two ways
451
00:16:18,566 --> 00:16:21,366
now we're seeing the value of that index column right
452
00:16:21,366 --> 00:16:22,299
if we want us
453
00:16:22,300 --> 00:16:25,133
put this data set back into the original order
454
00:16:25,133 --> 00:16:26,999
we can just resort
455
00:16:27,266 --> 00:16:30,099
but we're going to do it by index this time
456
00:16:30,100 --> 00:16:33,400
we will reorganize those from smallest to largest
457
00:16:33,600 --> 00:16:35,133
and boom fantastic
458
00:16:35,133 --> 00:16:37,766
we now have our data back in the original order
459
00:16:37,766 --> 00:16:40,466
this is part of the reason I like to have an index
460
00:16:40,466 --> 00:16:44,366
or an ID column in almost any data set I'm working with
461
00:16:44,366 --> 00:16:46,566
with it can really help you solve issues
462
00:16:46,566 --> 00:16:48,466
like the one we just solved here
463
00:16:49,366 --> 00:16:50,533
we'll do one more thing
464
00:16:50,533 --> 00:16:52,299
and this is a slightly more advanced
465
00:16:52,300 --> 00:16:53,966
option within the sort menu
466
00:16:54,200 --> 00:16:55,900
so we know that
467
00:16:55,900 --> 00:16:58,866
our data were originally in the order of months
468
00:16:58,933 --> 00:17:02,899
and so let's see if we can sort order based on month
469
00:17:03,166 --> 00:17:07,399
now the first option we're given here is to sort a to Z
470
00:17:08,100 --> 00:17:10,066
that's not really gonna give us what we want
471
00:17:10,066 --> 00:17:12,666
we don't want our months sorted alphabetically
472
00:17:13,200 --> 00:17:14,733
maybe we would at some point
473
00:17:14,733 --> 00:17:15,799
but that's not what we want
474
00:17:15,800 --> 00:17:19,266
we want them back in calendar year order
475
00:17:19,300 --> 00:17:20,966
so can we do that
476
00:17:21,500 --> 00:17:22,400
the answer is yes
477
00:17:22,400 --> 00:17:25,000
if we choose the drop down on the order menu
478
00:17:25,000 --> 00:17:27,200
we can go here to custom list
479
00:17:27,200 --> 00:17:28,700
and this is a little bit hard to find
480
00:17:28,700 --> 00:17:30,800
you wouldn't necessarily know this is in here
481
00:17:30,966 --> 00:17:32,866
and if we look at this custom list
482
00:17:32,866 --> 00:17:36,466
we can create our own list and put any order we want
483
00:17:36,533 --> 00:17:37,966
or as you see
484
00:17:37,966 --> 00:17:40,533
there are some standardized lists
485
00:17:40,533 --> 00:17:41,833
days of the week
486
00:17:42,366 --> 00:17:46,399
months so let's go with this list of full months
487
00:17:46,400 --> 00:17:47,700
that's the order we want
488
00:17:47,700 --> 00:17:50,266
things in the order will be January
489
00:17:50,266 --> 00:17:51,599
February March and so on
490
00:17:51,600 --> 00:17:52,900
and we click okay
491
00:17:53,100 --> 00:17:55,300
and voila our order
492
00:17:55,500 --> 00:17:58,233
our data goes back into the original order
493
00:17:58,300 --> 00:18:00,800
so it's another way of unsorting
494
00:18:00,800 --> 00:18:03,433
and going back to that original data format
495
00:18:04,066 --> 00:18:05,066
again with Excel
496
00:18:05,066 --> 00:18:07,266
it's always useful when you're manipulating
497
00:18:07,266 --> 00:18:08,799
when you're changing your data
498
00:18:08,966 --> 00:18:10,666
making sure you have some way to
499
00:18:10,666 --> 00:18:12,833
get it back to the way it originally was
500
00:18:12,900 --> 00:18:15,266
that's a really important data management practice
501
00:18:15,266 --> 00:18:16,599
and even when we're sorting
502
00:18:16,600 --> 00:18:18,433
we need to be thinking about that
503
00:18:18,566 --> 00:18:19,966
gonna give you some exercises
504
00:18:19,966 --> 00:18:22,066
so you can practice this yourself now
505
00:18:22,466 --> 00:18:23,333
so at this point
506
00:18:23,333 --> 00:18:24,899
a great thing for us to be doing
507
00:18:24,900 --> 00:18:27,666
is actually combining some of our skills together
508
00:18:27,733 --> 00:18:30,199
so that we can really integrate what we're learning
509
00:18:30,200 --> 00:18:31,900
that's gonna lead to much more
510
00:18:32,100 --> 00:18:34,400
advanced use of Excel in the future
511
00:18:34,400 --> 00:18:37,000
is thinking about how we can integrate these tools
512
00:18:37,000 --> 00:18:38,366
and use them all together
513
00:18:38,400 --> 00:18:41,100
so this is why I asked you to create this temp diff
514
00:18:41,100 --> 00:18:42,933
column where we're calculating
515
00:18:42,933 --> 00:18:45,999
how variable the temperatures are in each month
516
00:18:46,166 --> 00:18:48,999
I asked you to write a formula to do this
517
00:18:49,000 --> 00:18:50,933
and I started you off here by reminding you
518
00:18:50,933 --> 00:18:53,099
that we would always start with an equal sign
519
00:18:53,300 --> 00:18:54,900
there are number of ways we can do this
520
