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Selecting Columns and Multiple Columns

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About this lesson

We discuss how to select individual and multiple columns within a data frame.

Exercise files

Download this lesson’s related exercise files.

Selecting Columns and Multiple Columns.docx
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Selecting Columns and Multiple Columns - Solution.docx
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Quick reference

Selecting Columns and Multiple Columns

Selecting data from a column or specific columns is easy!

When to use

Use this whenever you need to select a column of data, or multiple columns.

Instructions

Given a dataframe named my_df with columns:
   my_cols = ["Mon", "Tues", "Wed"]
 

To grab a specific column, such as 'Tues':
   my_df["Tues"]
 

To Grab more than one column, pass a list of lists:
   my_df[["Mon", "Wed"]]

Hints & tips

  • Grab One Column: my_df["Tues"]
  • Grab More Than One Column: my_df[["Mon", "Wed"]]
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  • 00:05 Okay, so we've got our data frame, it's looking really cool,
  • 00:08 how do we grab some of these columns if we want to do something with them?
  • 00:12 Well, it's actually pretty simple and that's what we're going to do now.
  • 00:14 So let's just call my_df and then we can pass in whatever column we want to grab.
  • 00:20 So if we want to grab Monday we just do it like this Shift+Enter to run and
  • 00:25 you see here's Monday.
  • 00:26 So, A is point 3381, there we go.
  • 00:31 D is -0.292, 0.92 and we can just spot check.
  • 00:38 Yeah, that looks the same, yeah, that looks the same.
  • 00:40 So yeah, we've just grabbed this column.
  • 00:42 Now you'll notice,
  • 00:43 this looks an awful lot like a series that we looked at a couple of videos ago.
  • 00:47 It's just got our data listed.
  • 00:48 It's got our index values or rows, A, B, C and D.
  • 00:53 And it says the names listed here is Monday because that's the column head and
  • 00:58 the data type is a float 64 bit.
  • 01:00 It doesn't mention anything about a series so it may look like a series but
  • 01:04 you may not be convinced.
  • 01:05 If you're not convinced we can actually find out we can run a type function, and
  • 01:09 then just pass in this whole thing and see what exactly this is.
  • 01:14 And it's a panda's series.
  • 01:16 So sure enough, it is a series.
  • 01:18 So if you're curious about what our data frame is, we can run a type for
  • 01:21 the data frame too, just grab our data frame right here has that in and
  • 01:25 that's a panda's data frame which should have been obvious, I guess.
  • 01:30 But very interesting, very cool.
  • 01:31 So here, we're just grabbing one column, right?
  • 01:36 And you may just want to do that, but you may want to grab more than one.
  • 01:39 So how do we do that?
  • 01:40 Well, we can just call my_df again.
  • 01:43 And earlier we passed in a list, right?
  • 01:46 Now we want to pass in a list of lists, so double brackets, and
  • 01:50 then just inside of here, just designate which ones you want.
  • 01:55 Say we want Monday and Wednesday.
  • 01:56 If we Shift+Enter to run this, boom, we get Monday and Wednesday.
  • 02:00 Notice Tuesday is gone.
  • 02:02 Now notice these are case sensitive.
  • 02:06 So if we wanted to call lowercase monday, we're going to get an error.
  • 02:11 So we get this key error,
  • 02:12 if we scroll down, usually when you get an error, and you're going to get errors all
  • 02:16 the time cause you're going to have typos and things.
  • 02:18 But you want to kind of look down towards the bottom and it usually tells you at
  • 02:23 very last thing is what exactly is going on, since Monday is not an index.
  • 02:27 Well, that's because we misspelled Monday.
  • 02:29 Because these guys are case sensitive, it has to be uppercase or lowercase.
  • 02:35 If it's in fact, uppercase or lowercase, very cool.
  • 02:38 Those are columns, that's how we grab them and
  • 02:42 we can do different things with them once we've grabbed them.
  • 02:44 We'll look at that going forward.
  • 02:46 But yeah, pretty much all there is to columns.
  • 02:48 In the next video, I'll show you how to create new columns
  • 02:51 once your dataframe is already been created.
  • 02:53 If you want to add, for instance, a Thursday column on there,
  • 02:56 we'll look at how to do that in the next video.

Lesson notes are only available for subscribers.

Pandas DataFrames
05m:03s
Creating New Columns
03m:55s
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