Creating New Columns

Quick reference

• 00:05 Okay so, we created our data frame we've got Monday, Tuesday, and Wednesday, and
• 00:09 we're feeling pretty good about ourselves because it's looking pretty good here now.
• 00:13 But then we realize, we made a mistake, we need a Sunday column too,
• 00:17 how do we add an extra column once our data frame has already been created?
• 00:21 Well, that's what we're going to do in this video, and it's actually really,
• 00:24 really simple.
• 00:24 We can just come down here and go my_df, and
• 00:28 then pass in whatever we want to create.
• 00:31 So let's create a column called Sun, and
• 00:34 then we just want to set this equal to whatever we want.
• 00:37 So we can come up here to our see this randn guy that we did earlier,
• 00:42 and just copy this code, and let's generate some more random data.
• 00:48 Now remember, this is rows and columns, so we need four rows, but
• 00:54 we only want one column because we're creating one column called Sun.
• 00:59 And so we can do that, hit Shift+Enter to run this.
• 01:02 Now if we call our data frame again, we see now we've got Sun, so
• 01:07 very cool and very, very simple.
• 01:10 So that's one way you can do it.
• 01:12 Now, a lot of times when you want to create new columns,
• 01:14 it's because you want to do something, you want to add up all your columns.
• 01:18 So you want to find the average of all your columns, or you want to find the min
• 01:21 or the max of all your columns, or have all your data in each column.
• 01:25 So how do we do that?
• 01:26 Well, let's create another column, and let's call it Total.
• 01:30 So we just go through the same process we just did, we just go my_df, and
• 01:34 then just pass in whatever we want the column header to be right?
• 01:38 And then set this equal to whatever we want.
• 01:41 So let's say we want to add up Monday, Tuesday, and
• 01:46 Wednesday, and output that into the Total, or
• 01:50 we could just call my_df and then Mon, and then +, right?
• 01:56 So we can go my_df, and then we want to pass Tues,
• 02:03 and then again, + my_df and then Wed.
• 02:09 Remember these are case sensitive, so if it's W in Wednesday,
• 02:13 you need to type in W here.
• 02:15 So if we Shift+Enter, and run this, and
• 02:18 now pull up our my_df again, we see this, new Total.
• 02:23 And is this a correct number?
• 02:25 I don't know, let's go ahead and confirm that.
• 02:27 So I'm just going to copy and paste all this stuff down here and
• 02:31 let's just do some quick math.
• 02:33 We can do this in Jupyter Notebooks, this is not a Pandas thing,
• 02:36 this is just Jupyter Notebooks in Python, we can add, right?
• 02:40 So if we Shift run to see what this is,
• 02:44 it's -1.52263, whatever, and
• 02:49 here we see -1.52263, whatever.
• 02:54 So it looks like that's correct.
• 02:56 So we did adding, you could just as easily do subtraction, right?
• 03:04 Or multiplication, or whatever you want.
• 03:06 And as we go through the course, we'll learn about how to do different things
• 03:10 like finding the mean, finding the average, finding the min, the max,
• 03:14 the standard deviations, all the cool statistical things you would want to do.
• 03:18 But it's just as easy to create new columns and start doing stuff.
• 03:22 So very cool, let's get rid off of this just to clear things a bit, and
• 03:26 that's all there's to it.
• 03:28 Now, we created this Sun column, may be we didn't want to create that Sunday column.
• 03:34 We've got Monday, Tuesday and Wednesday and we're adding them all together and
• 03:37 outputing the total in this Total column,
• 03:40 we just sort of forgot about this Sun column.
• 03:42 So okay, we've decided this is a mistake, how do we get rid of this Sun column?
• 03:46 Well, we'll look at that in the next video.

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