00:18:54,900 --> 00:18:56,733
but I think maybe the easiest way
521
00:18:56,733 --> 00:18:59,299
is to just click on our individual cells
522
00:18:59,300 --> 00:19:00,800
and say that we wanna take the difference
523
00:19:00,800 --> 00:19:03,500
between the high temperature and the low temperature
524
00:19:03,766 --> 00:19:04,899
and that's all it is
525
00:19:04,900 --> 00:19:07,300
it's just a simple arithmetic equation
526
00:19:07,300 --> 00:19:09,866
that we can write using the minus sign
527
00:19:09,866 --> 00:19:11,766
so we don't always have to use functions
528
00:19:11,766 --> 00:19:13,599
we can use math equations
529
00:19:13,600 --> 00:19:14,666
we can use plus
530
00:19:14,666 --> 00:19:15,666
minus divide
531
00:19:15,666 --> 00:19:16,666
and multiply
532
00:19:16,666 --> 00:19:17,499
and a lot of the time
533
00:19:17,500 --> 00:19:19,366
that's going to give us something that we want
534
00:19:19,500 --> 00:19:21,166
we see that that temperature
535
00:19:21,166 --> 00:19:23,733
that variability in the month of January
536
00:19:23,733 --> 00:19:25,666
is 21 degrees Fahrenheit
537
00:19:25,666 --> 00:19:26,899
that's not a whole lot
538
00:19:27,000 --> 00:19:27,800
and remember
539
00:19:27,800 --> 00:19:29,900
what we can do is there are multiple
540
00:19:29,900 --> 00:19:32,566
different ways that we can copy this value down
541
00:19:32,933 --> 00:19:34,299
but if we do that
542
00:19:34,333 --> 00:19:36,566
Excel is going to assume
543
00:19:36,566 --> 00:19:40,433
because these are relative cell references
544
00:19:40,733 --> 00:19:43,499
Excel is going to assume that we're always want
545
00:19:43,500 --> 00:19:46,400
going to want to be looking immediately to the left
546
00:19:46,966 --> 00:19:49,466
and then subtracting the value
547
00:19:49,466 --> 00:19:51,966
that's two cells to the left
548
00:19:52,066 --> 00:19:55,299
and so if we just paste that in right here
549
00:19:56,133 --> 00:19:59,166
then we can see that we get the same value
550
00:19:59,166 --> 00:20:01,399
and every time we click on a cell
551
00:20:01,766 --> 00:20:04,499
we're always looking in column D
552
00:20:04,900 --> 00:20:06,200
minus column C
553
00:20:06,300 --> 00:20:07,900
but as we go down
554
00:20:07,900 --> 00:20:11,633
the row number is updating automatically perfect
555
00:20:11,700 --> 00:20:12,800
and this is interesting right
556
00:20:12,800 --> 00:20:13,866
because we actually see that
557
00:20:13,866 --> 00:20:14,733
where I live
558
00:20:14,733 --> 00:20:16,666
that temperature variability is
559
00:20:16,733 --> 00:20:18,699
about the same every single month
560
00:20:18,700 --> 00:20:21,066
it's right around the low twenties
561
00:20:21,566 --> 00:20:23,199
as low temperatures go up
562
00:20:23,200 --> 00:20:24,966
high temperatures also go up
563
00:20:25,066 --> 00:20:26,533
where did we see this before
564
00:20:26,533 --> 00:20:28,533
we saw this when we made a scatter plot
565
00:20:28,533 --> 00:20:30,733
so we're seeing how a lot of these different ways
566
00:20:30,733 --> 00:20:31,999
of looking at our data
567
00:20:32,200 --> 00:20:33,766
are starting to come together
568
00:20:33,866 --> 00:20:34,866
so let's go ahead
569
00:20:34,866 --> 00:20:36,533
and we'll sort our data set now
570
00:20:36,533 --> 00:20:39,566
based on this new value that we just created
571
00:20:39,933 --> 00:20:41,533
and I'm going to say okay
572
00:20:41,533 --> 00:20:44,799
we're gonna use temptive cell values and no
573
00:20:44,800 --> 00:20:47,100
we're not gonna do it based on month order
574
00:20:47,100 --> 00:20:49,033
we're gonna go smallest to largest
575
00:20:49,366 --> 00:20:53,066
our least variable month is the month of August
576
00:20:53,200 --> 00:20:54,666
our most variable month
577
00:20:54,666 --> 00:20:55,966
in terms of temperature
578
00:20:56,266 --> 00:20:58,399
is the month of April
579
00:20:58,566 --> 00:21:01,133
and again if we wanted to undo this
580
00:21:01,133 --> 00:21:02,666
we could use undo
581
00:21:02,766 --> 00:21:06,233
we could sort custom on the months column
582
00:21:06,266 --> 00:21:08,466
or we can just sort numerically
583
00:21:08,466 --> 00:21:10,133
based on our index column
584
00:21:10,133 --> 00:21:12,133
all of these would get us what we wanted
585
00:21:12,133 --> 00:21:13,566
so I hope you were successful in
586
00:21:13,566 --> 00:21:14,566
calculating that difference
587
00:21:14,566 --> 00:21:16,699
in doing these sorting functions and soon
588
00:21:16,700 --> 00:21:19,966
we'll be moving on with a close companion of sorting
589
00:21:19,966 --> 00:21:21,233
which is filtering
590
00:21:21,666 --> 00:21:24,999
so we just did some really great work with sorting
591
00:21:25,000 --> 00:21:26,733
and now we're gonna move on to a skill
592
00:21:26,733 --> 00:21:29,133
that I consider a close companion of sorting
