Skip to playerSkip to main contentSkip to footer
eTrainerbox
  • 6/7/2025
Transcript
00:00Welcome to the homework walkthrough for the examining and filtering section of this course.
00:04Let's start off by connecting to our data set.
00:07We'll choose connect to data, and we're going to go over to the text file option.
00:11From here we'll navigate to our Indian food CSV file.
00:15We'll select the file and choose open.
00:17Next we're going to go over to our automatic prompt for the clean step and select it.
00:22If this option doesn't come up, then you can always hit the plus sign to the right hand side of the Indian food input step
00:29and choose clean step.
00:30Now that we're in our clean step, we're going to find the exact number of rows and fields in the data set.
00:35We can find that in the top left hand corner of our profile pane.
00:39We can see that there are nine fields and 255 rows in the data set.
00:44The next question we want to answer is how many states the dishes originate from in this data set.
00:50Let's go ahead and find the states field.
00:53If we move over to the right, we can see it.
00:54And then we want to know the distinct count of the number of states in the data set.
01:00By looking to the right hand side of the field name, we can see that there are 25 unique values in this column.
01:06That means there's 25 unique states contained within the data set.
01:10Now let's take a quick look at our data types found in our data set.
01:14We can see that we have a combination of string fields and numeric fields.
01:18But where could we see this in a better way?
01:20Let's go ahead and change this to our list view so that we can see a condensed profile of our fields.
01:26Now we can see that we have 1, 2, 3, 4, 5, 6, 7 fields that are string type.
01:33This is also an easy place to change your data types and to modify your fields as needed.
01:38Let's go back and see the profile pane how it normally is with our distribution.
01:43We can see that our prep time and cook time fields are set up using the summary view.
01:47What we want to answer is what is our most common prep time and what is our most common cook time for our meals contained inside the data set.
01:55To do that, we can switch over to the detailed view for these fields to figure out the most common times.
02:01I'm going to click on more options.
02:03Then I'll go down to my detail.
02:05And I can quickly see that my highest bar is 10.
02:08So 10 minutes is the most common prep time in this data set.
02:12Let's go over to cook time and do the same.
02:14We'll switch to our detail view and we can see that 30 minutes is the most common cook time for any of our meals in the data set.
02:22The next thing we want to do is find a dish that has lime in it.
02:25As Sai's favorite is lime, so we know that he's going to make a dish with lime in it.
02:29So we're going to go over to our ingredients field.
02:32We'll hit our search icon.
02:34We're going to type in lime and we're going to hit enter.
02:37And then it will be filtered down to just those ingredients that have lime.
02:42We'll click on that.
02:44And our data will profile down to just those that have lime.
02:47And they will be highlighted in blue.
02:49We can also see that our data grid has named this dish as the only one that has lime in it.
02:55So it seems like this one's going to have to make the list into the food truck.
02:58Let's go ahead and clear our search.
03:00The next thing we want to do is move our prep and cook time fields to the rightmost in the data set.
03:06So let's go ahead and we'll drag our slider over.
03:10And I can click on my prep time and use control or command if you're using a Mac.
03:15And I can click and drag my fields over.
03:19And I want them to be the rightmost, so I'll let that black line appear and I'll drop them there.
03:24Next up, we want to find out which state has the most sweet flavor profile.
03:28So to do that, we can use our highlighting functionality.
03:32We'll go over to sweet.
03:33We'll select it under our flavor profile.
03:36And searching down our list, I think West Bengal is the winner of the most sweet flavor profile.
03:42All right, now that we've profiled the data a bit, we're going to filter down the data set to just size preferences.
03:49He's given us a list, so let's go through it and filter our data down.
03:53Now, since this is a dessert food truck, we're only going to be looking at desserts.
03:56So we're going to go over and select dessert under our course.
04:00Right-click and choose keep only.
04:03That will filter our entire data set just down to desserts.
04:06All right, and then we're going to choose just a particular number of states that are size preferences.
04:11So we're going to go down, go to more options, choose filter, and we're going to pick selected values.
04:17That way we can get a list to select from.
04:19And we're going to choose Bihar, Disha, and West Bengal states only.
04:25All right, that's looking good.
04:26We want to make sure that's on keep only because we're only going to keep those data sets.
04:30And we're going to choose done.
04:32Next, we're going to look at cook time because this is a food truck and we need our dishes to be made pretty quickly.
04:37So we're going to place a filter on the cook time so that it's less than 25 minutes.
04:43Let's go ahead and add a calculation filter.
04:46And we're going to say cook time less than or equal to 25 minutes.
04:52Let's click save on that.
04:53And our data will be filtered down to just those dishes that take less than 25 minutes to cook.
04:58All right, so what are the remaining dishes that will be the initial food truck menu?
05:03Looks like we have Balu Shahi, Perni, Sendesh, and Chiridoy.
05:10Looks like we'll need quite a bit of prep time for these meals, but the cook time will be relatively quick.
05:14So I think our customers will be pretty happy with this list.
05:18Let's go ahead and save our flow so that we can hand this information out back to Sai.