WEBVTT 00:02.550 --> 00:06.210 In this video we learn how to use them the methods any and all. 00:06.240 --> 00:08.310 But first of all let's impart pandas. 00:08.340 --> 00:15.970 And we use our Titanic's data frame and then we create a boolean series. 00:15.960 --> 00:21.640 I would check for each row of whether the peasant says a male here. 00:21.640 --> 00:24.190 So we create a boolean serious nothing new. 00:25.000 --> 00:25.540 So now what. 00:25.540 --> 00:28.290 If you want to check if at least one passenger is a male. 00:28.290 --> 00:33.120 So we have at least the one true here in our panda series. 00:33.250 --> 00:36.990 And for this task we can use the any method. 00:37.060 --> 00:45.730 So we have our panda serious and we have to use your brackets and then use the any method and let's 00:45.730 --> 00:55.310 check here the any method return whether any element is true or whether you requested X's so we get 00:55.310 --> 00:55.960 it through here. 00:55.970 --> 01:00.320 If any of the elements in our series here is true. 01:01.010 --> 01:02.910 So let's check this out. 01:03.140 --> 01:05.210 And of course we got a true here. 01:05.210 --> 01:08.410 So we actually know that on the Titanic there were many males. 01:08.450 --> 01:14.870 And with the all method we can check whether all passengers were made. 01:15.680 --> 01:24.250 So we do check here if in our pandas serious All values are true as AND UH LET'S CHECK you're also with 01:24.260 --> 01:33.300 Chef tab return whether all elements are true so we actually know that there were many females and males 01:33.330 --> 01:34.400 and uh. 01:34.470 --> 01:36.290 So not all passengers are males. 01:36.300 --> 01:45.360 So we should get a false and that's true so our male example was quite obvious and we could have answered 01:45.450 --> 01:52.830 the questions here by just having look here at the panda series however let's try a less obvious case 01:52.890 --> 02:01.860 and let's check whether at least one passenger is exactly 80 years old so we create here a panel serious 02:01.860 --> 02:06.960 and checked whether the peasants sounds like exactly 80 years out and then we check with the any method 02:07.950 --> 02:09.730 if we get at least the one true. 02:11.710 --> 02:19.250 And this is the case so from early videos we know that um the oldest passenger on board was exactly 02:19.250 --> 02:27.970 80 years old and we can use in the any end all method not only for boolean serious but also for serious 02:27.980 --> 02:30.570 with numerical values. 02:31.040 --> 02:37.280 So we might know that the bringing value to a stands not only for the integer 1 but for all numbers 02:37.280 --> 02:41.720 that are not equal to zero and the Boolean value for instance far. 02:41.720 --> 02:42.570 Exactly. 02:42.680 --> 02:45.790 And only zero. 02:45.800 --> 02:54.200 So now let's create here a panda series with the elements minus 1 0 point 5 for 1 minus open 1 and 0 02:55.630 --> 03:02.740 and with the all method we check if all elements in our pandas serious act rate are less than zero. 03:02.740 --> 03:10.080 So if they are not equal to zero and we can see here that we have a zero so we should get you a false 03:12.160 --> 03:20.440 and with the any method we can check if we have at least the one element in our panda serious that is 03:20.500 --> 03:21.910 and equal to zero. 03:21.910 --> 03:30.550 And here we should get a true and now last but not least let's check our Fifth Column if all passengers 03:30.550 --> 03:34.240 paid a price that is greater than then zero. 03:34.270 --> 03:40.190 So if you only have one passenger that paid nothing actually for the ticket then we should get here 03:40.210 --> 03:49.110 a false and this is the case or from earlier videos we know that there were some passengers that actually 03:49.110 --> 03:57.370 it didn't pay anything for their ticket so we have some zero values in our fair column and therefore 03:57.600 --> 04:02.230 by using the all method we get here a false. 04:02.230 --> 04:07.360 All right so now we are finished with this video and I hope to see you in the next one by.