593
00:21:29,133 --> 00:21:30,799
sorting and that's filtering
594
00:21:30,966 --> 00:21:33,266
so let's quickly compare and contrast
595
00:21:33,266 --> 00:21:34,933
what's going on with these two things
596
00:21:34,933 --> 00:21:35,699
with sorting
597
00:21:35,700 --> 00:21:37,100
we're changing the order of our data
598
00:21:37,100 --> 00:21:38,500
but we're keeping it all there
599
00:21:38,500 --> 00:21:40,900
we're just looking at it in a different order
600
00:21:41,166 --> 00:21:43,499
and maybe that's because we want it alphabetical
601
00:21:43,500 --> 00:21:44,800
or an increasing order
602
00:21:44,800 --> 00:21:45,766
or month order
603
00:21:45,766 --> 00:21:46,799
something like that
604
00:21:46,800 --> 00:21:48,033
with filtering
605
00:21:48,133 --> 00:21:51,066
we're actually only looking at some of our data
606
00:21:51,066 --> 00:21:52,933
removing some of our data from view
607
00:21:52,933 --> 00:21:53,999
that we're not interested in
608
00:21:54,000 --> 00:21:54,900
so that we can focus on
609
00:21:54,900 --> 00:21:57,200
on data that we are interested in
610
00:21:57,333 --> 00:21:59,333
so these are slightly related
611
00:21:59,333 --> 00:22:01,566
but slightly different ways of organizing
612
00:22:01,566 --> 00:22:03,766
and drawing different kinds of meaning from our data
613
00:22:04,100 --> 00:22:06,200
I say that these are closely related
614
00:22:06,200 --> 00:22:08,166
and it looks like Excel agrees with me
615
00:22:08,166 --> 00:22:11,133
because we see that filter is right next to sort
616
00:22:11,133 --> 00:22:12,399
in the data menu here
617
00:22:12,400 --> 00:22:14,166
and so it's going to be easy to find
618
00:22:14,333 --> 00:22:16,933
let's do some practice filtering to see how it works
619
00:22:16,933 --> 00:22:18,999
and to see what we can get out of filtering
620
00:22:19,266 --> 00:22:21,899
now notice them already clicked within our data
621
00:22:21,900 --> 00:22:23,000
that's important
622
00:22:23,466 --> 00:22:25,499
because now that my cursor
623
00:22:25,500 --> 00:22:27,666
I have a highlighted cell within our data
624
00:22:27,666 --> 00:22:29,999
I can just click on this filter button
625
00:22:30,466 --> 00:22:31,799
and what that's going to do is
626
00:22:31,800 --> 00:22:32,900
it's going to add in
627
00:22:32,900 --> 00:22:35,400
pull down menus to each of our columns
628
00:22:35,500 --> 00:22:36,800
this is really interesting
629
00:22:36,800 --> 00:22:38,466
we didn't get a pop up window
630
00:22:38,466 --> 00:22:39,466
we got something else
631
00:22:39,466 --> 00:22:40,399
we got these pull downs
632
00:22:40,400 --> 00:22:42,500
so let's see what we can get here
633
00:22:43,166 --> 00:22:47,066
let's assume that we want a filter based on month
634
00:22:47,066 --> 00:22:50,799
and we only wanna look at the winter months around here
635
00:22:50,800 --> 00:22:51,866
where I am and so
636
00:22:51,866 --> 00:22:52,933
winter months around here
637
00:22:52,933 --> 00:22:53,699
I'm just gonna say
638
00:22:53,700 --> 00:22:55,300
we consider December
639
00:22:55,300 --> 00:22:57,166
January and February to be winter
640
00:22:57,766 --> 00:22:59,466
and the problem with the data right now is
641
00:22:59,466 --> 00:23:00,499
those months are separated
642
00:23:00,500 --> 00:23:02,633
we have January and February together
643
00:23:03,400 --> 00:23:04,600
December is different
644
00:23:04,600 --> 00:23:05,566
it's down at the bottom
645
00:23:05,566 --> 00:23:07,066
because it's in order of months
646
00:23:07,066 --> 00:23:07,666
and so maybe
647
00:23:07,666 --> 00:23:09,866
we wanna make sure we can look at all those together
648
00:23:09,866 --> 00:23:10,999
so we'll filter out everything
649
00:23:11,000 --> 00:23:11,800
other than that
650
00:23:11,800 --> 00:23:14,000
we'll just click on this little pull down menu
651
00:23:14,466 --> 00:23:15,899
and you immediately see that
652
00:23:15,900 --> 00:23:18,133
we also have our sort options here
653
00:23:18,133 --> 00:23:20,499
and so this is one way that you could do sorting
654
00:23:20,500 --> 00:23:21,366
we won't do it now
655
00:23:21,366 --> 00:23:22,999
because we've just been practicing with it
656
00:23:23,000 --> 00:23:25,066
but if you wanna apply the filter
657
00:23:25,066 --> 00:23:26,699
to your entire data set
658
00:23:26,733 --> 00:23:28,933
and then use these pull down menus to sort
659
00:23:28,933 --> 00:23:29,866
you could do that
660
00:23:30,066 --> 00:23:31,266
so this is really great
661
00:23:31,266 --> 00:23:33,999
it's a comprehensive data reorganization window
662
00:23:34,000 --> 00:23:35,066
that we have here
663
00:23:35,800 --> 00:23:36,733
what you see
664
00:23:36,733 --> 00:23:39,066
is that everything that's shown in our data set
665
00:23:39,066 --> 00:23:40,799
has a checkbox next to it
666
