WEBVTT 00:00.140 --> 00:06.450 OK so we discuss the decimal type and you've already seen type but we're focusing on the decimal type 00:06.960 --> 00:08.430 or we did focus on it. 00:08.460 --> 00:11.400 But like I said there's a bit of competition. 00:11.640 --> 00:16.950 So there are other types that we could use to represent similar data which is to say numbers that are 00:16.950 --> 00:19.260 not whole numbers. 00:19.310 --> 00:25.560 So there's also things like float and double. 00:25.870 --> 00:27.980 So we're going to talk about those in this video. 00:28.090 --> 00:33.560 The differences have to be upfront between all of them are quite technical. 00:33.610 --> 00:36.940 They have to do with how things are stored in memory. 00:37.180 --> 00:44.410 It would be difficult to explain without going all the way into how binary works and base 10 versus 00:44.410 --> 00:47.300 base 2 and how things are stored in memory. 00:47.350 --> 00:53.190 So I'm going to kind of skim over some of this because the honest answer is that it doesn't. 00:53.260 --> 00:56.230 Or the truth is that it doesn't really matter. 00:56.230 --> 00:58.830 All that matters is that you know functionally the differences. 00:58.840 --> 01:03.660 But if you do want to learn more about the difference between them there are some good resources online 01:03.670 --> 01:08.380 and highly recommend that you check out some YouTube videos and I would love to link you to a specific 01:08.380 --> 01:09.200 one. 01:09.280 --> 01:11.080 I've had some copyright claim issues. 01:11.110 --> 01:13.210 So last time I did that I'm not going to do it. 01:13.210 --> 01:15.730 Now if you're curious you can research it. 01:15.820 --> 01:19.000 But essentially like I said it boils down to how they're stored. 01:19.000 --> 01:26.880 So the big question is what is the difference between float and double is take them as a unit and decimal. 01:27.100 --> 01:33.290 If we go to the documentation there's a page that lays it out relatively plainly. 01:33.390 --> 01:38.840 It says the Decimal data type is a fixed point type and calculations are exact. 01:39.100 --> 01:45.090 The float and double data types are floating point types and calculations are approximate. 01:45.360 --> 01:49.420 So if get fixed point and floating point they are different. 01:49.590 --> 01:54.350 That has to do with what I was describing how they're stored and memory is different. 01:55.290 --> 01:58.640 That's not as important just knowing that they are different is important. 01:58.680 --> 02:05.610 However the side effect of how they're stored is that float and doubles are approximate which seems 02:05.610 --> 02:07.270 kind of weird. 02:07.500 --> 02:14.610 It doesn't seem like you would ever want to use them if they are not exact but essentially they're approximate 02:15.390 --> 02:25.180 and decimals are exact sort of boil it down float and double will store larger numbers using less space 02:25.190 --> 02:26.790 so that's an advantage. 02:27.030 --> 02:33.600 They can take gigantic numbers things that you can't store in a decimal and they'll use less space. 02:33.870 --> 02:35.530 However that comes at a big cost. 02:35.530 --> 02:40.010 It's a big but it comes to the cost of precision. 02:40.540 --> 02:43.890 So it doesn't mean that your numbers will be wildly inaccurate. 02:43.930 --> 02:49.710 If you're using a float or double but it does mean that they're not going to be as accurate as a decimal 02:51.450 --> 02:59.100 So here's a simple table that it just basically takes information from the documentation floats will 02:59.160 --> 03:00.750 have precision issues. 03:00.750 --> 03:03.430 After around 7 pages. 03:03.780 --> 03:05.490 So if we had something like 03:08.770 --> 03:14.980 this number here we could run into a problem where this one might not reliably be one when you retrieve 03:14.980 --> 03:17.110 it it might change. 03:17.380 --> 03:21.780 And that could be problematic if you need it to be precise. 03:22.030 --> 03:27.640 But if you're working with gigantic numbers something like this or it's even I didn't something like 03:27.640 --> 03:29.680 this. 03:29.680 --> 03:33.210 This one at the very end isn't going to be a deal breaker. 