WEBVTT 00:00.810 --> 00:06.840 In this video we learn how to use the methods and unique and largest and smallest for complete data 00:06.840 --> 00:08.100 frame. 00:08.090 --> 00:14.760 So first of all let's import pandas and we work with the Titanic data set and we will have a look at 00:14.760 --> 00:16.100 the first five rows. 00:16.110 --> 00:20.810 And the last five rows and four pandas serious. 00:20.840 --> 00:28.890 That is the method unique and let's try out if that's also the method unique for a complete data frame 00:30.240 --> 00:31.770 and we get here an error message. 00:31.770 --> 00:39.370 So data frame object has no attributes unique so no panacea is the unique method actually returns say 00:39.460 --> 00:41.280 umpire Ray with all unique value. 00:41.290 --> 00:43.810 So let's try this for The Age column 00:49.320 --> 00:53.010 so we give we get to the unique values for the age column. 00:53.010 --> 01:00.070 However the end unique method also works for a complete data frame and let's try this out here. 01:00.570 --> 01:07.450 And obviously for each column here we get the amount of unique values in the column. 01:07.620 --> 01:11.060 For example as far survived we have 1 0 0 0. 01:11.060 --> 01:14.250 So we have two unique values for passenger class. 01:14.250 --> 01:15.830 We have three unique values. 01:15.840 --> 01:20.130 So Class 1 2 and 3 4 6 we have 2 unique values. 01:20.130 --> 01:22.110 Male and female and so on 01:24.900 --> 01:33.220 and let's have a tech here with the shift tab so we have further parameters axis and drop and a so by 01:33.220 --> 01:37.200 default we drop all missing values but we can change this here 01:41.860 --> 01:47.280 and there I can see in the columns that we have missing values the number of unique values increased 01:47.280 --> 01:51.570 by 1 so by the missing value by the end a value. 01:51.570 --> 01:56.940 And now you can see here that we have for each column the unique values but we can also decide here 01:57.300 --> 02:03.000 that we want to have for each row the unique values by putting your X's equals 1 02:06.550 --> 02:12.320 and in our case of a third the Titanic said I said that's not too meaningful here in row 1 we have 8 02:12.340 --> 02:24.160 unique values so let's check this so we have 1 2 3 4 5 we already had 0 6 7 and 8 including the missing 02:24.160 --> 02:27.320 value will we have 8 unique values. 02:27.320 --> 02:36.880 All right and again we can either include or exclude the missing values here. 02:37.020 --> 02:43.250 Okay so now let's try the method and largest which we also have for pandas serious and let's try to 02:43.250 --> 02:51.050 apply this also for a complete data frame and that's press shift tap here and largest that returns the 02:51.050 --> 02:54.790 first and Rose ordered by columns in the descending order. 02:55.550 --> 03:02.840 So we have to pass here and which stands for example for five rows or four ten rows. 03:03.080 --> 03:08.540 And we have to define the columns by which you want to order a data frame so for example we want to 03:08.540 --> 03:14.000 have the five passengers who paid the largest fare or the largest ticket price. 03:14.150 --> 03:21.010 So let's try this out here and here we have peasants on number 2 5 8. 03:21.100 --> 03:28.930 And uh she paid actually five hundred and twelve dollars a pound and then we have passengers six hundred 03:28.940 --> 03:29.850 seventy nine. 03:30.430 --> 03:36.430 And he also paid five hundred and twelve and the peasants are paid. 03:36.480 --> 03:40.170 The fifth largest ticket price is passenger number eighty eight. 03:40.170 --> 03:46.860 And he paid two hundred and sixty three dollars or pounds and he would actually get the same result 03:47.150 --> 03:53.790 with the software use so we could sort our data frame by the fare column in a descending manner and 03:53.790 --> 03:57.880 then get to the first five rows and this method. 03:57.930 --> 04:04.090 So that's the same here and that's also the end smallest method. 04:04.090 --> 04:09.950 It also works with the complete data frame and with the end smallest method we could filter actually 04:09.950 --> 04:12.520 the five fare youngest passengers on board. 04:12.560 --> 04:20.910 So let's try this so and here we have the five youngest passengers so we have passenger number 800 and 04:20.910 --> 04:24.620 the three And he's only open 40 years old. 04:24.620 --> 04:29.070 And and the fifth the youngest passengers passenger numbers 78. 04:29.250 --> 04:36.610 And he is 0 point eight three years old and we can also change end to one so we can only select the 04:36.610 --> 04:39.330 youngest passenger here. 04:39.910 --> 04:42.990 And as you can see here we get here still a data frame. 04:43.000 --> 04:47.430 So we have a data frame with one row so passenger number 8 0 3. 04:47.740 --> 04:56.880 And before we also learned that we can filter out a specific row with the method idea X-Men so here 04:56.970 --> 05:03.600 is the age column we get the index label for the youngest passenger and then we filled out the row of 05:03.780 --> 05:06.450 the youngest peasant service then the log operator. 05:06.450 --> 05:13.680 And here we do not get a data frame but we get to hear a panda series so on the left we have the columns 05:13.680 --> 05:21.380 and on the right the values and we have a set and smallest we still get a data frame here with only 05:21.380 --> 05:24.300 one row and the values here. 05:24.540 --> 05:24.950 All right. 05:24.950 --> 05:29.390 So now we are finished with this video and I hope to see you in the next one by.