00:23:41,366 --> 00:23:43,133
so as you recall
667
00:23:43,133 --> 00:23:45,333
we only now wanna look at winter months
668
00:23:45,333 --> 00:23:47,299
we only wanna look at December
669
00:23:47,300 --> 00:23:48,666
January and February
670
00:23:49,000 --> 00:23:51,700
think the easiest way to do this is to unselect
671
00:23:51,700 --> 00:23:52,333
everything now
672
00:23:52,333 --> 00:23:54,066
we're not gonna see anything in our data
673
00:23:55,333 --> 00:23:57,833
but we'll go ahead and reselect December
674
00:23:57,933 --> 00:24:00,266
February and January those three months
675
00:24:00,500 --> 00:24:02,900
now I'm gonna click okay and we'll see what happens
676
00:24:04,400 --> 00:24:05,566
this is a little scary
677
00:24:05,566 --> 00:24:06,699
the first time it happens
678
00:24:06,700 --> 00:24:09,166
it looks like you just lost all your data
679
00:24:09,533 --> 00:24:10,866
but what it's doing is
680
00:24:10,866 --> 00:24:13,533
it's hiding data that you said you're not interested in
681
00:24:13,533 --> 00:24:15,066
and only showing you the data
682
00:24:15,066 --> 00:24:16,966
that you said you are interested in
683
00:24:17,366 --> 00:24:19,599
how do we know our other data is still there
684
00:24:19,666 --> 00:24:21,666
well we could undo this and get it back
685
00:24:21,666 --> 00:24:22,566
and that's really easy
686
00:24:22,566 --> 00:24:23,999
but we can also see
687
00:24:24,000 --> 00:24:27,100
when we look at our row numbers
688
00:24:27,166 --> 00:24:29,266
it's telling us we're missing a bunch of rows
689
00:24:29,266 --> 00:24:30,399
it's only showing us here
690
00:24:30,400 --> 00:24:31,566
highlighted in blue
691
00:24:31,666 --> 00:24:33,466
row 2 row 3
692
00:24:33,533 --> 00:24:34,499
row thirteen
693
00:24:35,200 --> 00:24:37,933
it's telling us we have other rows of data in there
694
00:24:37,933 --> 00:24:39,466
they just happen to be him so
695
00:24:39,466 --> 00:24:41,599
that should make us feel a little bit better about it
696
00:24:42,000 --> 00:24:43,533
we now have our information
697
00:24:43,533 --> 00:24:45,366
just for our winter months
698
00:24:45,666 --> 00:24:48,433
how can we get all of our information back
699
00:24:49,566 --> 00:24:50,366
you see that
700
00:24:50,366 --> 00:24:53,666
there's a little filter icon here in this pull down tab
701
00:24:53,666 --> 00:24:56,566
that's telling us that we're filtering on month
702
00:24:56,566 --> 00:24:58,666
so we would go back into month
703
00:24:58,866 --> 00:25:01,499
to clear that out and restore data set
704
00:25:01,866 --> 00:25:02,366
you can see
705
00:25:02,366 --> 00:25:05,199
we also have this option of clearing out that filter
706
00:25:05,200 --> 00:25:08,066
up here on the data menu
707
00:25:08,166 --> 00:25:09,866
and we can do that in either place
708
00:25:09,866 --> 00:25:11,566
and as soon as we clear that out
709
00:25:11,600 --> 00:25:13,200
we get all of our data back
710
00:25:13,200 --> 00:25:14,566
like nothing happened
711
00:25:14,600 --> 00:25:15,566
and so hopefully
712
00:25:15,566 --> 00:25:16,733
that's reassuring
713
00:25:16,733 --> 00:25:20,099
that we're not actually changing or losing data
714
00:25:20,133 --> 00:25:22,899
we're just changing what Excel is showing us
715
00:25:22,900 --> 00:25:24,433
temporarily if we want
716
00:25:25,000 --> 00:25:26,566
what are the kinds of filtering
717
00:25:26,566 --> 00:25:28,299
can we do in a data set like this
718
00:25:28,300 --> 00:25:30,466
well we've got a lot of numerical data
719
00:25:30,666 --> 00:25:33,399
and we just did a filter based on text and says
720
00:25:33,400 --> 00:25:36,400
let's take a look at how we can filter based on numbers
721
00:25:36,700 --> 00:25:38,100
we're dealing with temperature
722
00:25:38,400 --> 00:25:41,933
and let's suppose that we want to have an outdoor event
723
00:25:41,933 --> 00:25:43,666
maybe a party or a wedding
724
00:25:43,666 --> 00:25:46,166
and we wanna make sure the weather is neither too hot
725
00:25:46,166 --> 00:25:48,433
nor too cold to have this outdoor event
726
00:25:48,566 --> 00:25:50,499
which months might be good for this
727
00:25:50,966 --> 00:25:53,066
I'm gonna arbitrarily define not too hot
728
00:25:53,066 --> 00:25:55,966
not too cold is somewhere between 60 degrees Fahrenheit
729
00:25:55,966 --> 00:25:57,966
and 80 degrees Fahrenheit during the day
730
00:25:57,966 --> 00:26:00,199
that sounds like really good temperatures to me
731
00:26:00,300 --> 00:26:03,300
so how can we filter our data
732
00:26:03,300 --> 00:26:07,000
so we can only see high temperatures between 60 and 80
733
00:26:07,066 --> 00:26:08,666
let's go into our pull down menu here
734
00:26:08,666 --> 00:26:09,666
and figure this out
735
00:26:10,766 --> 00:26:11,899
now if we approach this
736
00:26:11,900 --> 00:26:14,133
the way we approached our text filtering
