WEBVTT 00:01.030 --> 00:01.870 Hi everyone. 00:01.870 --> 00:07.270 So in the last few days we learned how to index then select a rose from a panda's data frame with the 00:07.270 --> 00:08.210 index operator. 00:08.230 --> 00:15.600 I log and in this video we will index not only rose but also columns for the eye look operator. 00:16.150 --> 00:23.830 But first of all we import pandas and we import our Summer Olympic Games medal data set and we have 00:23.830 --> 00:25.560 a look at the first five throws. 00:25.570 --> 00:27.940 So no surprise here. 00:27.940 --> 00:34.650 And what we already learned we can for example select the very first row at an exposition zero. 00:34.740 --> 00:41.460 It's uh the I like operator by passing here. 00:41.470 --> 00:43.620 The index positions zero. 00:45.010 --> 00:46.840 And we get here as a panda. 00:46.840 --> 00:51.340 Serious the first row off our pandas data frame. 00:51.430 --> 00:56.430 Now let's assume we only want to have the name of the athlete off to the first draw. 00:56.490 --> 00:59.480 Then we can in addition slice also for the athlete. 00:59.790 --> 01:01.190 So we make a comment here. 01:01.350 --> 01:06.590 So the first position within the brackets here is for the index position of our rows. 01:06.600 --> 01:11.580 And the second position here is for the index positions of our columns. 01:11.580 --> 01:12.530 We want to select. 01:12.990 --> 01:16.170 So here we want to select the athlete column. 01:16.320 --> 01:18.840 And we have to apply position based indexing. 01:18.900 --> 01:22.110 Same mess with lists an umpire race and matrices. 01:22.110 --> 01:32.820 So our year column has them the index positions 0 1 2 3 and our athlete column has them the index position 01:32.820 --> 01:33.240 far 01:37.500 --> 01:43.840 and TV get them the athlete of the very first the medal and it's I say tiles. 01:43.870 --> 01:45.790 So now what if you want to select. 01:45.820 --> 01:50.910 For the first row the columns yes city and sport. 01:51.250 --> 01:54.790 So let's copy and paste it. 01:54.880 --> 02:00.700 So we want to slice for the column at an exposition 0 1 and 2. 02:01.210 --> 02:10.150 So the therapist next thing we have to slice from index position zero and to index position of three 02:10.150 --> 02:13.860 excluding song. 02:13.860 --> 02:15.140 Here we get the the year. 02:15.150 --> 02:21.100 The city in the sports column for our very first row is on its nineteen hundred ninety six and Essence 02:21.120 --> 02:24.590 and its Aquatics and this the I operator. 02:24.690 --> 02:29.760 It works out exactly in the same way as selecting and slicing and umpire race. 02:30.210 --> 02:35.610 So the first position is for the row and next position and the second position is for the column and 02:35.610 --> 02:38.750 next position and zero indexing applies as well. 02:38.760 --> 02:46.320 And if we slice the starting points including and the ending points excluding and we can also pass a 02:46.320 --> 02:46.850 list. 02:46.920 --> 02:54.520 So let's copy and paste here and let's say we want again the first row but we want to have column at 02:54.520 --> 02:58.620 an exposition to 4 6 and 8. 02:58.660 --> 03:06.810 So let's see what we get here so the climate in exposition to a sport its aquatics column at an exposition 03:06.810 --> 03:14.220 for its athlete I feel it highlights the climate in exposition 6 is gender. 03:14.250 --> 03:22.410 So is a man and the column at an exposition 8 some kind of medal it's the gold medal and we can also 03:22.470 --> 03:27.010 make some more complex queries so there is no limit on this. 03:27.060 --> 03:35.010 So for example we want to have the column C a two four six and eight but we want to have the rows from 03:35.010 --> 03:44.030 in the exposition 34 including until next position let's say thirty nine excluding and here we can see 03:44.090 --> 03:51.480 we get the column sport athlete gender and medal farther rows from an exposition 34 until thirty eight 03:51.540 --> 03:55.540 including and with the I Look operator. 03:55.660 --> 04:01.200 We can also select the columns or complete columns so let's check it here. 04:01.250 --> 04:09.440 Some method I log and we can say we want for all rows we want to have column four here. 04:09.700 --> 04:18.280 So we get to a column form which is athlete and now we can check if this is the same as selecting a 04:18.280 --> 04:20.530 column with the attribute athlete. 04:21.130 --> 04:31.810 So we ask if some other I log all rows and the fourth column is equal to slam method athlete and that's 04:31.810 --> 04:39.710 true and we already learned that we can alternatively also slice for the athlete column by having athlete 04:39.890 --> 04:41.560 in brackets after summer. 04:41.570 --> 04:42.830 So that's also possible. 04:42.890 --> 04:44.570 So we are fierce free alternatives. 04:45.320 --> 04:49.720 However if I only want to slice for a column I would never use the i log. 04:50.030 --> 04:53.300 So some other athletes are smarter and better actually. 04:53.300 --> 04:59.870 All right so let's again slice here with the spare brackets to have it complete. 05:00.800 --> 05:03.140 So that's the third alternative. 05:03.140 --> 05:09.470 And with this we are finished now with the session and in the next session we will burn the index operator 05:09.470 --> 05:09.920 lock. 05:09.950 --> 05:11.570 So I hope to see you there by.