Eve: Building RESTful APIs with MongoDB and Flask Transcripts
Chapter: Setup and tools
Lecture: Working with different versions of Eve

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0:00 In this section, we're going to look at summary statistics for that student data that we just loaded. Let's get going.
0:06 Here's the summary statistics. This is taken from that University of California, Irvine website.
0:11 We've got multiple columns in here describing a student. And at the bottom here, we've got grades.
0:18 This data set was used to look into what features impact how a student performs on their grades.
0:24 And we see that there's a G1, G2, and G3, which are the grades. Now I'm not really going to get into modeling in this section here,
0:33 but we will look at some of the summary statistics. So the first thing I generally do when I've got a data set
0:40 is I'm going to look at the types of the data. And with Pandas, we can say .dtypes. This is going to return what's called a Pandas series.
0:49 And in the index of this series, we see the columns, and on the right-hand side, we see the types.
0:56 In this case, you'll notice that in brackets, we have PyArrow indicating that we are using PyArrow as the back-end,
1:04 and we have optimized storage there. We also see that there's int64s. So those are integer numbers that are backed by PyArrow.
1:13 They're using 8 bytes to represent the integer numbers. And we're not seeing any other types other than strings and integers here.
1:20 Another thing I like to do with Pandas is do this describe method. I was once teaching this describe method to some of my students
1:28 when I was doing some corporate training, and when I did it, someone went like this and hit themselves in the head,
1:33 and I asked them, what? What happened? Did I say something wrong? And they said, no, but we just spent the last three weeks
1:40 implementing this same describe functionality for our SQL database. So this is one of the nice things about Pandas.
1:48 It has a bunch of built-in functionality that makes it really easy. Describe is one line of code, and you get a lot of output from it.
1:55 So this is returning a Pandas data frame. Pandas is going to reuse a data frame and a series all over the place.
2:02 In this case, the index is no longer numeric. In the bold on the left-hand side, we can see count, mean, std, min.
2:09 That's the index. You can think of those as row labels. Along the top, we have the column names. These correspond to the original column names,
2:18 but these are the numeric columns. So for each numeric column, we have summary statistics. Count has a specific meaning in Pandas.
2:27 Generally, when you think of count, you think of this as how many rows we have. In Pandas, count doesn't really mean that.
2:34 It means how many rows don't have missing values. You just need to keep that in mind when you're looking at that count value.
2:43 Mean, that's your average. Standard deviation is an indication of how much your data varies.
2:48 We have the minimum value. At the bottom, we have the maximum value. In between there, we have the quartiles.
2:54 I like to go through this data and look at the minimum values and the maximum values to make sure that those make sense.
3:00 Maybe look at the median value, which would be the 50th percentile. Compare that to the mean to get a sense of how normal or how skewed our data is.
3:10 Also, look at those counts to see if we have missing values as well. In this case, it looks like most of our data is 5 or below.
3:19 We do have some going up to 22 or 75, but most of it is not very high. It doesn't look like we have any negative values.
3:28 Now, remember, we just looked at that Dtypes attribute, which said that we are using 8-byte integers to store this information.
3:36 Most of these values don't need 8 bytes to store them. In fact, all of them could be represented with 8 bits of memory.
3:45 We could use pandas to convert these integer columns to use 8 bits instead of 8 bytes for each number. That would use 1 8th the amount of memory.
3:56 We could shrink this data even further than we got by using PyArrow without any loss of fidelity in our data.
4:03 There are a bunch of other things that we can do. One of the methods is the quantile method. I'm going to run that. This actually failed.
4:12 Let's scroll down and look at the error here. It says, arrow not implemented. It says, function quantile has no kernel matching input type strings.
4:20 The issue here is we have non-numeric columns. To get around that, we can specify this parameter, numeric only is equal to true.
4:27 This is going to give us back a series. Why did this give us back a series? Because this is an aggregation method.
4:36 You can think of our original data as 2 dimensions. We are taking the quantile, the 99th percent quantile.
4:43 That is taking each of those columns and telling us what's the 99th percentile of that. It's collapsing it to a single value.
4:50 Because we have 2 dimensions, we're going to collapse each of those columns to a single row.
4:55 Pandas is going to flip that and represent that as a series where each column goes in the index and the 99th percentile goes into the value.
5:05 You'll see that Pandas uses data frames and series all over the place. You need to get used to these data structures.
5:11 The quantile method has various parameters that you can pass into it. In Jupyter, I can hold down shift and hit tab to pull up that documentation.
5:19 You can see that this Q parameter, the first parameter, accepts a float or an array-like or a sequence-like parameter.
5:27 In this case, instead of passing in 0.99, a scalar value like I did above, I'm going to pass in a list.
5:34 Let's say I want the first percentile, the 30th percentile, the 50th percentile, the 80th percentile, and the 99th.
5:41 When we do that, instead of getting back a series, we're now going to get back a Pandas data frame.
5:48 But if you look in the index here, the index is the quantiles that we asked for.
5:53 This illustrates that power of Pandas that you can do relatively complicated things with very little amount of code.
6:01 Also, you need to be aware that this is kind of confusing in that you can call the same method and it might return a one-dimensional object
6:09 or it might return a two-dimensional object depending on what you're passing into it. In this section, we looked at summary statistics of our data.
6:17 Once you've loaded your data into a data frame, you're going to want to summarize it to understand what's going on there.
6:24 That describe method is very useful. Then there are various other aggregation summaries that we can do as well. as well. I showed one of those which is

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