737
00:26:14,133 --> 00:26:15,733
we'll see that all we can do is
738
00:26:15,733 --> 00:26:19,166
we can just select or deselect individual
739
00:26:19,166 --> 00:26:20,299
specific temperatures
740
00:26:20,300 --> 00:26:22,533
and so this is the same thing as filtering by months
741
00:26:22,533 --> 00:26:25,599
really we're just gonna eliminate those specific rows
742
00:26:25,600 --> 00:26:26,500
and so there's no point
743
00:26:26,500 --> 00:26:27,566
in doing this manually
744
00:26:27,566 --> 00:26:28,599
that's not gonna help us
745
00:26:28,600 --> 00:26:30,466
especially if we have a really big data set
746
00:26:30,466 --> 00:26:31,466
and remember
747
00:26:31,500 --> 00:26:34,066
most of these tools are gonna become most useful
748
00:26:34,066 --> 00:26:35,866
when we have large data sets
749
00:26:35,866 --> 00:26:37,366
so what else can we do
750
00:26:37,500 --> 00:26:40,800
we can go into this number filters menu
751
00:26:40,800 --> 00:26:44,300
a sub menu and now we have all these different features
752
00:26:44,300 --> 00:26:45,666
equals does not equal
753
00:26:45,666 --> 00:26:47,266
greater than less than
754
00:26:47,600 --> 00:26:50,900
and here is one that's really gonna be helpful between
755
00:26:51,400 --> 00:26:52,866
as I just said
756
00:26:52,866 --> 00:26:57,366
we want to find days that are warmer
757
00:26:57,566 --> 00:26:59,233
or 60 degrees
758
00:27:00,766 --> 00:27:01,566
and
759
00:27:02,800 --> 00:27:04,600
cooler or 80 degrees
760
00:27:04,600 --> 00:27:07,066
so we want values between 60 and 80
761
00:27:07,066 --> 00:27:09,266
including those numbers we'll just say
762
00:27:09,933 --> 00:27:11,499
and now if we click that
763
00:27:12,900 --> 00:27:15,266
fantastic our data set gets smaller
764
00:27:15,266 --> 00:27:17,133
that's exactly what we wanted to see
765
00:27:17,133 --> 00:27:19,999
we wanna see that sub set of months
766
00:27:20,000 --> 00:27:22,166
that meet the criteria that I set
767
00:27:22,166 --> 00:27:24,266
for the kind of party that I wanna have outside
768
00:27:24,500 --> 00:27:25,700
and so now we know
769
00:27:25,700 --> 00:27:28,066
we can schedule these events pretty confidently
770
00:27:28,066 --> 00:27:29,133
in March April
771
00:27:29,133 --> 00:27:30,099
May October
772
00:27:30,100 --> 00:27:30,866
or November
773
00:27:30,866 --> 00:27:33,099
so that's nice to know we've got these options
774
00:27:33,500 --> 00:27:34,466
and as usual
775
00:27:34,466 --> 00:27:37,199
we can just really quickly clear this out
776
00:27:37,266 --> 00:27:38,866
and get all of our data back
777
00:27:39,300 --> 00:27:40,600
so filtering as you see
778
00:27:40,600 --> 00:27:41,200
is a really
779
00:27:41,200 --> 00:27:43,900
really great way to draw new meaning from our data
780
00:27:43,900 --> 00:27:45,166
to simplify our data
781
00:27:45,166 --> 00:27:47,233
and to answer specific questions
782
00:27:47,400 --> 00:27:49,333
this is very similar to using some more
783
00:27:49,333 --> 00:27:50,866
advanced functions in Excel
784
00:27:50,933 --> 00:27:52,733
and after you do some practice with filtering
785
00:27:52,733 --> 00:27:54,733
we're gonna move on to those more advanced functions
786
00:27:54,733 --> 00:27:56,799
where we can answer similar questions
787
00:27:57,366 --> 00:27:59,699
before we move on from filtering
788
00:27:59,700 --> 00:28:01,900
let's look at another application of filtering
789
00:28:01,900 --> 00:28:02,866
and you may remember
790
00:28:02,866 --> 00:28:04,799
that when I was showing you how to plot
791
00:28:04,800 --> 00:28:08,733
and first introducing the idea of charts and graphs
792
00:28:08,733 --> 00:28:11,266
and looking at a bar or column chart
793
00:28:11,466 --> 00:28:12,466
I was actually
794
00:28:12,466 --> 00:28:13,766
in real time
795
00:28:13,766 --> 00:28:15,766
adjusting what I was showing you
796
00:28:16,866 --> 00:28:20,166
this is one potential application of filtering
797
00:28:20,166 --> 00:28:20,933
that can be really
798
00:28:20,933 --> 00:28:21,866
really useful
799
00:28:21,933 --> 00:28:23,333
now what I was actually doing
800
00:28:23,333 --> 00:28:25,866
when I was doing that demonstration is
801
00:28:25,866 --> 00:28:29,699
I was manually changing which values I was showing you
802
00:28:29,700 --> 00:28:32,100
just by dragging the selected data
803
00:28:32,333 --> 00:28:35,599
and the chart updates in real time based on that
804
00:28:35,600 --> 00:28:36,933
and so that's a neat tool
805
00:28:36,933 --> 00:28:38,133
that anytime you want
806
00:28:38,133 --> 00:28:39,666
you're not sure which data
807
00:28:39,666 --> 00:28:41,233
you should be showing in your chart
808
00:28:41,466 --> 00:28:43,599
you can just change your selection
809
00:28:43,600 --> 00:28:45,533
and the chart will update in real time
810
00:28:45,533 --> 00:28:46,699
