Pandas Series

Quick reference

• 00:05 Okay, in this video I want to talk about Pandas series.
• 00:07 Now, there are two sort of main workhorses when it comes to Pandas.
• 00:11 The first one is a series and the second one is the data frame.
• 00:15 And we're going to be doing most of the work throughout this course with
• 00:18 the data frame.
• 00:19 But we need to talk about the series at least a little bit because data frames
• 00:22 are made up of series.
• 00:23 And they're just sort of important to understand.
• 00:25 So a series is very much like a NumPy array,
• 00:28 except that it has an index that we can label and play around with.
• 00:33 So a series are built off of NumPy arrays, so that's kind of important.
• 00:37 And I should notice up here we've still got our import numpy as np.
• 00:40 It's because Pandas uses NumPy for just a ton of stuff.
• 00:45 So anytime you want to use Pandas, yeah, you definitely have to also import NumPy.
• 00:49 So let's create one of these series real quick, and
• 00:53 let's create a Python list and call it label.
• 00:56 And let's just call this give us some labels of Monday as go Tuesday and
• 01:03 Wednesday, I'll just use abbreviations.
• 01:09 And then we can create some data, and let's just call this,
• 01:14 I don't know, 41, 32, and 16.
• 01:17 Now, we can create an array and let's go, let's just call it my_array.
• 01:23 And this is going to be a NumPy array, so np.array, and we can pass in that data.
• 01:30 I guess we could've put this in here, but I like to separate them out, just so
• 01:33 that we can understand exactly what's going on.
• 01:36 So all right, let's create a simple series.
• 01:38 So I'm going to call pd.Series.
• 01:42 Remember, right here we're calling pd, Panda as pd.
• 01:46 And then let's start out just by putting in our array and just see what this does.
• 01:54 So if we run this, Shift+Enter to run this, we see here is our series.
• 01:58 And here's our data 41, 32, and 16.
• 02:01 And over here, we have our index.
• 02:03 And there are no labels, it's just numbered.
• 02:06 And like all Python lists, they start at 0, 1, and 2, right?
• 02:10 So it always starts at 0.
• 02:11 So that's cool.
• 02:12 This is strictly speaking a series.
• 02:14 And you'll notice, it's different than a NumPy array because it has this index and
• 02:18 these labels.
• 02:18 But here we can actually designate what we want those labels to be.
• 02:21 So we can just slap a comma in here and type in the label.
• 02:25 Then we Shift+Enter, we get Monday, Tuesday, Wednesday.
• 02:28 Very cool and pretty easy to do.
• 02:30 So we can determine what this is by calling type.
• 02:35 And then we could just grab this whole thing here and paste it in, Shift+Enter.
• 02:42 Yes, this is in fact a series, that's kind of interesting to know.
• 02:45 So, okay, this is a series, right?
• 02:49 It's a data type of 32 integers of 32.
• 02:52 We could also, instead of putting numbers through here,
• 02:55 you could pass anything you want into a series, we could just do labels.
• 02:58 So we just put our label in there and shift around it.
• 03:01 Now we get Monday, Tuesday, and Wednesday as the data in our series and
• 03:06 we go back to having our numbered index.
• 03:10 So that's kind of fun.
• 03:13 Let's change this back.
• 03:15 So we can name this, we could just call this,
• 03:19 my_series, set that equal to this whole thing.
• 03:23 And now we can actually call things off of this so we can go my_series.
• 03:28 And then for instance if we just want to call the whatever was Monday, that was 41.
• 03:36 And we can designate this again by just calling my_series.
• 03:42 See, Monday 41, if we wanted Wednesday, we could just call it Wednesday.
• 03:46 That was 16, yep sure enough 16.
• 03:49 So very cool.
• 03:50 That's one way to create a series.
• 03:52 You can also create them using a dictionary.
• 03:54 So let's create a dictionary, just a basic Python dictionary.
• 03:58 And let's use our same data, we can go Monday, and then that was 41.
• 04:06 Tuesday and that was 32, and
• 04:10 then Wednesday and that was 16.
• 04:15 This just the basic Python dictionary but
• 04:19 now we can call pd.Series, and then just pass in that dictionary.
• 04:26 And we get the same thing.
• 04:27 And it will take the key value pairs and split them apart into our series.
• 04:33 And the keys, Monday, Tuesday and
• 04:34 Wednesday become the index labels in our series.
• 04:39 So those are series, like I said,
• 04:40 we're not going to use a lot of series stuff throughout this course.
• 04:43 We're going to be doing data frames, which we'll start to learn about in the next
• 04:47 video, but data frames are always made up of series.
• 04:49 A lot of times you'll break apart your data frame to do things.
• 04:52 And when you do that,
• 04:53 you'll break it apart into a series that you then pull specific data out.
• 04:59 So it's good to understand what they are and
• 05:01 at least have a basic knowledge of them.
• 05:03 And now we've got that.
• 05:04 So in the next video, we'll start to look at data frames.

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