WEBVTT 00:01.110 --> 00:06.460 In this video we learn how to change a single column labels and a single index labels in a pen. 00:06.470 --> 00:08.160 That's data frame. 00:08.160 --> 00:14.160 And so far we only learned how to change and replace a complete row or column index. 00:14.160 --> 00:20.640 And there are many cases and rewrote but we only want to change one or two entries in an index object 00:21.210 --> 00:28.470 so let's import pandas here and we import our Summer Olympic Games medal data set and we said to the 00:28.560 --> 00:30.480 athlete column to the index 00:33.680 --> 00:36.150 and then we can select our index object here. 00:37.040 --> 00:41.840 So here we have our labels of our row index and we can select a single entry. 00:42.380 --> 00:52.950 So for example the first element in exposition 0 here it's off the tiles and let's assume now we are 00:52.950 --> 00:56.700 not happy with the spelling of for the tiles. 00:56.700 --> 01:05.910 So we think that there must be a Y instead of a J and we can try to set our new value here and we get 01:05.910 --> 01:13.500 here an error message because an index object M is immutable the however there is a way to do this or 01:13.530 --> 01:21.720 to change a single label or entry with the rename method so let's have a look at the renamed method 01:21.750 --> 01:30.490 and press shift tab and in the rename method we have to pass a map bar and in the map we have to define 01:30.490 --> 01:34.930 the old label that we want to rename and we have to define the new label. 01:35.230 --> 01:41.800 And other methods would be actually a dictionary where on the left side is the key we have to define 01:41.970 --> 01:44.610 the oth label that we want to replace. 01:44.800 --> 01:52.870 And on the right side as well you of our dictionary we have to define the new value of the new label 01:53.020 --> 01:59.610 that replaces and the old label and that's another parameter called Xs. 01:59.610 --> 02:03.730 Where it we have to define which X is them on a map. 02:03.750 --> 02:04.560 Also target 02:07.960 --> 02:14.320 so in other words we have to define in which X is spend US should change here for the tiles. 02:14.350 --> 02:21.670 And here in our example we have to define um the row axis and either we can pass a zero. 02:22.500 --> 02:24.540 Let's check here. 02:24.940 --> 02:32.500 Now we have our new spelling with the Y all we can say in quotation marks uh index. 02:32.590 --> 02:36.770 So both alternatives are possible here and also here. 02:36.790 --> 02:46.060 We get off the tiles and y and last but not least the less and the in-place perimeter which we have 02:46.060 --> 02:50.950 to set to true if we want to change our data frame and set up the new value here. 02:53.050 --> 02:56.060 So now let's check again our data frame. 02:56.530 --> 02:59.800 And here we can see that we have changed our label three tiles. 02:59.860 --> 03:02.530 So here we have now a Y. 03:02.830 --> 03:05.790 And we can also do the same for a column label. 03:05.800 --> 03:12.970 So for example we want to change the gender label to sex and we want to change the city label to host 03:12.970 --> 03:20.520 city so we can change more than only one label so we can change many labels here and we can create a 03:20.910 --> 03:24.120 map dictionary with many a key value Paris. 03:24.120 --> 03:32.440 So here in this example to key value pairs so we have here our dictionary and on the left side is the 03:32.440 --> 03:40.200 old label that we want to change gender and as a value the new labor and then be separated with a comma. 03:40.230 --> 03:44.360 And then on the left side that's another key of our dictionary city. 03:44.370 --> 03:47.020 So this is the label we want to change. 03:47.030 --> 03:54.260 And on the right side as the value of our dictionary f our new label host city and again we have to 03:54.260 --> 03:56.110 define the axis. 03:56.450 --> 04:00.020 And in this case it is the column axis. 04:00.050 --> 04:00.740 So one 04:03.630 --> 04:08.000 so we can see where your host city and sex are not gender. 04:08.010 --> 04:12.870 And instead of passing one to the parameter axis we can also pass columns 04:17.100 --> 04:19.060 so we if you have a city and sex. 04:19.080 --> 04:23.930 And again we have to separate the in-place parameter to true. 04:23.970 --> 04:32.340 If you want to change our data frame and let's say again all that a frame and now we can see here. 04:32.370 --> 04:39.420 So we changed our label for the tiles our index label and we changed our column labels host city and 04:39.420 --> 04:42.610 sex. 04:42.890 --> 04:43.280 All right. 04:43.280 --> 04:47.360 So now this is it for the time being and hope to see you in the next session by.