and that's really helpful
811
00:28:46,933 --> 00:28:48,633
but another thing we can do
812
00:28:48,700 --> 00:28:51,266
is we can apply a filter
813
00:28:51,933 --> 00:28:54,833
we can apply a filter to the data
814
00:28:54,866 --> 00:28:56,366
that are used in our chart
815
00:28:56,566 --> 00:28:57,899
and if we do that
816
00:28:57,900 --> 00:29:00,466
then the chart will update in a similar fashion
817
00:29:00,466 --> 00:29:03,099
so if I go back to what we just did in filtering
818
00:29:03,100 --> 00:29:03,900
and I say okay
819
00:29:03,900 --> 00:29:07,500
actually we only wanna look at those winter months
820
00:29:07,666 --> 00:29:10,366
here we have those winter months filtered down
821
00:29:10,366 --> 00:29:11,699
to just those three
822
00:29:11,700 --> 00:29:13,300
everything else has been removed
823
00:29:13,300 --> 00:29:14,800
and our chart has updated
824
00:29:14,900 --> 00:29:16,466
just as we expected it would
825
00:29:16,800 --> 00:29:18,666
now we would wanna change some things about this
826
00:29:18,666 --> 00:29:20,899
it's really squat and wide
827
00:29:20,966 --> 00:29:23,066
and so we would make some modifications
828
00:29:23,066 --> 00:29:26,466
but this is a great way to really easily assess
829
00:29:26,466 --> 00:29:28,799
the effects of including different data sets
830
00:29:28,800 --> 00:29:30,866
and data points in your chart
831
00:29:30,866 --> 00:29:32,366
and looking at how you can change it
832
00:29:32,366 --> 00:29:33,799
to show different things
833
00:29:33,966 --> 00:29:35,933
so again this is an important integration
834
00:29:35,933 --> 00:29:37,399
of a few of the different skills we had
835
00:29:37,400 --> 00:29:39,233
you can use filtering
836
00:29:39,266 --> 00:29:40,599
when you're making a chart
837
00:29:40,600 --> 00:29:41,766
to show different things
838
00:29:41,766 --> 00:29:43,466
and learn different things about your data
839
00:29:43,766 --> 00:29:45,566
we're just working with filtering
840
00:29:45,733 --> 00:29:48,766
filtering is one way of reducing our data
841
00:29:48,766 --> 00:29:49,933
and only looking at our data
842
00:29:49,933 --> 00:29:51,866
if it meets certain conditions
843
00:29:52,066 --> 00:29:54,166
there's another way we can do this
844
00:29:54,366 --> 00:29:56,599
we can have conditional functions
845
00:29:56,600 --> 00:29:59,400
and conditional functions will do the same thing
846
00:29:59,566 --> 00:30:01,133
except conditional functions
847
00:30:01,133 --> 00:30:02,466
can sometimes be more powerful
848
00:30:02,466 --> 00:30:03,399
because they can apply
849
00:30:03,400 --> 00:30:06,700
some kind of mathematical function to our data
850
00:30:06,700 --> 00:30:08,900
that only meet certain conditions
851
00:30:09,466 --> 00:30:12,466
just as we're using filtering to find winter months
852
00:30:12,466 --> 00:30:15,399
or find months with temperatures within a certain range
853
00:30:15,400 --> 00:30:16,533
we can do the same thing
854
00:30:16,533 --> 00:30:17,899
using these conditional functions
855
00:30:17,900 --> 00:30:18,766
I'm gonna show you these
856
00:30:18,766 --> 00:30:20,499
because they're a very powerful tool
857
00:30:20,500 --> 00:30:22,500
for understanding what's going on with our data
858
00:30:23,066 --> 00:30:24,266
as I mentioned early on
859
00:30:24,266 --> 00:30:24,766
with functions
860
00:30:24,766 --> 00:30:26,399
there are hundreds of functions in Excel
861
00:30:26,400 --> 00:30:28,800
and there are even several conditional functions
862
00:30:28,800 --> 00:30:30,166
we're going to use count
863
00:30:30,166 --> 00:30:32,033
if we're going to count
864
00:30:32,100 --> 00:30:34,800
how many data points match the criteria
865
00:30:34,800 --> 00:30:35,733
we're setting
866
00:30:35,733 --> 00:30:37,399
we could also do some if
867
00:30:37,400 --> 00:30:39,200
we could add data points to each other
868
00:30:39,200 --> 00:30:40,533
if they meet conditions
869
00:30:40,533 --> 00:30:43,266
average if we could take averages
870
00:30:43,266 --> 00:30:45,033
but only for some of our data
871
00:30:45,600 --> 00:30:48,566
this is a very similar idea to filtering
872
00:30:48,566 --> 00:30:50,599
it just adds in this additional step
873
00:30:50,600 --> 00:30:52,166
of adding some math to it
874
00:30:52,166 --> 00:30:53,333
if we'd like to do these
875
00:30:53,333 --> 00:30:55,066
additional mathematical functions
876
00:30:55,166 --> 00:30:56,333
in this case as I said
877
00:30:56,333 --> 00:30:58,299
we're going to be focusing on count if
878
00:30:58,400 --> 00:30:59,300
and I'm going to show you
879
00:30:59,300 --> 00:31:01,000
how these conditional functions work
880
00:31:01,733 --> 00:31:02,599
as you know
881
00:31:02,600 --> 00:31:04,333
when you're putting a function into its cell
882
00:31:04,333 --> 00:31:05,899
we immediately start getting clues