03:33.220 --> 03:36.700 Most of the time depending on what you're working with. 03:36.700 --> 03:41.800 With that said it's really important to know that there is a difference and there's a lot of kind of 03:42.130 --> 03:48.540 classic bugs and issues people have run into lots of blog posts around floating point math problems 03:49.180 --> 03:52.630 floating point math issues and bugs. 03:52.630 --> 03:55.410 Doubles are basically the same thing as a float. 03:55.540 --> 03:56.560 They're just bigger. 03:56.560 --> 04:01.400 They have larger capacity however they take up more space and memory. 04:02.080 --> 04:04.530 You can see four bytes versus eight bytes. 04:04.990 --> 04:07.450 A double however is more precise. 04:08.270 --> 04:12.720 So it's precise to roughly 15 digits rather than just 7. 04:13.280 --> 04:17.150 So if you do use one of these it's better to use doubles. 04:17.570 --> 04:24.080 Which brings us to the question what numeric data types should you use when you're storing information 04:24.080 --> 04:25.550 that has a decimal. 04:25.580 --> 04:31.430 And the answer at least in my opinion is you should always try and use decimal unless you know that 04:31.430 --> 04:32.660 precision doesn't matter. 04:32.870 --> 04:36.840 So if you're doing things like financial data prices. 04:36.920 --> 04:40.310 If you're a bank you need that precision Absolutely. 04:40.370 --> 04:45.580 You need to use decimal Otherwise you can get away by with floats or doubles. 04:45.710 --> 04:48.200 But for the most part I'll be using decimal. 04:48.250 --> 04:52.830 It's kind of my preferred data type when we're working with decimals. 04:52.840 --> 04:58.590 Oh and before I forget Actually I already forgot but this is after I recorded the video. 04:58.730 --> 05:00.330 I'm going and adding this in. 05:00.380 --> 05:07.280 I wanted to show you that you can actually see the inconsistency that in preciseness of using floats 05:07.730 --> 05:09.510 and doubles as well. 05:09.530 --> 05:21.570 So I'm going to create a simple table and I will just call it let's go with things right now. 05:21.810 --> 05:30.150 Critical thing isn't all that it's going to have and there is a price which will go with it. 05:30.570 --> 05:31.480 OK. 05:31.800 --> 05:38.670 So then we'll do our insert into Thing is and we'll do what we need to do. 05:38.670 --> 05:42.350 Price values. 05:42.690 --> 05:44.250 And let's start with a simple one. 05:44.360 --> 05:48.660 Let's do eighty eight point four five. 05:49.230 --> 05:50.570 And if we select that out of there 05:54.910 --> 06:02.470 good we get eighty eight point four or five but we'll run into problems if we start making more complex 06:02.470 --> 06:03.490 longer numbers. 06:03.670 --> 06:12.080 So let's do something like 88 seventy seven point forty five and that should still work. 06:12.610 --> 06:15.260 If we select it out you can see it's unchanged. 06:15.430 --> 06:22.050 Remember we're working with four five six characters here and we go back to that table that I had says 06:22.070 --> 06:28.870 around seven issues we run into precision problems seven characters run into position problems. 06:28.870 --> 06:39.880 So if we do something a little longer now 8 8 7 7 6 6 5 5 4 4 and we hit enter. 06:39.880 --> 06:41.950 Notice how we got stored. 06:41.950 --> 06:48.340 So it doesn't just truncate it doesn't chop things off but it only stores the first seven digits. 06:48.940 --> 06:55.480 And then it just puts these empty tent places not 10 spaces but multiplied by 10 zeros essentially at 06:55.480 --> 06:56.250 the end. 06:56.740 --> 07:05.450 So we end up with this which is very different than this and if we instead had this as a double instead 07:05.450 --> 07:10.610 of a float we would have that precision extended out to 15 characters. 07:10.610 --> 07:15.820 So it is significant and it makes a big impact depending on what you're doing and what you need. 07:16.280 --> 07:22.490 So that's why I recommend using decimal where you can control exactly what you need and how long you 07:22.490 --> 07:23.480 need things to be. 07:23.480 --> 07:28.220 However if you're working with gigantic numbers and you don't need to be that precise you can use double 07:29.060 --> 07:29.550 or float. 07:29.630 --> 07:35.960 But typically My rule of thumb is if you need to use one used double rather than float because you have 07:36.620 --> 07:44.320 you know 15 characters 15 places of precision rather than 7 you get double precision. 07:44.570 --> 07:44.790 OK.