WEBVTT 00:07.820 --> 00:11.780 In this video we go ahead and look at Wild Card. 00:12.440 --> 00:12.770 All right. 00:12.800 --> 00:16.430 So when you talk about wild card you use the keyword card. 00:16.430 --> 00:21.950 Like and uh how is this important in PostgreSQL. 00:22.280 --> 00:26.960 It's very, very important because you use that to actually call your table. 00:27.170 --> 00:27.560 All right. 00:27.560 --> 00:29.930 Let's go ahead and look at something here. 00:31.490 --> 00:37.790 Over here you can see we have so many email addresses now maybe in the company you actually filter the 00:37.790 --> 00:43.670 email and just get in students or in the schoolyard to get students that have only Gmail. 00:44.090 --> 00:55.490 What I have done and what we have that come because already you can see we have a.gov.edu and so on 00:55.490 --> 00:56.660 and that work. 00:56.750 --> 01:00.950 And how can we use a filter that remember you are going to filter this. 01:00.950 --> 01:05.190 So I think at this moment you might see the importance of the wild white card. 01:05.700 --> 01:08.280 Now, let me go ahead and give you an example of how this works. 01:08.280 --> 01:09.510 So let me go ahead and see. 01:09.540 --> 01:11.190 Select all from students. 01:11.220 --> 01:21.180 And uh I'm going to see where is it where class I'm going to say where email. 01:21.210 --> 01:24.300 Then I'm going to use a keyword called like. 01:24.330 --> 01:29.400 And with this keyword I'll go over here and use what is called a percentage. 01:29.400 --> 01:40.440 So using the percentage then I can go ahead and uh, see that if I want to get maybe, maybe I want 01:40.470 --> 01:46.710 to use filtered email with that org or that org and put a semicolon at the end. 01:46.740 --> 01:48.930 Let's go ahead and run this query and check it out. 01:49.200 --> 01:49.500 All right. 01:49.500 --> 01:54.210 It says that uh, the Where clause okay I have a semicolon right in here. 01:54.240 --> 02:00.300 I have to remove that set off from students where Guyana is the query and check it out. 02:00.330 --> 02:02.280 Now look at the email. 02:02.310 --> 02:09.450 The other email I have here are emails which.org can you see that Or emails. 02:09.450 --> 02:11.970 Here are emails with that arc. 02:12.000 --> 02:16.470 And what if I want to check people that have Gmail? 02:16.500 --> 02:21.000 All I need to do is this percentage was always come first. 02:21.030 --> 02:26.280 Again, I see gmail.com and let's go ahead and check it out. 02:26.280 --> 02:31.140 And no student has, uh, email with Gmail account. 02:31.140 --> 02:36.360 So what if it is named as a Google that's com. 02:36.390 --> 02:38.250 Let's run this and check it out. 02:38.250 --> 02:43.650 So we have these two students that has google.com and so on. 02:43.710 --> 02:49.500 And uh, we go ahead and check people that have only.org. 02:49.530 --> 02:51.990 I can go ahead and hit on this. 02:51.990 --> 02:55.320 And uh, we have only that arc. 02:56.130 --> 03:09.540 Now if I go ahead and uh, say maybe I actually want to see people that have, uh, email that is named 03:09.540 --> 03:15.340 twitter.com or I need to go ahead and do is quietly say twitter.com. 03:15.340 --> 03:21.160 And if I should do that, it should filter every of these and give you people the answers, or have 03:21.190 --> 03:23.620 email that ends with twitter.com. 03:23.650 --> 03:25.840 Go ahead and uh, execute this. 03:25.840 --> 03:28.240 And we have just four of them. 03:29.020 --> 03:34.480 You can see this twitter.com, twitter.com twitter.com and twitter.com. 03:34.810 --> 03:39.070 So this is a way you can actually use this white card, right. 03:39.100 --> 03:39.640 All right. 03:40.060 --> 03:40.870 Very good. 03:40.900 --> 03:53.410 Now what if I want to go ahead and, uh, actually see I want people maybe this is not just email. 03:53.440 --> 03:59.140 Let's go ahead and see under the first name first underscore name. 03:59.140 --> 04:09.790 And I want all the people that has P as the that ends their last name to not have P that end as their 04:09.820 --> 04:10.750 first name. 04:10.780 --> 04:12.580 The handle is actually scary. 04:12.730 --> 04:14.080 We have no one. 04:14.080 --> 04:18.470 But what if I take this P to the front before the percentage. 