883
00:31:05,900 --> 00:31:09,066
on what kind of information it needs for its arguments
884
00:31:09,466 --> 00:31:10,199
for count if
885
00:31:10,200 --> 00:31:12,833
it wants to know what range it's looking in
886
00:31:13,066 --> 00:31:15,466
and because we're saying right now
887
00:31:15,466 --> 00:31:16,099
in this case
888
00:31:16,100 --> 00:31:18,866
we're going to be looking for good weather months
889
00:31:18,866 --> 00:31:22,133
which I'm defining as months with a low temperature
890
00:31:22,133 --> 00:31:23,833
greater than 40 degrees
891
00:31:24,933 --> 00:31:25,899
so we'll select
892
00:31:25,900 --> 00:31:28,566
the range of data associated with low temperature
893
00:31:29,333 --> 00:31:32,566
now how am I going to define this good weather month
894
00:31:32,566 --> 00:31:34,333
I have it right there next to it
895
00:31:34,333 --> 00:31:37,999
we want low temperatures to be greater than 40 degrees
896
00:31:38,000 --> 00:31:39,700
we put the condition
897
00:31:39,700 --> 00:31:42,900
we put the criteria in quotes
898
00:31:43,066 --> 00:31:44,899
and now as soon as I hit enter
899
00:31:44,900 --> 00:31:46,966
Excel's going to do this count for us
900
00:31:47,066 --> 00:31:48,333
and it's going to say okay
901
00:31:48,333 --> 00:31:50,599
there are seven months that meet your condition
902
00:31:50,800 --> 00:31:53,666
of having low temperatures greater than 40 degrees
903
00:31:54,066 --> 00:31:55,899
remember we could be doing this ourselves
904
00:31:55,900 --> 00:31:57,166
this is a small data set
905
00:31:57,166 --> 00:31:58,066
and that's the point
906
00:31:58,066 --> 00:31:59,266
you can check this visually
907
00:31:59,266 --> 00:32:01,966
if you want to confirm that Excel is correct
908
00:32:02,000 --> 00:32:04,133
but these functions become very powerful
909
00:32:04,133 --> 00:32:05,799
when you don't wanna be counting at yourself
910
00:32:05,800 --> 00:32:06,766
when you're looking at rose
911
00:32:06,766 --> 00:32:08,533
with hundreds of data points
912
00:32:08,533 --> 00:32:10,099
that's what we're going to be doing soon
913
00:32:11,066 --> 00:32:12,899
now another condition that I'm going to say
914
00:32:12,900 --> 00:32:14,466
is having good weather months
915
00:32:14,466 --> 00:32:15,399
I don't like really
916
00:32:15,400 --> 00:32:16,466
really hot weather
917
00:32:16,666 --> 00:32:17,799
I like it warm
918
00:32:18,166 --> 00:32:19,299
so I'm also going to say
919
00:32:19,300 --> 00:32:20,933
we only want once
920
00:32:20,933 --> 00:32:22,999
where the high temperature
921
00:32:23,333 --> 00:32:24,533
and so now we're selecting
922
00:32:24,533 --> 00:32:26,066
this high temperature range
923
00:32:26,566 --> 00:32:27,599
is going to be
924
00:32:28,000 --> 00:32:30,433
less than 80 degrees Fahrenheit
925
00:32:32,933 --> 00:32:34,999
and there are eight of those months
926
00:32:35,666 --> 00:32:38,366
so this is one quick example
927
00:32:38,866 --> 00:32:40,399
leading you into understanding
928
00:32:40,400 --> 00:32:42,466
how these conditional functions work
929
00:32:42,466 --> 00:32:44,399
we just counted up months
930
00:32:44,900 --> 00:32:47,800
that meet our condition of having lows greater than 40
931
00:32:47,866 --> 00:32:49,399
we counted up months
932
00:32:50,000 --> 00:32:54,366
leading our condition of having highs less than 80
933
00:32:54,566 --> 00:32:56,666
but what we haven't done yet is
934
00:32:56,666 --> 00:33:00,799
we haven't figured out if any months meet both criteria
935
00:33:01,766 --> 00:33:03,666
that is what's going to come next
936
00:33:03,666 --> 00:33:05,266
after you have a little opportunity
937
00:33:05,266 --> 00:33:07,999
to practice using some of these conditional functions
938
00:33:08,733 --> 00:33:10,266
so we've been doing a great job
939
00:33:10,266 --> 00:33:12,499
working with these conditional functions so far
940
00:33:12,566 --> 00:33:14,099
have a good understanding of count if
941
00:33:14,100 --> 00:33:15,933
presumably you've gotten to feel
942
00:33:15,933 --> 00:33:17,999
for the fact that you could use some if
943
00:33:18,000 --> 00:33:19,433
and average if also
944
00:33:19,533 --> 00:33:20,566
and what we looked at is
945
00:33:20,566 --> 00:33:22,299
we looked at how we're setting conditions
946
00:33:22,300 --> 00:33:24,200
for this count function right
947
00:33:24,566 --> 00:33:26,499
but we still have more work we wanna do
948
00:33:26,500 --> 00:33:27,766
we actually wanna look at
949
00:33:27,766 --> 00:33:29,633
two conditions at the same time
950
00:33:30,066 --> 00:33:31,099
we know in my area
951
00:33:31,100 --> 00:33:32,666
that we have seven months
952
00:33:32,666 --> 00:33:34,699
or low temperatures are greater than 40
953
00:33:34,900 --> 00:33:36,166
and we have eight months
954
00:33:36,166 --> 00:33:37,999
or high temperatures are less than eighty
