WEBVTT 00:01.060 --> 00:06.440 In the last video we have localized our stock price time series to New York time. 00:06.760 --> 00:14.230 So here we can see that New York Time is a four hours behind UTC time and we can also check this here 00:14.860 --> 00:18.320 with the index attribute tea set. 00:18.400 --> 00:20.670 So this is New York time. 00:21.140 --> 00:26.830 And let's assume that you want to convert these New York Times to L.A. time because we are located in 00:26.830 --> 00:31.030 L.A. and frequently trade in the cheese stock or whatever. 00:31.030 --> 00:37.690 And actually we can convert two other time zones with the method the tea set convert and it's actually 00:37.690 --> 00:41.340 required that we have localized our time series before. 00:41.590 --> 00:45.290 So if you haven't localized that we cannot convert it. 00:45.310 --> 00:50.890 So first of all we can convert our New York Time to UTC bypassing here UTC 00:53.790 --> 01:02.880 and here we have the corresponding UTC time so 10:00 a.m. New York is uh 2:00 p.m. UTC time and also 01:02.880 --> 01:10.110 here we have the difference to UTC time and of course that's uh 0 and let's convert our New York Time 01:10.110 --> 01:18.200 to Los ángeles time bypassing America Los Angeles so apparently Los Angeles Times. 01:18.220 --> 01:24.300 Three hours behind the New York time and seven hours behind UTC time. 01:24.490 --> 01:30.040 So 10:00 a.m. New York is actually corresponding to a 7:00 a.m. Los Angeles time. 01:30.040 --> 01:36.580 And also here if you want to save our new time zone and actually change our data frame we have to assign 01:36.580 --> 01:39.350 it to a variable and the. 01:39.370 --> 01:49.700 For example we can assign here our cheap price data frame with Los Angeles Time to the variable to L.A.. 01:49.770 --> 01:54.150 So now we have two data frames for our two year data set. 01:54.150 --> 01:57.540 So we have it in L.A. time and also New York time 02:00.260 --> 02:07.440 and now let's assume that we want to concatenate both data frames horizontally and we can do this with 02:07.530 --> 02:09.700 the direct pen us method Comcare. 02:09.750 --> 02:14.520 And we pass the G and the G L.A. data frame. 02:14.730 --> 02:21.360 And the interesting question is now how will the rows and the timestamps be aligned in the concatenated 02:21.360 --> 02:22.590 data frame. 02:22.740 --> 02:24.180 And let's simply have a look here. 02:24.180 --> 02:25.710 So we're safe. 02:25.740 --> 02:33.840 The concatenated data frame and the variable comp and let's have a look at comp so that's actually the 02:33.920 --> 02:37.700 DG dataset coming from the New York data frame. 02:37.700 --> 02:40.740 And here we have the Los Angeles data frame. 02:40.970 --> 02:44.060 And actually it looks like that the arrows match here. 02:44.300 --> 02:51.380 So we have the same prices and volume data and on the left hand side we have actually a daytime index 02:51.560 --> 02:55.190 which is localized to a UTC time. 02:55.310 --> 02:58.960 So panel is actually recognized that the T in New York. 02:59.030 --> 03:06.190 Data frame was localized in New York time and the Los Angeles data frame and Los Angeles time. 03:06.680 --> 03:12.480 And it actually aligns both that frames by simply using here the UTC time. 03:12.830 --> 03:20.610 And you can also check out our new index and we can actually see that it is the UTC time. 03:20.940 --> 03:25.170 And then we can actually add two new columns to our combined data frame. 03:25.170 --> 03:33.950 So one column with New York time and one column with Los Angeles time and we actually convert our daytime 03:33.990 --> 03:40.730 index with the UTC time to the corresponding time zones. 03:40.980 --> 03:42.520 So let's have a look here. 03:42.870 --> 03:48.400 And here we have on the right hand side two new columns New York time and Los Angeles time. 03:48.510 --> 03:56.700 And finally you might ask yourself where to get here the time zones that you have to pass here and therefore 03:56.730 --> 04:04.820 we have to impart to the pilot he said module and we can actually get all available time zones with 04:04.840 --> 04:09.620 the the attribute all time zones and we can check the length. 04:10.250 --> 04:17.850 So in total we are 592 time zones and we can also get to the most common time zones with the attribute 04:17.880 --> 04:27.390 common time zones so here we have quite a few times zones so let's scroll down here and here we can 04:27.390 --> 04:28.990 see a U.S. time zone. 04:29.010 --> 04:37.860 So U.S. Eastern U.S. Central and so on and finally we can also see a UTC that where the videos on time 04:37.850 --> 04:44.410 zones that probably are not one of the most attractive topics but the pretty important nevertheless. 04:44.410 --> 04:48.190 So thanks for watching and see us on the next video by.