Selecting Subsets of Rows and Columns

Quick reference

• 00:05 Okay, in this video, I want to talk about selecting specific points and
• 00:09 also selecting subsets of rows and columns.
• 00:13 So what do I mean by specific points?
• 00:16 Well, for instance, if we want this thing right here, so if we want B Wednesday,
• 00:21 this -0.201, how can we grab that specific thing?
• 00:26 And by subsets, I mean any sort of subset, so
• 00:30 say we want Monday from A and Wednesday from A, and
• 00:34 Monday from D and Wednesday from D, how do we grab that subset?
• 00:39 So those two things are what we're going to talk about in this video.
• 00:42 So to grab a specific point, we can go ahead and go my_df.loc,
• 00:47 like we did in the last video.
• 00:49 But instead of just passing one row, we need a row and a column.
• 00:57 You can use double or single quotation marks, so
• 01:01 we need a row and a column, all right?
• 01:04 So for row, we want B, so we could change this to B.
• 01:08 And for column, we want Wednesdays, so we change this to Wednesday.
• 01:13 I just wanted to show you the format so it's not confusing, and sure enough,
• 01:18 we get -0.2012 blah, blah, blah, 0.201217, you notice this one's much larger,
• 01:25 it just rounded on this one and here we get the whole thing.
• 01:29 So what exactly is this data that just got passed to us?
• 01:33 Well, let's run a type and see, this is kind of interesting.
• 01:36 It's a numpy.float64,
• 01:39 floats are decimals long decimals, but they're kind of interesting, it's a NumPy.
• 01:44 NumPy has half back up, remember from the very beginning of the course, Pandas is
• 01:49 built on NumPy, so we see these NumPy things popping up, so that's pretty cool.
• 01:55 So that's how to grab a specific point, fairly simple.
• 01:58 To grab a subset is very sort of similar,
• 02:03 let's just go my_df.loc again.
• 02:07 And before we did a row column,
• 02:10 we're going to do the same thing, but instead of passing just one point,
• 02:13 we're going to pass a list with several points for both of these.
• 02:18 So for our row, say we want row A and row D, so we could just pass A,
• 02:23 D, and for our columns, say we want Monday,
• 02:31 And Wednesday.
• 02:34 So if we run this, boom, we get Monday and Wednesday for A and D.
• 02:38 And we can confirm, we can go 0.468 and that's 0.468, -1.046,
• 02:45 -1.046, and the same thing for D, -1004, there we go.
• 02:50 And Wednesday, it's 1.894, 1.894, so very cool and pretty simple.
• 02:56 Now, we can do more than one or more than two, we can do A, D, and C.
• 03:00 Now, if we run this, check it out, we get A, D, and C.
• 03:05 But it's in the exact order that we put here,
• 03:09 not in the order that it's listed here.
• 03:11 So here it's A, B, C, D, so you would think it should be A, B, C, D, or
• 03:16 at least A, C, D.
• 03:17 But it's not, it's designated just by how we put it in here.
• 03:20 So if we wanted it in the original order,
• 03:22 we would have to put it like that inside of here, and then we get A, C, and D.
• 03:27 Same thing here, we can add as many of these as we want, if we want the total
• 03:32 two, we can run this and notice I mixed quotation marks.
• 03:37 So these are double quotes and these are single quotes, you can do both like this.
• 03:41 You can do all single quotes, you can do all double quotes,
• 03:44 it really doesn't matter.
• 03:46 That's how to grab both specific points from our data frame and subsections.
• 03:53 In the next video, we'll look at conditional selection,
• 03:56 making selections based on certain conditions.

Lesson notes are only available for subscribers.