955
00:33:38,333 --> 00:33:41,599
but how many months fit both of these criteria
956
00:33:41,600 --> 00:33:42,666
both of these conditions
957
00:33:42,666 --> 00:33:44,266
we can't do this with count if
958
00:33:44,466 --> 00:33:45,766
this means we need to work
959
00:33:45,766 --> 00:33:47,833
with another set of conditional functions
960
00:33:47,933 --> 00:33:50,133
these are the IFS functions
961
00:33:50,133 --> 00:33:52,066
there's an s on these
962
00:33:52,066 --> 00:33:54,033
and so I'll just type this in here
963
00:33:55,133 --> 00:33:56,699
I will type it in properly
964
00:33:57,000 --> 00:33:57,900
and as we see here
965
00:33:57,900 --> 00:34:01,200
count IFS will count
966
00:34:01,466 --> 00:34:04,766
cells specified by a given set of conditions
967
00:34:04,766 --> 00:34:05,733
so not just one
968
00:34:05,733 --> 00:34:07,999
so we can use this to tackle our problem here
969
00:34:08,000 --> 00:34:09,466
and this is a really neat set of functions
970
00:34:09,466 --> 00:34:11,099
I actually use these quite a bit
971
00:34:11,133 --> 00:34:12,366
when I'm summarizing data
972
00:34:12,366 --> 00:34:14,299
as an alternative to filtering
973
00:34:14,300 --> 00:34:16,000
which is another way of doing this
974
00:34:16,733 --> 00:34:20,866
as usual we wanna set our range for our first criterion
975
00:34:20,966 --> 00:34:23,166
and again that's low temperatures
976
00:34:23,166 --> 00:34:24,899
so I select that column
977
00:34:25,000 --> 00:34:25,933
and as we know
978
00:34:25,933 --> 00:34:27,499
we want those temperatures
979
00:34:27,500 --> 00:34:29,566
to be greater than 40 degrees
980
00:34:29,900 --> 00:34:32,400
but now we're looking at our second condition
981
00:34:32,400 --> 00:34:33,433
that we're setting
982
00:34:34,166 --> 00:34:36,033
still within the same function
983
00:34:36,166 --> 00:34:37,799
but it's a separate condition
984
00:34:37,900 --> 00:34:40,000
so we need to be looking in the separate column
985
00:34:40,000 --> 00:34:42,600
because now we're talking about high temperatures
986
00:34:42,600 --> 00:34:46,066
we want those high temperatures to be less than eighty
987
00:34:46,800 --> 00:34:48,333
count ifs 4
988
00:34:48,333 --> 00:34:50,866
check both of these boxes at the same time
989
00:34:50,866 --> 00:34:52,866
January is the low
990
00:34:52,866 --> 00:34:53,966
greater than 40
991
00:34:53,966 --> 00:34:54,933
is the highest
992
00:34:54,933 --> 00:34:56,199
than 80 yes or no
993
00:34:56,200 --> 00:34:57,300
and it'll go down the line
994
00:34:57,300 --> 00:34:58,833
and so let's see what we get here
995
00:34:59,333 --> 00:35:00,866
three months total
996
00:35:00,900 --> 00:35:02,600
across this data set
997
00:35:02,600 --> 00:35:04,266
that meet both of those criteria
998
00:35:04,266 --> 00:35:05,799
so now we can be confident
999
00:35:05,800 --> 00:35:07,666
that we have three months across the year
1000
00:35:07,666 --> 00:35:09,166
where lows are greater than 40
1001
00:35:09,166 --> 00:35:10,399
and less than 80
1002
00:35:10,400 --> 00:35:11,966
we could have done this manually
1003
00:35:11,966 --> 00:35:12,999
but even just checking
1004
00:35:13,000 --> 00:35:14,333
two different things at the same time
1005
00:35:14,333 --> 00:35:15,333
can be a real challenge
1006
00:35:15,333 --> 00:35:17,666
so these count IPS functions
1007
00:35:17,700 --> 00:35:18,266
can be very
1008
00:35:18,266 --> 00:35:19,133
very useful
1009
00:35:19,133 --> 00:35:21,499
they're great alternative to filtering
1010
00:35:21,500 --> 00:35:22,066
or something
1011
00:35:22,066 --> 00:35:24,433
that you can use in combination with filtering
1012
00:35:24,900 --> 00:35:25,666
and remember
1013
00:35:25,666 --> 00:35:27,999
something I've been encouraging you to do more and more
1014
00:35:28,000 --> 00:35:29,866
now that we're getting more advanced material
1015
00:35:29,866 --> 00:35:31,166
is be thinking of ways
1016
00:35:31,166 --> 00:35:32,199
that you can integrate
1017
00:35:32,200 --> 00:35:33,133
these different skills
1018
00:35:33,133 --> 00:35:34,333
and use them together
1019
00:35:34,333 --> 00:35:35,666
and that's when you're going to become
1020
00:35:35,666 --> 00:35:37,899
a very powerful Excel user
1021
00:35:38,533 --> 00:35:40,699
we're almost finished with Week 3
1022
00:35:40,700 --> 00:35:42,066
soon I'm gonna wrap up
1023
00:35:42,066 --> 00:35:43,733
and recap what we've done this week
1024
00:35:43,733 --> 00:35:45,333
and then you'll move on to an assignment
1025
00:35:45,333 --> 00:35:46,699
where you'll have the opportunity
1026
00:35:46,700 --> 00:35:47,900
to integrate
1027
00:35:47,933 --> 00:35:48,799
many of these skills
1028
00:35:48,800 --> 00:35:49,866
you've been working on
1029
00:35:49,933 --> 00:35:51,133
great job so far
74169
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