WEBVTT 00:01.170 --> 00:02.070 Hi everyone. 00:02.070 --> 00:08.070 So in this video we learn how to select rose and that the special index operator a log which enables 00:08.070 --> 00:10.880 us to slow court position based indexing. 00:11.190 --> 00:17.130 But before we introduce a brand new data frame that contains all metals that have been awarded an Olympic 00:17.130 --> 00:22.010 summer games from eighteen hundred ninety six to 2012. 00:22.080 --> 00:25.820 So in our photo that's already start a CSP fight. 00:25.830 --> 00:27.930 Call it some adopt see a suite. 00:28.560 --> 00:35.630 And we can impart to So first of all we have to import pandas and then we can import our summer dot 00:35.730 --> 00:37.370 see a sweet file with them. 00:37.380 --> 00:46.740 The pandas method read C a we and we assign the variable summer and we can f look at our data frame. 00:46.740 --> 00:53.330 So here are the first five rows and actually each row is actually a medal that the has been awarded 00:53.440 --> 00:56.100 during a summer Olympic games. 00:56.220 --> 01:01.530 So it's starting here from eighteen hundred ninety six and the host city was Athens. 01:01.540 --> 01:09.950 Um the sport was aquatics discipline swimming and the athlete that won the gold medal was afraid hails 01:10.410 --> 01:17.190 from Hungary and it's a man and the event was hundred meter freestyle swimming. 01:17.400 --> 01:18.110 So let's check. 01:18.120 --> 01:21.830 Also the last five rows with them the Taylor method 01:26.200 --> 01:35.470 so we end here in the year 2012 where the host city was London and the very last medal that was awarded 01:35.860 --> 01:38.260 was in wrestling freestyle. 01:39.220 --> 01:43.030 And the athlete was Jimmy Lindbergh from Sweden. 01:43.030 --> 01:51.120 It's a man obviously and the event was wrestling freestyle until the 96 kg and he won the bronze medal 01:52.410 --> 01:58.730 and let's also get some meta information so let's call the method info 02:01.850 --> 02:08.550 and here we can see we have a range indexed so here the indexed Labor starts from zero to thirty one 02:08.550 --> 02:10.670 thousand one hundred sixty four. 02:10.710 --> 02:14.550 So we have thirty one thousand one hundred sixty five entries are rows. 02:14.580 --> 02:20.760 So in total thirty one thousand one hundred sixty five medals were awarded. 02:20.760 --> 02:23.480 And our data frame contains some nine columns. 02:23.760 --> 02:29.750 So yes it is part disciplined athlete country gender event and medal Yeah. 02:29.790 --> 02:34.490 And actually we have almost no missing values only in the country column. 02:34.500 --> 02:42.250 We have actually four missing values and the data types are in the year column in the US and in all 02:42.270 --> 02:45.540 the other columns mustering so he is object. 02:45.570 --> 02:46.480 But an easy read. 02:46.490 --> 02:54.410 So if we see object here we have actually a string data type in the columns and we have already a memory 02:54.410 --> 02:56.750 use itself off to point to one megabyte. 02:56.750 --> 03:03.500 So this is actually a larger data set than the Titanic data set. 03:03.500 --> 03:09.260 All right so now we want to select a single rose and let's assume we want to select only here the very 03:09.260 --> 03:20.480 first row of tiles and let's type summer and in the last video we saw that by just passing here the 03:20.510 --> 03:28.920 LEI the 0 does not work and therefore we have to introduce and our indexing operator I log so we write 03:28.940 --> 03:37.000 here dot I log and so then in step brackets we pass in the index position of the row that we want to 03:37.000 --> 03:37.480 select. 03:37.480 --> 03:41.910 So you want to select select the very first row here. 03:41.910 --> 03:46.310 And the concept of zero based indexing also works with a log. 03:46.330 --> 03:52.490 So if you want to select the very first row we have to select the row at indexed positions 0. 03:53.950 --> 04:01.640 So let's try it here and now we get the very first row and the values of the very first row are here 04:01.640 --> 04:05.800 on the right side and the column labels on the left side. 04:05.960 --> 04:14.630 So we have the 896 in the city Athens the spot was aquatics that discipline while swimming the athlete 04:14.660 --> 04:18.620 was afraid hails from Hungary. 04:18.680 --> 04:25.850 He's a man and the event was a hundred meter freestyle and he won the gold medal and we can also check 04:26.000 --> 04:28.220 the data type off our slides here 04:35.890 --> 04:39.500 and same as with columns if you slice one row with and the eye look. 04:39.510 --> 04:46.550 Operator we get a panda serious and we can also select other rows so let's copy it here. 04:46.900 --> 04:56.230 So let's select the second draw at index position 1 and we get here auto hash man who won the silver 04:56.230 --> 04:56.610 medal. 04:56.640 --> 04:58.330 Let's check it here. 04:58.380 --> 05:07.030 Here that the second draw at an exposition 1 or two a man so and same as with the lists a number higher 05:07.030 --> 05:11.650 raise them the zero based indexing works and also we can index 05:14.590 --> 05:18.520 the very last row by having the index position minus one 05:21.270 --> 05:28.670 so here we get the very last row it's here to me left back so also here we get the pen the serious with 05:28.810 --> 05:29.870 the very last throw. 05:29.870 --> 05:38.840 So to me that back won bronze and say yes to that lists an umpire race that can also make slices. 05:38.840 --> 05:46.320 So for example we want to have uh the first five rows so starting from an next position 0 2 indicate 05:46.340 --> 05:51.590 position five but five excluding Sonia. 05:51.590 --> 06:02.060 We get to the very first five rows and of course we can also get the very last five rows so starting 06:02.060 --> 06:05.200 from the next position minus five. 06:05.200 --> 06:06.800 To the very last 06:09.480 --> 06:14.730 and this would give us them the same five last rows and then the tail method so starting from Damion 06:14.760 --> 06:21.510 Jankowski to Jimmy Lynn back here we have the tail method Exactly 06:24.610 --> 06:24.780 yeah. 06:24.790 --> 06:31.910 And we can also make some way advance slices so let's say we want to have the rolls from index position 06:32.890 --> 06:39.240 456 till index position four hundred fifty nine. 06:39.370 --> 06:43.130 So here we have some medals from nineteen hundred and Paris. 06:43.270 --> 06:50.990 And this plus rowing and what we can also do we can pass a list of index positions. 06:51.130 --> 06:52.450 So for example 06:55.350 --> 07:02.050 we want to have the robot in next position to at an exposition forty five and that next position seven 07:02.050 --> 07:09.430 hundred sixty five so we pass here a list of index positions within our span brackets here 07:12.540 --> 07:18.770 and here we get the rows at an exposition to forty five and seven hundred sixty five all right so on 07:18.770 --> 07:23.720 this video we learned how to slice and select rows with the eye lock operator. 07:23.730 --> 07:31.310 And essentially it works them the exact same way as slicing it lists or an umpire race with them position 07:31.310 --> 07:32.510 based indexing. 07:32.510 --> 07:34.380 So this is actually nothing new. 07:34.380 --> 07:39.530 And in the next session we learn how to slice rows and columns of the the I log. 07:39.710 --> 07:40.820 So hope to see you there. 07:40.870 --> 07:41.090 By.