04:18.500 --> 04:19.610 Let's go ahead and check it out. 04:19.640 --> 04:25.730 So if I put this p to the front, it means that I want people whom their first names start with p. 04:25.760 --> 04:28.190 Let's go ahead and execute this query and check it out. 04:28.190 --> 04:30.170 And check out the first name. 04:30.170 --> 04:35.120 And you can see that all of the people that are first names started with P are here. 04:35.240 --> 04:44.330 So if I take this to the back and let's say A, it means I want all the first names that end with E. 04:44.360 --> 04:46.640 And let's go ahead and execute that and check it out. 04:46.670 --> 04:48.050 Check out the first name here. 04:48.380 --> 04:56.360 That to get to the hassle a hassle and check their last name at the end of their first name are E. 04:56.720 --> 04:58.310 The same thing is with last name. 04:58.310 --> 05:05.210 If I go ahead and say last underscore name and if I execute this query, check out the last name and 05:05.210 --> 05:08.600 you can see that the the name ends with a. 05:08.720 --> 05:16.400 And the same thing is applicable if I go and the studies the last name with a before the percentage. 05:16.400 --> 05:22.920 And if I execute this query and none of them has their first name and last name started with a e. 05:22.950 --> 05:24.720 And what people that started with C. 05:24.750 --> 05:26.130 Just go ahead and check it out. 05:26.580 --> 05:27.720 And, uh. 05:27.750 --> 05:28.710 Last name? 05:28.740 --> 05:29.010 No. 05:29.010 --> 05:30.060 Won't start with C. 05:30.210 --> 05:32.730 And what if uppercase C. 05:33.120 --> 05:36.780 Go ahead and check it out and have people tell you which uppercase C. 05:36.870 --> 05:37.890 Check it out. 05:38.160 --> 05:44.340 So that is case sensitive quiet and C uppercase A because I didn't mention that. 05:44.370 --> 05:49.320 You can see we have people that started with uppercase E for the last name. 05:49.320 --> 05:57.480 So this is applicable to both first name last name email gender error date and country. 05:57.990 --> 06:01.950 Now there is another one called uh the underscore. 06:01.980 --> 06:12.660 If I go ahead and remove these and I come over here and say, maybe I want people who have who earned 06:12.660 --> 06:16.830 some moxie and we have one, two, three, four, five. 06:16.920 --> 06:22.380 I'm gonna say the very first one is a character that represents the first character. 06:22.560 --> 06:28.230 The second underscore represents the second character underscore represent the top character. 06:28.320 --> 06:35.940 And if I end these people, that character starts, have three characters and end with T were printed 06:35.940 --> 06:37.080 out for last name. 06:37.080 --> 06:38.850 And let's go ahead and check it out. 06:38.940 --> 06:40.020 No one. 06:40.500 --> 06:45.630 And what if we go to the first name and uh, first underscore name. 06:45.630 --> 06:49.020 And let's go ahead and execute this and look at this. 06:49.050 --> 06:50.730 Gusti Motty. 06:50.970 --> 06:54.030 Bertie, Katie and Patty. 06:54.150 --> 06:55.380 I hope that is cool. 06:55.380 --> 07:01.800 So these people, this very first name presents or whoever that has a first name, no matter what it 07:01.800 --> 07:02.160 is. 07:02.160 --> 07:05.550 But the two last name ends with T at the. 07:06.870 --> 07:07.800 I hope that is cool. 07:07.800 --> 07:12.240 So the center is applicable to country, the IMO and so on. 07:13.230 --> 07:14.580 I hope that is very very interesting. 07:14.580 --> 07:16.740 So go ahead and pull it down, practice with it. 07:16.740 --> 07:20.070 And uh that is very very interesting. 07:20.070 --> 07:26.820 So just like we did in the percentage, if I go ahead and uh, check this and I put something like P 07:26.850 --> 07:34.810 or E, it means people whom the first letter starts with P and ends with any tricky words. 07:34.840 --> 07:37.030 Go ahead and execute this and let's check it out. 07:37.060 --> 07:43.270 You can see the whole path page, paper and blue. 07:44.410 --> 07:46.750 And that is how it is. 07:46.750 --> 07:49.390 So it is before or is it after. 07:49.390 --> 07:54.760 And that is applicable to percentage and underscore in this white card. 07:54.760 --> 08:02.020 And that is a very interesting aspect of our query system or query function and PostgreSQL. 08:02.020 --> 08:08.680 So go ahead and turn on this uploaded to last name to email to gender in word date and country. 08:08.680 --> 08:10.570 And you really love what it gets. 08:10.570 --> 08:14.320 And it can be used to make some filters that are very very interesting. 08:14.320 --> 08:15.790 So thank you so much. 08:15.820 --> 08:20.110 If you have any question, shop a question under the question section. 08:20.110 --> 08:22.570 And I'm going to get back to you as soon as possible. 08:22.600 --> 08:25.840 Thank you and see you in the next video lecture.