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Operations: Sorting, Ordering, and Finding Nulls

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About this lesson

In this video, we discuss how to sort and order your data.

Exercise files

Download this lesson’s related exercise files.

Operations: Sorting, Ordering, and Finding Nulls.docx
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Operations: Sorting, Ordering, and Finding Nulls - Solution.docx
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Quick reference

Operations: Sorting, Ordering, and Finding Nulls

Sorting can be done using the sort_values() function.

When to use

Use the sort_values function to sort and order data in a DataFrame.

Instructions

To sort data in a particular column:
   my_df.sort_values('Wed')

To sort data in a particular column in descending order:
   my_df.sort_values('Wed', ascending=False)

Hints & tips

  • Ascending: my_df.sort_values('Wed')
  • Descending: my_df.sort_values('Wed', ascending=False)
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  • 00:05 Okay, in this video I want to talk pretty quickly about sorting and
  • 00:08 ordering your data.
  • 00:09 So we've got, let's just use our current data frame, and we've got Monday, Tuesday,
  • 00:14 Wednesday along the top where we've got our just numbered indexed as the row
  • 00:18 headings here.
  • 00:19 And you'll notice on Wednesday, we still have our 111 and
  • 00:22 we've also got 111 down here.
  • 00:24 So let's say we wanted to order Wednesday in numerical order with all
  • 00:29 of the similar things bunched together and just in order from lowest to greatest,
  • 00:34 we could do that using the sort values function.
  • 00:37 So we could just go my_df.sort_values and
  • 00:43 if we shift tab, we see, let's make this bigger here.
  • 00:47 We can see you can sort by whatever you want to sort by and
  • 00:52 in our case, we're going to pick Wednesday.
  • 00:55 The x zero is it's going to sort by that by default.
  • 00:58 Ascending true in place equals false, kind equal quick sort, in position equals last,
  • 01:05 we don't really care about these last two things but we've seen this in
  • 01:08 place many times and we could go ascending true or false and we'll look at that.
  • 01:12 So let's go ahead and close this.
  • 01:13 So we want to sort by, let's say Wednesday, and if we run this, we see 111,
  • 01:18 111, 222 and 333.
  • 01:20 So it's sorted them in order from low to high in ascending order.
  • 01:26 We could go ascending equals False and
  • 01:32 now it does the opposite from high to low 333, 222, 1111.
  • 01:36 So that's a quick way to sort values.
  • 01:39 Now we did that with numbers, you could do the same thing with strings.
  • 01:42 So let's head back up to our data frame up here and
  • 01:47 let's change Monday from 1, 2, 3, 4,
  • 01:52 to let's go John, Sally, Bob and Tina.
  • 01:57 We shift run we get John, Sally, Bob and Tina.
  • 02:00 Now if we want to sort by Monday, we see it's been rearranged to Bob,
  • 02:06 John, Sally, Tina, and you'll notice it's still ascending but
  • 02:10 it's ascending in alphabetical order.
  • 02:12 So B is the first letter in the alphabet in our list at least than J, S and
  • 02:17 T and you can see the index numbers here changed to reflect that.
  • 02:22 So pretty interesting.
  • 02:23 Now, we have all have different values Bob, John, Sally and Tina.
  • 02:28 If we came up here and change Tina to John and shift around this and
  • 02:32 then I did this again, we would get Bob, John, John, Sally because it's going to
  • 02:38 group the two Johns together because they're the same value, and that's cool.
  • 02:42 And just like before we can change this ascending to false and
  • 02:48 it's just going to change the order from the highest letter in the alphabet S,
  • 02:54 down to J, J and then B for Bob.
  • 02:57 So, quick and easy way to sort values.
  • 03:00 Pretty cool and pretty useful and
  • 03:02 that is pretty much the end of the sort of hardcore pandas section.
  • 03:06 We learned a lot.
  • 03:07 We went over a lot of sort of dense stuff in this section and
  • 03:11 it may take a little bit of time for this all to sink in but
  • 03:14 the more you play around with this, the easier this becomes.
  • 03:17 You start creating these data frames like it was nothing and
  • 03:20 as you do things with them, it just really becomes second nature.
  • 03:24 In the next section, we're going to move into pandas visualization,
  • 03:27 we're going to start looking at charts and graphs and all kinds of graphics and
  • 03:30 cool stuff like that and that'll be coming up in the next video.

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