Deleting Rows

Quick reference

• 00:05 Okay, in the last video, we looked at how to remove columns.
• 00:08 In this video, we're going to look at how to remove rows.
• 00:11 And it's actually very similar.
• 00:13 In fact, it's almost exactly the same, but
• 00:15 there's some slight variations that make it worth talking about.
• 00:18 So that's what we're going to do in this video.
• 00:20 So, okay, we want to get rid of a row.
• 00:22 And let's say C, I really don't like the look of C, we need to get rid of it.
• 00:26 How do we do that?
• 00:26 Well, we just call my_df.drop, and
• 00:30 then we just pass in whatever row we want.
• 00:34 Now, remember in the last video when we tried to do this with just putting Sunday
• 00:38 in, we got an error.
• 00:40 If we do this in this video with a row, we don't get an error.
• 00:44 Why is that?
• 00:45 Well, remember our axis, these column headers are axis 1,
• 00:50 the row headings are axis 0.
• 00:53 And if we Shift+Tab right here, we can see the default is axis 0.
• 01:00 So if we're going to be removing something from axis 0,
• 01:05 we don't actually have to type in axis 0, because it's the default.
• 01:09 So that's kind of interesting.
• 01:11 I have mixed feelings about that.
• 01:13 I think, especially for beginners, you should just get in the habit of typing in
• 01:17 axis=0 anyway, even though you don't have to, why?
• 01:23 Because axes aren't really intuitive, right?
• 01:26 You're going to forget that these top columns are axis 1,
• 01:29 you're going to forget that these rows are axis 0.
• 01:32 But if you get in the habit of always typing in axis,
• 01:35 you're less likely to forget over time.
• 01:38 So I would suggest that you always type in your axis.
• 01:41 Now again, we can come down here and go my_df and run this again,
• 01:46 and our troublesome C row is puffed back up.
• 01:50 That's because we didn't designate
• 01:53 right here that we wanted to do this in place, right?
• 01:56 So we can always go inplace=True.
• 02:00 And now if we run this and
• 02:04 then run our data frame again, now C has been deleted forever more, right?
• 02:10 So that's how to drop rows.
• 02:12 Pretty simple.
• 02:12 Now, I mentioned in the last video that we like this inplace=True thing,
• 02:17 because it keeps us from deleting things that we might not want to delete.
• 02:21 And when you do use inplace=True, it deletes it forever, but not really.
• 02:27 I mean, yes, but no, there's a kind of a hacky way around that.
• 02:30 And I want to show you that right now.
• 02:32 As long as your notebook is still open, and you've got all these old cells, right?
• 02:37 Even though it's been deleted down here,
• 02:39 we can always come back up to the top and just run all these cells again.
• 02:43 And it will generate our deleted stuff again, right?
• 02:46 So I'm just going to hold down the Shift key,
• 02:49 click on one of these cells and hit Enter.
• 02:52 And I'm continuing to hold down the Shift key, and I'll just keep hitting Enter,
• 02:56 Enter, Enter, Enter.
• 03:00 And we can come down, Enter, Enter, Enter.
• 03:06 Now, if we Shift run this one again, boom, our C has come back.
• 03:10 Now these numbers will have sort of changed a little bit,
• 03:13 that's because up here remember, we created all these numbers randomly.
• 03:17 So if we run these cells again, it will generate new random numbers, which may or
• 03:21 may not be what we want.
• 03:23 We can get around that if we use a seed, we didn't actually use a seed, and
• 03:26 I didn't even talk about it.
• 03:27 But when you create a random number, you can issue a seed and
• 03:32 if you had done that, then it will generate the same random numbers if
• 03:35 you use that same seed in the future.
• 03:37 Sort of beyond the scope all of this, we don't really care about that, but
• 03:40 just something to keep in mind.
• 03:41 If you were using actual data and you weren't creating random numbers, well,
• 03:45 then if you ran these cells again, in that case, they would be the same.
• 03:49 And then down here, your numbers would still be the same and
• 03:52 you'd have your C row back.
• 03:54 So kind of a hacky way around it.
• 03:55 I mean, it's not a great thing to do.
• 03:57 But in a pinch, if you accidentally inplace deleted something you didn't
• 04:01 want to delete, you could always scroll back up.
• 04:03 And it's kind of one of the nice things about Jupyter Notebooks.
• 04:06 It has all that stuff there as long as you haven't deleted it.
• 04:10 As we're going along, I'm sort of deleting somethings just to clean things up.
• 04:14 As long as you haven't done that, you're good to go.
• 04:16 So that's all for deleting rows, deleting columns.
• 04:19 In the next video,
• 04:20 I want to show you how to select rows, couple of ways that you can select rows.
• 04:23 So that you can actually grab data and start doing stuff with it, and
• 04:27 that'll be in the next video.

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